At the end of last week, many news bulletins in the UK carried a story that British Postal Delivery workers ("Postmen" or "Postwomen") were unhappy with a new computer system which had changed their delivery routes. According to the stories, some postpersons were having to walk at 4mph for three and a half hours. (Example story.)
The stories were short on detail about the "computer system" except that it had details of 27 million addresses and came from Canada with the name of Pegasus Europe. It seems to have been around for several years, but evidently there have been recent changes in the system which have led to the unhappiness.
Reading the story, it was obvious that there was a mathematical model behind Pegasus of a familiar kind -- optimal (or efficient) route planning for vehicles (postpersons) with constraints (loads, time). Someone, somewhere had been using operational research to help the postal service. And as an operational researcher, I ought to feel proud.
But I don't. The news reports showed that something was lacking in the O.R. process. I can recognise several possibilities for what was wrong, but without further information I can't give a full diagnosis. Maybe someone from the post office can help. So, in no particular order, here are my observations and questions:
(1) Was this system tested and developed for the UK postal service, or was it an off-the-shelf system into which British data was inserted? If it was the latter, then did anyone verify the assumptions that had been made by the designers?
(2) Given the size of the database, it seems likely that the system is largely, if not wholly, deterministic. If so, what sensitivity analysis was carried out? And if so, what changes were made to the data and the model as a result? If not, why not?
(3) How much communication was there before, during and after the development of the system? Who with? Did the creators/users of the system discuss what they were doing with staff at all levels of the system?
(4) Was the objective simply cost-based? Or were there other criteria?
(5) Did anyone concerned with the data collection, input, modelling and recommendations actually go out and test the results? Or, to put it bluntly, would the modeller trust the model's output if they were asked to do a postperson's job?
(6) Did the modellers collect feedback from the postal service about the implementation?
For years, I have taught that an essential part of O.R. is a feedback loop, that an O.R. project is not properly implemented until it has been accepted (and possibly welcomed) into the practice of the organisation. It seems, from the press, that this project lacked this.
We are often reminded of places in companies where there is "OR inside". I feel that this is a story of "OR Inside" which omitted the essential, friendly face of OR outside.
Where was Genchi Genbutsu?
Monday 15 December 2008
eBay and the AHP
I am an eBayer (or whatever the appropriate term is) to buy items for my stamp and postal history collection. From time to time, I have also sold on eBay. As such, one becomes aware of the size and complexity of the organisation that hs come to symbolise online trading in many countries. From time to time, once one has registered with eBay, one becomes aware of the data analysis carried out by the company's statisticians. I was emailed to ask if there was anything wrong, as I had not bought as much in the previous few months as I had in a similar period earlier -- in other words, my behaviour was outside their forecast limits.
Last week, I was invited to take part in an eBay survey and said yes. I am not sure what behaviour of mine had prompted this particular survey; it may have been another action that was outside their forecast limits, when I bought in a "Buy it now" sale; I invariably use auctions for my collecting.
There was something familiar about the structure of the questions, but I confess that I didn't recognise what it was until after I had answered the last questions and submitted the online survey. eBay was using the AHP (Analytic Hierarchy Process) to find out what I felt about some of the factors that vary from vendor to vendor. I was asked to imagine that I was considering an item at a particular price (30 British pounds) in a Buy it Now sale. That fixed one variable. Then I was asked to consider variations of delivery cost, photo and description quality, speed of communication, first in a ranking, and then by a succession of "Which do you prefer?" comparisons where two sales with differing profiles were offered. I think that I was reasonably consistent in my answers, although the example sales that were offered did not fit into my profile of eBay use. (For stamps, postage is seldom more than GBP1 within the UK and usually less than USD3 internationally; the examples offered had postage figures of up to GBP6.)
Wanting to find out more, I Googled for "eBay analytic hierarchy process" and came up with the answer that eBay does use AHP and ANP (network) and its use is mentioned in the citation of an INFORMS award to Thomas Saaty and in a publicity release put out by Decision Lens, the consultancy used by eBay for their AHP work.
It is strange to contribute to an OR study and only realise that you are doing so afterwards!
Last week, I was invited to take part in an eBay survey and said yes. I am not sure what behaviour of mine had prompted this particular survey; it may have been another action that was outside their forecast limits, when I bought in a "Buy it now" sale; I invariably use auctions for my collecting.
There was something familiar about the structure of the questions, but I confess that I didn't recognise what it was until after I had answered the last questions and submitted the online survey. eBay was using the AHP (Analytic Hierarchy Process) to find out what I felt about some of the factors that vary from vendor to vendor. I was asked to imagine that I was considering an item at a particular price (30 British pounds) in a Buy it Now sale. That fixed one variable. Then I was asked to consider variations of delivery cost, photo and description quality, speed of communication, first in a ranking, and then by a succession of "Which do you prefer?" comparisons where two sales with differing profiles were offered. I think that I was reasonably consistent in my answers, although the example sales that were offered did not fit into my profile of eBay use. (For stamps, postage is seldom more than GBP1 within the UK and usually less than USD3 internationally; the examples offered had postage figures of up to GBP6.)
Wanting to find out more, I Googled for "eBay analytic hierarchy process" and came up with the answer that eBay does use AHP and ANP (network) and its use is mentioned in the citation of an INFORMS award to Thomas Saaty and in a publicity release put out by Decision Lens, the consultancy used by eBay for their AHP work.
It is strange to contribute to an OR study and only realise that you are doing so afterwards!
Monday 1 December 2008
Genchi Genbutsu
Genchi Genbutsu is a new expression for me. It was posted in a discussion board, as relevant experience for anyone in O.R.. The wikipedia summary is:
Genchi Genbutsu (現地現物) means "go and see for yourself" and it is an integral part of the Toyota Production System. It refers to the fact that any information about a process will be simplified and abstracted from its context when reported. This has often been one of the key reasons why solutions designed away from the process seem inappropriate.
I don't know why there is so little literature drawing on the parallels between Genchi Genbutsu and practical O.R.; good O.R. requires the modeller to understand what is happening in practice, and you do not learn that by sitting in an office. I suppose that there are eqivalent expressions in English -- "go and see for yourself", "management by walking around", "walk the line". But too many managers (and a few O.R. people) rely on second-hand information.
Genchi Genbutsu (現地現物) means "go and see for yourself" and it is an integral part of the Toyota Production System. It refers to the fact that any information about a process will be simplified and abstracted from its context when reported. This has often been one of the key reasons why solutions designed away from the process seem inappropriate.
I don't know why there is so little literature drawing on the parallels between Genchi Genbutsu and practical O.R.; good O.R. requires the modeller to understand what is happening in practice, and you do not learn that by sitting in an office. I suppose that there are eqivalent expressions in English -- "go and see for yourself", "management by walking around", "walk the line". But too many managers (and a few O.R. people) rely on second-hand information.
Tuesday 25 November 2008
Defining O.R.
The journal that I edit, International Abstracts in O.R. (IAOR), aims to index and abstract the worldwide academic literature of O.R. and Management Science. This means that I (and others) need to make binary decisions about papers; is this one "O.R/M.S." or not?
So what rules to follow? I have several. There are about 40 journals which are abstracted cover-to-cover. These are the journals published by one or more national O.R. societies, such as Operations Research, Management Science, Journal of the Operational Research Society, 4OR and ORiON. There are others which are clearly primary journals in O.R. such as Omega and Health Care Management Science Dealing with these is, so to speak, easy. Then there are about a hundred which regularly include O.R. related papers. Then, I have a long list of journals which have, at one time or another, provided one or more abstracts for IAOR. Some of these abstracts have been found by serendipity, others by researchers citing them in papers in the principal journals. But I need to decide that binary question in each case; is this O.R.? I look at the content (as described in the title and the abstract). As I do, I ask myself what the paper is about. If it is a paper about theory, is the theory directly concerned with a modelling tool (not, I stress, necessarily a mathematical tool) from the suite of techniques used in O.R.. If so, I say yes. If the theory is less directly relevant, I speculate about whether it is close to a technique that is used in O.R.. Practical papers are considered with the question: is this about a decision-making problem? Is there something that a decision-maker could learn from? Is this paper about a problem in practice where I would expect an O.R. person to be involved?
These may appear naive heuristics, but they work. And in my reading, I can add further questions. When I taught in a unit devoted to statistics and operational research, I often used the (crude) distinction that statistics was concerned with looking at what had happened, and O.R. with modelling what might happen, to answer the questions "What if?" and/or "What's best?"
There are still fuzzy edges, at the interfaces with other disciplines. Engineering problems, economic models, psychology of decision-making ... all pose classification uncertainties. But all of these point to the universality of O.R. in the world today.
So what rules to follow? I have several. There are about 40 journals which are abstracted cover-to-cover. These are the journals published by one or more national O.R. societies, such as Operations Research, Management Science, Journal of the Operational Research Society, 4OR and ORiON. There are others which are clearly primary journals in O.R. such as Omega and Health Care Management Science Dealing with these is, so to speak, easy. Then there are about a hundred which regularly include O.R. related papers. Then, I have a long list of journals which have, at one time or another, provided one or more abstracts for IAOR. Some of these abstracts have been found by serendipity, others by researchers citing them in papers in the principal journals. But I need to decide that binary question in each case; is this O.R.? I look at the content (as described in the title and the abstract). As I do, I ask myself what the paper is about. If it is a paper about theory, is the theory directly concerned with a modelling tool (not, I stress, necessarily a mathematical tool) from the suite of techniques used in O.R.. If so, I say yes. If the theory is less directly relevant, I speculate about whether it is close to a technique that is used in O.R.. Practical papers are considered with the question: is this about a decision-making problem? Is there something that a decision-maker could learn from? Is this paper about a problem in practice where I would expect an O.R. person to be involved?
These may appear naive heuristics, but they work. And in my reading, I can add further questions. When I taught in a unit devoted to statistics and operational research, I often used the (crude) distinction that statistics was concerned with looking at what had happened, and O.R. with modelling what might happen, to answer the questions "What if?" and/or "What's best?"
There are still fuzzy edges, at the interfaces with other disciplines. Engineering problems, economic models, psychology of decision-making ... all pose classification uncertainties. But all of these point to the universality of O.R. in the world today.
Innumeracy!
For those of us involved with O.R., numeracy is practically second nature. Most O.R. people have above average number skills, however you measure them. So it is sometimes salutary, and even shocking to realise that others are numerically illiterate, even though they may be otherwise well-educated.
Two stories from my experience this week illustrate this:
(1) (and this is horrifying) A radio interview with a debt-counselling service in my home city of Exeter. The speaker described how a door-to-door salesman offered a loan "with 100% interest", which the borrower thought was a very good deal.
(2) My national newspaper headlined a column chart showing the "U.K. Government borrowing" for this year and the previous two. Each column was below the axis, and the amount was clearly marked as being negative, becoming increasingly negative as time progressed. Obviously nobody had realised that borrowing a negative amount meant the opposite of what was intended.
Those of us involved in education can take these as reminders that when we have numerical results to communicate, we need to explain them as clearly as possible.
To end on a lighter note, on the same theme. Another newspaper story concerned with the current credit crunch had obviously been hastily sent through a spell-checker. There were two references to £100bun loans.
Two stories from my experience this week illustrate this:
(1) (and this is horrifying) A radio interview with a debt-counselling service in my home city of Exeter. The speaker described how a door-to-door salesman offered a loan "with 100% interest", which the borrower thought was a very good deal.
(2) My national newspaper headlined a column chart showing the "U.K. Government borrowing" for this year and the previous two. Each column was below the axis, and the amount was clearly marked as being negative, becoming increasingly negative as time progressed. Obviously nobody had realised that borrowing a negative amount meant the opposite of what was intended.
Those of us involved in education can take these as reminders that when we have numerical results to communicate, we need to explain them as clearly as possible.
To end on a lighter note, on the same theme. Another newspaper story concerned with the current credit crunch had obviously been hastily sent through a spell-checker. There were two references to £100bun loans.
Monday 17 November 2008
Google and influenza (flu)
One of my email signatures uses the following quotation:
In the information age, somebody has to specialize in the development and presentation of really useful information. Doing that for management and decision-making applications is the core role of Operational Research scientists. (Randy Robinson, the first executive director of INFORMS)
Throughout my working life, I have worked alongside some excellent statisticians, and been part of some wonderful data collection exercises. Randy R's comment sums up an important aspect of the work of O.R. people -- taking data which has been collected and making sense of that data for other people to use intelligently.
Over the past week, Google's work on modelling influenza epidemics has been made public. Essentially, the company is monitoring the fraction of search queries that they judge to be related to flu, week by week, and region by region in the USA. The results so far show that the fraction of queries that are related to flu increases during an epidemic, and the change can be seen more quickly than is possible by conventional means of epidemiological monitoring. So here is "really useful information" for "management and decision-making". Google's work can be read here.
In the information age, somebody has to specialize in the development and presentation of really useful information. Doing that for management and decision-making applications is the core role of Operational Research scientists. (Randy Robinson, the first executive director of INFORMS)
Throughout my working life, I have worked alongside some excellent statisticians, and been part of some wonderful data collection exercises. Randy R's comment sums up an important aspect of the work of O.R. people -- taking data which has been collected and making sense of that data for other people to use intelligently.
Over the past week, Google's work on modelling influenza epidemics has been made public. Essentially, the company is monitoring the fraction of search queries that they judge to be related to flu, week by week, and region by region in the USA. The results so far show that the fraction of queries that are related to flu increases during an epidemic, and the change can be seen more quickly than is possible by conventional means of epidemiological monitoring. So here is "really useful information" for "management and decision-making". Google's work can be read here.
Monday 10 November 2008
Wjat went wrong at T5?
Mention "Terminal 5" to an air traveller this year and you may well be greeted by a hollow laugh. The new terminal at Heathrow gummed up within a couple of hours of opening. The summary report states that:
"On the first day of operation alone, 36,584 passengers were frustrated by the 'Heathrow hassle' that Terminal 5 had been designed to eliminate." More than 600 flights were cancelled in the first 11 days, and "23,205 bags required manual sorting before being returned to their owners". The causes: "Insufficient communication between owner (BAA) and operator (British Airways, or BA), and poor staff training and system testing".
All this sounds strange, because the airline BA has one of the most efficient O.R. teams in the United Kingdom. (Yes I mean it, and am not hoping for freebies.) But the word "communication" is at the core of the problem, as a short extract from what Iggy Vaid (a shop steward for staff working on T5) said in an appraisal of the mess:
"I hate to say that about my own airline, but culturally the existing management structure is one where you cannot tell the emperor that he has no clothes; you have to say his clothes are beautiful. No supervisor or person can tell his or her boss that the system will not work. If you do you are not a team player; you are sidelined, so for that reason you say that it works and the emperor has beautiful clothes."
Even the most efficient O.R. work will not be successful if there is no way to communicate it.
Later in the report from which I am quoting (The Independent, Saturday 10th November 2008) another area of potential (inevitable) breakdown was highlighted:
"Should there be a failure in the system at any point it will not self-rectify."
Hidden in these words is a warning for every large O.R. project
"On the first day of operation alone, 36,584 passengers were frustrated by the 'Heathrow hassle' that Terminal 5 had been designed to eliminate." More than 600 flights were cancelled in the first 11 days, and "23,205 bags required manual sorting before being returned to their owners". The causes: "Insufficient communication between owner (BAA) and operator (British Airways, or BA), and poor staff training and system testing".
All this sounds strange, because the airline BA has one of the most efficient O.R. teams in the United Kingdom. (Yes I mean it, and am not hoping for freebies.) But the word "communication" is at the core of the problem, as a short extract from what Iggy Vaid (a shop steward for staff working on T5) said in an appraisal of the mess:
"I hate to say that about my own airline, but culturally the existing management structure is one where you cannot tell the emperor that he has no clothes; you have to say his clothes are beautiful. No supervisor or person can tell his or her boss that the system will not work. If you do you are not a team player; you are sidelined, so for that reason you say that it works and the emperor has beautiful clothes."
Even the most efficient O.R. work will not be successful if there is no way to communicate it.
Later in the report from which I am quoting (The Independent, Saturday 10th November 2008) another area of potential (inevitable) breakdown was highlighted:
"Should there be a failure in the system at any point it will not self-rectify."
Hidden in these words is a warning for every large O.R. project
Cost Benefit in New York
One of the few email newsletters that I really enjoy is the Internet Scout Report from the Computer Science Department, University of Wisconsin, even though it generally has little to do with O.R.. However, the phrase "weighing costs and benefits" leapt out at me this week, in relationship to the costs for tax-payers of a new sports stadium in New York, and the related benefits. The email gave links to news stories about the issue. Obviously this is an area for O.R. analysis (not just economics) and modelling. But perhaps the most telling comment is that in the Sabernomics blog, where it records:
There is little evidence of large increases in income or employment associated with the introduction of professional sports or the construction of new stadiums.
There is little evidence of large increases in income or employment associated with the introduction of professional sports or the construction of new stadiums.
Monday 3 November 2008
Placing a value on an experience
In my previous post I referred to the IFORS Newsletter and the account of the IFORS conference in Sandton, South Africa. Among the pictures, there is one of several people stroking a cheetah in a wildlife rescue centre, with the note that there was a fixed charge per photograph. The four people shared the expense. This raises the question -- which is OR related -- of what would be a fair price for that "experience". Someone has had to make the decision and fix a price, presumably to try and maximise the revenue. Costs must be more or less fixed, and extra stroking won't wear out the cheetah. So someone judges what the market will bear. How?
It contrasts with a visit that I paid to a similar rescue centre, where stroking selected animals was free as part of the overall "experience". Another model of costing?
It contrasts with a visit that I paid to a similar rescue centre, where stroking selected animals was free as part of the overall "experience". Another model of costing?
More about IFORS 2008 conference
The IFORS Newsletter dated September 2008 has recently arrived on the web (HERE).
It is a colourful edition, with several accounts of the 2008 conference at Sandton in South Africa. My friend, former colleague, and predecessor as editor of IAOR (International Abstracts in Operations Research) has printed his diary and thoughts about the event. That is one of the worthwhile articles in the newsletter, even though Graham is described as one of the "stall warts" of IFORS, and his account is a "dairy account". Spell checkers are wonder full!
It is a colourful edition, with several accounts of the 2008 conference at Sandton in South Africa. My friend, former colleague, and predecessor as editor of IAOR (International Abstracts in Operations Research) has printed his diary and thoughts about the event. That is one of the worthwhile articles in the newsletter, even though Graham is described as one of the "stall warts" of IFORS, and his account is a "dairy account". Spell checkers are wonder full!
Tuesday 28 October 2008
Operational Reesearch and the urban cyclist
I am committed to cycling as a sensible means of transport, even though my own cycling has not been completely accident-free. The other day I was musing on whether there is much literature about O.R. and cycling. And then I heard about eBourbaki, which describes itself as "a mathematical problem-solving company" (no mention of O.R.!) and the competition that had been run to model cycle-hire facilities in London. Readers of "OR/MS Today" will have seen that one of the political conventions modeled the location of cycle-loan facilities in 2008. eBourbaki run their problems as competitions, and the winners created two mathematical models with one describing commuter flow and the other examining the possible configuration of any bicycle stations.
It is to be hoped that the city will work with the winners and consider using their solutions and models in the network design.
The models suggested that, for a London-based scheme to be successful, 12 large bicycle stations should be placed near railway stations in central London with 250 smaller stations distributed throughout the West End and the City of London. An average of 20 bikes per small station was found to be the most efficient number.
Monday 20 October 2008
Visualising numbers
It is only distantly related to OR/MS, but I have touched on the problem of guesstimates before, as it is a sign of numeracy (an essential OR skill) to know when numbers "feel" right. So I was pleased to see the megapenny site which has pictures of what large numbers of one cent pieces will look like and weigh. In these days of governments bailing out banks and othe financial concerns, it is instuctive to note that $18 billion is just enough to fill the Empire State Building with one cent coins.
Monday 6 October 2008
The Harvest Thanksgiving Supply Chain
In an earlier blog I mentioned links between O.R. and Christianity. September and October are the months when (in the Northern Hemisphere's temperate regions) churches celebrate harvest thanksgiving. (Cue for the traditional harvest hymn: "We plough the fields and scatter, The good seed on the land, But it is fed and watered, By God's Almighty hand." You can tell that I am a Brit, because I spell plough without a "w".) Increasingly church services also give thanks for the makers and producers of food -- and at our service yesterday the preacher thanked God for the drivers of delivery trucks -- she almost used the words "supply chain", but the congregation would not have been familiar with that technical term.
Operational Research and the Bible
An interesting diagram has been posted in a forum for vsiualising information.
The picture has one entry along the x-axis for each chapter in the Bible and there are coloured arcs between chapters which cross-reference to one another. (This means that there is a word, phrase or idea that occurs in one chapter that also appears in another; "The Lord is my Shepherd" in Psalms links with "I am the Good Shepherd" in the gospels.) There are 63779 such arcs -- the picture is beautiful. Mathematicians and O.R. scientists will recognise that the structure is a graph, with each chapter a vertex, and that leads to assorted questions about the graph. How many components does it have? Are there isolated vertices? What are the properties of the components?
I'd like to hear answers.
The picture has one entry along the x-axis for each chapter in the Bible and there are coloured arcs between chapters which cross-reference to one another. (This means that there is a word, phrase or idea that occurs in one chapter that also appears in another; "The Lord is my Shepherd" in Psalms links with "I am the Good Shepherd" in the gospels.) There are 63779 such arcs -- the picture is beautiful. Mathematicians and O.R. scientists will recognise that the structure is a graph, with each chapter a vertex, and that leads to assorted questions about the graph. How many components does it have? Are there isolated vertices? What are the properties of the components?
I'd like to hear answers.
Tuesday 16 September 2008
Operational Research and Psychology
Well, actually, as many people have pointed out in the past, psychology is part of O.R., so my title creates a false split.
A part of my education in O.R. was the apocryphal story about the O.R. consultants and the problem of the lifts in the skyscraper. Occupants of the building complained about the time they spent waiting for the lifts to get from the upper floors to the lobby. So the consultants looked at the options, they built models of the consequences of extra lifts, faster lifts, dedicated lifts from the upper floors, etc., etc. The reduction in mean waiting time was always very small. Then the psychologist in the team suggested that the company place mirrors in the waiting areas for each lift shaft. He explained why. Why? (see the end of this blog)
I had the same sort of problem at the weekend. Each year, on the second Saturday in September, I take part in a prayer walk around the churches of the city of Exeter, my home. The idea is that a group will start early in the morning, follow a winding route around the city, stopping to pray in about 25 different churches, praying for the city and its people. I have the responsibility of planning the route; for several years we have used the same route. This is not a TSP, of course, because the walkers don't end at the start point. It is not quite minimal but is quite close to the best.
In 2008, two new churches came into the list of city churches. We decided to include one of them, miss the other, and omit another, because it was linked strongly with these two. But the selected new church would add so much extra mileage to the old route that we needed a new plan. So we made the walk have two starts, with two groups starting at 8am and converging on the cathedral at 10am to walk for the rest of the day together. So I devised such a route; the two initial legs had minimal lengths for the churches that they visited, given the fixed end at 10am. However, I had forgotten the psychology. The second route had a lot of road walking, and went past the main railway station, so there was traffic. The consensus was that it would be better to walk a little further (about 300 to 400 metres) and walk beside the river on a footpath, away from the traffic. So that is what we will probably do next year -- alter the start and route to give more traffic-free paths.
Why the mirrors? The reason was that it was not the actual waiting time that people notices, but the perceived waste of time. Mirrors distracted people; some could admire themselves and tidy their clothes and appearance, others could watch. (In the 1970's there were sexist stereotypes for these activities, which I will not repeat.)
A part of my education in O.R. was the apocryphal story about the O.R. consultants and the problem of the lifts in the skyscraper. Occupants of the building complained about the time they spent waiting for the lifts to get from the upper floors to the lobby. So the consultants looked at the options, they built models of the consequences of extra lifts, faster lifts, dedicated lifts from the upper floors, etc., etc. The reduction in mean waiting time was always very small. Then the psychologist in the team suggested that the company place mirrors in the waiting areas for each lift shaft. He explained why. Why? (see the end of this blog)
I had the same sort of problem at the weekend. Each year, on the second Saturday in September, I take part in a prayer walk around the churches of the city of Exeter, my home. The idea is that a group will start early in the morning, follow a winding route around the city, stopping to pray in about 25 different churches, praying for the city and its people. I have the responsibility of planning the route; for several years we have used the same route. This is not a TSP, of course, because the walkers don't end at the start point. It is not quite minimal but is quite close to the best.
In 2008, two new churches came into the list of city churches. We decided to include one of them, miss the other, and omit another, because it was linked strongly with these two. But the selected new church would add so much extra mileage to the old route that we needed a new plan. So we made the walk have two starts, with two groups starting at 8am and converging on the cathedral at 10am to walk for the rest of the day together. So I devised such a route; the two initial legs had minimal lengths for the churches that they visited, given the fixed end at 10am. However, I had forgotten the psychology. The second route had a lot of road walking, and went past the main railway station, so there was traffic. The consensus was that it would be better to walk a little further (about 300 to 400 metres) and walk beside the river on a footpath, away from the traffic. So that is what we will probably do next year -- alter the start and route to give more traffic-free paths.
Why the mirrors? The reason was that it was not the actual waiting time that people notices, but the perceived waste of time. Mirrors distracted people; some could admire themselves and tidy their clothes and appearance, others could watch. (In the 1970's there were sexist stereotypes for these activities, which I will not repeat.)
Wednesday 10 September 2008
Education and brains
"To repeat what others have said, requires education; to challenge it, requires brains."
Mary Pettibone Poole, A Glass Eye at a Keyhole, 1938
I had not come across this thought-provoking quotation until yesterday; then I was in a meeting and found it inscribed on the wall of the small conference room. Yes, it has O.R. applications. Part of the skill in the O.R. scientist should be (must be?) the ability to recognise where a model developed earlier in a different context might apply to a practical problem. So queue theory is used for call centres and for slow moving inventories. Location theory applies to ATM outlets and airports. But sometimes the obvious is not appropriate -- then the O.R. scientist needs brains to challenge the obvious.
Wikipedia doesn't tell me anything about the author or the book. And I wonder who has used their brains and challenged Mary's words, rather than repeat them?
Mary Pettibone Poole, A Glass Eye at a Keyhole, 1938
I had not come across this thought-provoking quotation until yesterday; then I was in a meeting and found it inscribed on the wall of the small conference room. Yes, it has O.R. applications. Part of the skill in the O.R. scientist should be (must be?) the ability to recognise where a model developed earlier in a different context might apply to a practical problem. So queue theory is used for call centres and for slow moving inventories. Location theory applies to ATM outlets and airports. But sometimes the obvious is not appropriate -- then the O.R. scientist needs brains to challenge the obvious.
Wikipedia doesn't tell me anything about the author or the book. And I wonder who has used their brains and challenged Mary's words, rather than repeat them?
Operational Research and Christianity
I recently read that the educational background of the Bishop Sarah Frances Davis in the African Methodist Episcopal church has a degree in Management Science. Can O.R./M.S. help in such a role? In IAOR, there are several papers recorded which refer to applications of O.R. in the life of the church. In the 1970's and 80's, Malcolm King, Alan Mercer and I published some papers about the place of modelling in the deployment of clergy in the U.K.
[Malcolm King, David K. Smith and Alan Mercer "Towards Diocesan Planning" Journal of the Operational Research Society 29 p856-866 (1978); Malcolm King and David K. Smith "Are the Clergy Being Deployed Fairly?" The Churchman 5 p54-60 (1980); Malcolm King and David K. Smith "Planning the Deployment of Clergy" Long Range Planning 5 p104-111 (1982)]
Earlier, Alan had published "The Churching of Urban England" in the proceedings of the 1969 IFORS conference, which looked at the location of places of worship.
So there are two possible areas for fruitful O.R.; manpower planning, and location models.
I wonder if Bishop Davis knows about this work.
[Malcolm King, David K. Smith and Alan Mercer "Towards Diocesan Planning" Journal of the Operational Research Society 29 p856-866 (1978); Malcolm King and David K. Smith "Are the Clergy Being Deployed Fairly?" The Churchman 5 p54-60 (1980); Malcolm King and David K. Smith "Planning the Deployment of Clergy" Long Range Planning 5 p104-111 (1982)]
Earlier, Alan had published "The Churching of Urban England" in the proceedings of the 1969 IFORS conference, which looked at the location of places of worship.
So there are two possible areas for fruitful O.R.; manpower planning, and location models.
I wonder if Bishop Davis knows about this work.
Monday 1 September 2008
The Numerati by Stephen Baker
Also in the INFORMS enews:
Business Week reporter Stephen Baker, has written The Numerati, which describes the superpowered contributions that mathematicians-and operations researchers-are making to businesses and organizations today. Publishers Weekly has given advance praise, calling The Numerati a "captivating exploration" about "a new breed of entrepreneurial mathematicians." Houghton Mifflin is publishing the book this fall (autumn if you live in the United Kingdom).
There is a video of an interview with the author here.
Business Week reporter Stephen Baker, has written The Numerati, which describes the superpowered contributions that mathematicians-and operations researchers-are making to businesses and organizations today. Publishers Weekly has given advance praise, calling The Numerati a "captivating exploration" about "a new breed of entrepreneurial mathematicians." Houghton Mifflin is publishing the book this fall (autumn if you live in the United Kingdom).
There is a video of an interview with the author here.
Blogging on O.R.
In the INFORMS enews, there was this note:
Some of INFORMS' most enthusiastic members are blogging about O.R., operations management, engineering, and science. Have you visited their blogs yet?
Former INFORMS President Michael Trick of Carnegie Mellon University, who currently serves on the INFORMS Public Information Committee (PIC), blogs from conferences and shares fresh news about O.R. Michael Trick's Operations Research Blog.
Fellow PIC member Aurélie Thiele of Lehigh University shares her views at Thoughts on Business, Engineering, and Higher Education.
Wharton Professor Gerard Cachon, the current editor of M&SOM and the incoming editor of Management Science, blogs on operations management at Matching Supply with Demand.
Visit these blogs, catch up on news and views, and share your own O.R. perspectives. Know about more O.R. blogs? Let us know by sending an email to barry.list@informs.org.
To which we can add this blog, and that of Laura McLay (Punk Rock Operations Research)
Some of INFORMS' most enthusiastic members are blogging about O.R., operations management, engineering, and science. Have you visited their blogs yet?
Former INFORMS President Michael Trick of Carnegie Mellon University, who currently serves on the INFORMS Public Information Committee (PIC), blogs from conferences and shares fresh news about O.R. Michael Trick's Operations Research Blog.
Fellow PIC member Aurélie Thiele of Lehigh University shares her views at Thoughts on Business, Engineering, and Higher Education.
Wharton Professor Gerard Cachon, the current editor of M&SOM and the incoming editor of Management Science, blogs on operations management at Matching Supply with Demand.
Visit these blogs, catch up on news and views, and share your own O.R. perspectives. Know about more O.R. blogs? Let us know by sending an email to barry.list@informs.org.
To which we can add this blog, and that of Laura McLay (Punk Rock Operations Research)
Swimming again
A footnote to the blog (10th June 2008) about modelling demand for swimming.
A major cereal company in the U.K. (the name begins with "K") offered vouchers for free swims on its packets. As a promotion, it has been very successful. But the swimming pool in Exeter has had to restrict the hours when the vouchers can be used. It isn't really a rationing of use, simply a way of allowing the fee-paying swimmers time to enjoy their swim and concentrating the use of the vouchers to times when fee-payers can stay away.
This is an interesting solution to a demand problem. It has obvious links to revenue management.
A major cereal company in the U.K. (the name begins with "K") offered vouchers for free swims on its packets. As a promotion, it has been very successful. But the swimming pool in Exeter has had to restrict the hours when the vouchers can be used. It isn't really a rationing of use, simply a way of allowing the fee-paying swimmers time to enjoy their swim and concentrating the use of the vouchers to times when fee-payers can stay away.
This is an interesting solution to a demand problem. It has obvious links to revenue management.
My week in O.R. at the IFORS 2008 conference
The newsletter of the U.K. O.R. Society includes a feature "My week in O.R." or "My month in O.R.". In the issue for September 2008, my contribution about the IFORS conference was included, as follows:
It’s 9:20am on Sunday morning, and I’m going into church. Together with my wife Tina I am in Sandton, South Africa for the triennial IFORS (International Federation of OR Societies) conference, which starts today. Sandton is a strange place – a suburb of Johannesburg – with a strange, artificial air to it. We arrived yesterday and checked into one of the conference hotels. It’s four-star, international, impersonal and totally devoid of character. We are noticing the difference (it is almost culture shock) after a week and a half of holiday. The first week was a safari in an overland truck, staying in game lodges and backpackers’ hostels, in a group from five countries and speaking three languages. The beds were more comfortable than our four-star accommodation here, and the staff of lodges and hostels were friendlier.
Amidst the hotels, offices and shopping malls of Sandton, there is one convenient church; I’ve tried to find somewhere to worship at most of the IFORS conferences that I have been to, with interesting results. Here Tina and I, with conference friends, have arrived to find the church empty except for one man fixing the P.A. “Everyone else will be along shortly” he tells us; sure enough, at 9:28, the church fills up ready for the 9:30 service. We are welcomed, and the service proceeds, with more and more people drifting in over the next half-hour. The sermon – longer than in most U.K. churches, has an O.R. link; some of the New Testament parables stop with a cliff-hanger and seem incomplete; isn’t this a bit like some reports of O.R. work, which leave the reader asking “What happened next?”
My afternoon is spent talking about the International Abstracts in O.R. (IAOR), which I edit, and whose sales provide IFORS with a steady income stream. The conference is the time when we are launching an updated version of the electronic IAOR, so the leaders of IFORS are particularly interested in the report. Then I get involved with a meeting for editorial advisers of another journal, which is filled with statistics about submission rates and publication delays.
IFORS conferences follow a regular pattern: reception on Sunday, two days of papers, one day-long excursion or choice of excursions, and two more days of papers. The evening’s reception has been scheduled for two and a half hours, and by the time Tina and I get there, people are already drifting away. O.R. observation: receptions should be shorter, to allow people to meet one another; what is the optimum length?
Being in Africa, the conference opens with drumming and dancing, followed by a welcome from the minister of science and a plenary speech by Clem Sunter about Scenario Planning “The world and South Africa in the 2010s”. Some of his remarks about planning are clearly particularly pertinent for the minister. Then there are the usual parallel sessions that make up all conferences. I opt for the session on developing countries, to hear the seven papers that have been shortlisted as candidates for an IFORS prize. Like many conference sessions, this one is a curate’s egg.
I skipped out of the sessions for part of Tuesday to accompany my “accompanying person” on the trip to a glass factory – which was ninety percent disastrous. The most interesting bit was meeting the team who had created the key-rings in the conference packs. Made of glass beads, threaded onto wire, each is different. Our guide explained how the random patterns were produced, using a device worthy of Heath Robinson; all the parts used are recycled materials. The following day we have opted for a trip to a diamond mine. What struck us most forcefully was the ordinariness of the site, apart from the intense security. We didn’t lose touch with O.R.; the mine has monthly performance targets, derived from a forecasting model. Earlier this year, the target was missed because of power cuts; loss of 20% of the power meant a loss of 50% in production.
Sandton is a strange place for a conference. Because of the security, Tina can’t simply go off for a walk, and she spends a good deal of time reading in the hotel garden. There is a wide choice of shopping malls, one linked to our hotel by an underground passage, another attached to the convention centre. Even the winter sales in the shops fail to attract her for very long.
Thursday – another day of parallel sessions. In the evening, the gala conference dinner, enlivened (if that is the right word) by a parade of representatives of the 48 national societies that are members of IFORS, ordered according to when each society joined the federation. There is more dancing, loud jazz music, queues for the buffet, and very few speeches. Years ago, I read some words of Hermann Bondi: “Little children, from the age of three upwards, ask the question ‘Why?’ The aim of education is to stop such questions. Education has its failures. They are called scientists.” The dinner is a time for me to ask “Why?” and the subsequent question, for me (as an O.R. scientist), is “How?” “Why is the service so poor? How could it be improved?” These two questions have never been far from my mind all week. “Why is the design of the convention centre where the conference is being held so weird and inefficient? How …?” “Why is the hotel service indifferent? How …?” My mentors in my distant O.R. youth emphasised the importance of time spent on site, experiencing the problem that was being studied; managers in service industries often should learn the same way, experiencing the service as a customer and seeing how it could be improved. The hotel has a sort of feedback loop of control, with customer response forms; will any of my comments be dealt with if I were to come back next year?
And then comes Friday, and the closing plenary session. Very interesting, but the presentation is very poor, and the speaker could have said in 20 minutes what took 45. Those delegates who are still around start to disperse, some back home, others to holiday in South Africa. Has it been a successful conference? Yes, I have met a lot of people, some old friends, some new ones. Have there been any outstanding sessions? Not for me, sadly.
Back in the office in Exeter the following Monday, there are my emails to be dealt with, and a meeting with my research student, M, and her project sponsors. She has been trying to make sense of a large mass of historic data; we spend time trying to get something useful out of it for the sponsor. I encourage M to “play with the data” to try and find useful information, though I remind her that this is not an expression to use in our presentations. The meeting with sponsor goes well, as we are joined by one of the U.K.’s experts in the type of data we are looking at, and he has excellent communication skills as he talks about the project and its context. He doesn’t mind when I ask those two questions, over and over “Why?” and “How?”. In terms of information gained, the hour with him has given me as much as the whole conference.
It’s 9:20am on Sunday morning, and I’m going into church. Together with my wife Tina I am in Sandton, South Africa for the triennial IFORS (International Federation of OR Societies) conference, which starts today. Sandton is a strange place – a suburb of Johannesburg – with a strange, artificial air to it. We arrived yesterday and checked into one of the conference hotels. It’s four-star, international, impersonal and totally devoid of character. We are noticing the difference (it is almost culture shock) after a week and a half of holiday. The first week was a safari in an overland truck, staying in game lodges and backpackers’ hostels, in a group from five countries and speaking three languages. The beds were more comfortable than our four-star accommodation here, and the staff of lodges and hostels were friendlier.
Amidst the hotels, offices and shopping malls of Sandton, there is one convenient church; I’ve tried to find somewhere to worship at most of the IFORS conferences that I have been to, with interesting results. Here Tina and I, with conference friends, have arrived to find the church empty except for one man fixing the P.A. “Everyone else will be along shortly” he tells us; sure enough, at 9:28, the church fills up ready for the 9:30 service. We are welcomed, and the service proceeds, with more and more people drifting in over the next half-hour. The sermon – longer than in most U.K. churches, has an O.R. link; some of the New Testament parables stop with a cliff-hanger and seem incomplete; isn’t this a bit like some reports of O.R. work, which leave the reader asking “What happened next?”
My afternoon is spent talking about the International Abstracts in O.R. (IAOR), which I edit, and whose sales provide IFORS with a steady income stream. The conference is the time when we are launching an updated version of the electronic IAOR, so the leaders of IFORS are particularly interested in the report. Then I get involved with a meeting for editorial advisers of another journal, which is filled with statistics about submission rates and publication delays.
IFORS conferences follow a regular pattern: reception on Sunday, two days of papers, one day-long excursion or choice of excursions, and two more days of papers. The evening’s reception has been scheduled for two and a half hours, and by the time Tina and I get there, people are already drifting away. O.R. observation: receptions should be shorter, to allow people to meet one another; what is the optimum length?
Being in Africa, the conference opens with drumming and dancing, followed by a welcome from the minister of science and a plenary speech by Clem Sunter about Scenario Planning “The world and South Africa in the 2010s”. Some of his remarks about planning are clearly particularly pertinent for the minister. Then there are the usual parallel sessions that make up all conferences. I opt for the session on developing countries, to hear the seven papers that have been shortlisted as candidates for an IFORS prize. Like many conference sessions, this one is a curate’s egg.
I skipped out of the sessions for part of Tuesday to accompany my “accompanying person” on the trip to a glass factory – which was ninety percent disastrous. The most interesting bit was meeting the team who had created the key-rings in the conference packs. Made of glass beads, threaded onto wire, each is different. Our guide explained how the random patterns were produced, using a device worthy of Heath Robinson; all the parts used are recycled materials. The following day we have opted for a trip to a diamond mine. What struck us most forcefully was the ordinariness of the site, apart from the intense security. We didn’t lose touch with O.R.; the mine has monthly performance targets, derived from a forecasting model. Earlier this year, the target was missed because of power cuts; loss of 20% of the power meant a loss of 50% in production.
Sandton is a strange place for a conference. Because of the security, Tina can’t simply go off for a walk, and she spends a good deal of time reading in the hotel garden. There is a wide choice of shopping malls, one linked to our hotel by an underground passage, another attached to the convention centre. Even the winter sales in the shops fail to attract her for very long.
Thursday – another day of parallel sessions. In the evening, the gala conference dinner, enlivened (if that is the right word) by a parade of representatives of the 48 national societies that are members of IFORS, ordered according to when each society joined the federation. There is more dancing, loud jazz music, queues for the buffet, and very few speeches. Years ago, I read some words of Hermann Bondi: “Little children, from the age of three upwards, ask the question ‘Why?’ The aim of education is to stop such questions. Education has its failures. They are called scientists.” The dinner is a time for me to ask “Why?” and the subsequent question, for me (as an O.R. scientist), is “How?” “Why is the service so poor? How could it be improved?” These two questions have never been far from my mind all week. “Why is the design of the convention centre where the conference is being held so weird and inefficient? How …?” “Why is the hotel service indifferent? How …?” My mentors in my distant O.R. youth emphasised the importance of time spent on site, experiencing the problem that was being studied; managers in service industries often should learn the same way, experiencing the service as a customer and seeing how it could be improved. The hotel has a sort of feedback loop of control, with customer response forms; will any of my comments be dealt with if I were to come back next year?
And then comes Friday, and the closing plenary session. Very interesting, but the presentation is very poor, and the speaker could have said in 20 minutes what took 45. Those delegates who are still around start to disperse, some back home, others to holiday in South Africa. Has it been a successful conference? Yes, I have met a lot of people, some old friends, some new ones. Have there been any outstanding sessions? Not for me, sadly.
Back in the office in Exeter the following Monday, there are my emails to be dealt with, and a meeting with my research student, M, and her project sponsors. She has been trying to make sense of a large mass of historic data; we spend time trying to get something useful out of it for the sponsor. I encourage M to “play with the data” to try and find useful information, though I remind her that this is not an expression to use in our presentations. The meeting with sponsor goes well, as we are joined by one of the U.K.’s experts in the type of data we are looking at, and he has excellent communication skills as he talks about the project and its context. He doesn’t mind when I ask those two questions, over and over “Why?” and “How?”. In terms of information gained, the hour with him has given me as much as the whole conference.
Wednesday 27 August 2008
Operational Research and Design
One of the subject headings in the International Abstracts in O.R. (IAOR) is "Design". Over the years, there have been comparatively few abstracts which were classified under this heading. I wondered why. What sort of papers would be classified as "O.R. in Design"? One tends to think of design in connection with small (comparatively) items or matters, when one is not concerned with aesthetics. Things like household equipment, the layout of roads, small engineering items. A useful text is The Design of Everyday Things by Donald A Norman -- which doesn't mention O.R. but does discuss optimality quite frequently. But this aspect of design does not lead to academic papers. Manufacturers employ designers to make money, not to produce learned papers. Look at the jets in the rotor of a dishwasher; someone has designed them, found the best angles, positions and sizes, in order to efficiently and cheaply carry out a dishwashing cycle. Hard work -- hard O.R. work -- but not worth writing about. Sometimes the results of design are commercial secrets. When I was recently out of my postgraduate training, I went on a site visit and asked about a piece of equipment on the production line. Had the company patented it? No, because a patent would be visible to their rivals.
But sometimes one wishes that the results of design as the result of a modelling process could be made public. By doing that the benefits of one person's analysis could be usefully shared. I come across such an example regularly. What is the optimal separation between cycle racks? By the swimming pool, there are six racks, at 45cm apart. The outermost racks are therefore 225cm apart, and one can park seven bicycles in the space. Near the office, there are four racks, 100cm apart. Two bikes can be parked in each gap, so in 300cm there are eight bikes. Which is better?
But sometimes one wishes that the results of design as the result of a modelling process could be made public. By doing that the benefits of one person's analysis could be usefully shared. I come across such an example regularly. What is the optimal separation between cycle racks? By the swimming pool, there are six racks, at 45cm apart. The outermost racks are therefore 225cm apart, and one can park seven bicycles in the space. Near the office, there are four racks, 100cm apart. Two bikes can be parked in each gap, so in 300cm there are eight bikes. Which is better?
Tuesday 26 August 2008
Operational Research and Waste Management
In the International Abstracts in Operations Research (IAOR), every abstract is given at least one subject category. We have a list of about 200 such, and an important part of the added value of IAOR is the fact that this assignment of categories takes place -- by an expert in O.R.. In addition, many papers have a free format description added, which is indexed in the print version and becomes a part of the online record. Over the last few years, there have been an increasing number of papers which have had the free format "Waste management" added. These papers have dealt with vehicle routing, crew scheduling, location of obnoxious facilities and other topics. It seems that this is a growing area of application for O.R..
I have my own problem in waste management, and have considered it from an O.R. perspective. How should I deal with the clippings from the hedge at home? The hedge is about 50 metres long, nearly two metres tall, and is privet. I use electric trimmers, once or twice a year. Privet branches tend to be long and straight, but there are a lot of them. The options are (1) to load them into the car and take them to the council dump, (2) to buy sacks from the council to be taken away by the garden refuse collection, (3) to burn them on a bonfire, (4) to shred them, (5) to leave them in a heap to rot slowly.
(1) and (2) are expensive options, and I would derive no benefit; (3) I reject on the basis that I do not like polluting the air and have no space for a fire; (5) is unsightly in the garden. So I shred the cuttings. But how? Over the years I have found that I can get a lot of shredding done with the rotary mower, simply by driving the mower over the trimmings as they have fallen on the ground. The method fails if the long straight branches have been raked -- the random alignment of branches is important. Some branches are not shredded by this process, and those go though a electric shredder -- which I do not use for the whole process as it is slower than the mower. Then I have to compost or use the shredded wood and leaves as mulch -- so I benefit from the nutrients; the garden is not totally organic, but I feel that I have done a little for the planet, and found my personal optimal solution. Now how can this process be written up as a journal paper?
I have my own problem in waste management, and have considered it from an O.R. perspective. How should I deal with the clippings from the hedge at home? The hedge is about 50 metres long, nearly two metres tall, and is privet. I use electric trimmers, once or twice a year. Privet branches tend to be long and straight, but there are a lot of them. The options are (1) to load them into the car and take them to the council dump, (2) to buy sacks from the council to be taken away by the garden refuse collection, (3) to burn them on a bonfire, (4) to shred them, (5) to leave them in a heap to rot slowly.
(1) and (2) are expensive options, and I would derive no benefit; (3) I reject on the basis that I do not like polluting the air and have no space for a fire; (5) is unsightly in the garden. So I shred the cuttings. But how? Over the years I have found that I can get a lot of shredding done with the rotary mower, simply by driving the mower over the trimmings as they have fallen on the ground. The method fails if the long straight branches have been raked -- the random alignment of branches is important. Some branches are not shredded by this process, and those go though a electric shredder -- which I do not use for the whole process as it is slower than the mower. Then I have to compost or use the shredded wood and leaves as mulch -- so I benefit from the nutrients; the garden is not totally organic, but I feel that I have done a little for the planet, and found my personal optimal solution. Now how can this process be written up as a journal paper?
Wednesday 20 August 2008
O.R. and the Infrastructure (3)
Another thought about the hidden science. In the U.K. (and I guess in many other countries) most traffic lights (whatever you call them) at road junctions are controlled by computer. Detectors are located close to the stop line and also in advance of that line, indicating the presence of vehicles waiting and approaching. (Next time you are cycling or walking past traffic lights, have a look for black tar-covered lines in the tarmacadam, which cover detector wires, or look for miniature radar sets on the lights themselves.) The logic behind the programs that control the lights is developed by O.R. scientists. In the programme about infrastructure "Britain from Above", the presenter visited a traffic control room, where the staff had the power to change lights when their traffic monitoring equipment (including TV cameras) detected congestion. It was left unsaid that most of the time the traffic flow is controlled automatically; the people in the control room had to deal with the exceptions, the unusual. Why can't the computer control be extended to cover these exceptions? Cost and complexity. It would cost too much to build in rules for exceptional cases, which would be complex. It is good O.R. (IMHO) to know when to stop building too complex a model. Besides, traffic control has multiple objectives, and the importance of the different objectives changes with the time of the day and much else.
O.R. and the Infrastructure (2)
I'm sure that I shall return to the expression that "O.R. is the hidden science" many times. In the T.V. presentation of "Britain from Above" already mentioned, the presenter observed part of the distribution chain that supplies shops and (especially) supermarkets. In a throwaway remark, he mentioned the 15-minute time windows for collections and deliveries at many stores. And these are part of the world of the O.R. scientist. There have been numerous papers on vehicle routing, and many software companies employ O.R. staff to provide tools for scheduling vehicles with time windows. It is a testimony to the success of O.R. that these hidden tools work, and so everyone can take them for granted! Yes, even the best systems can go wrong, but when was the last time that you couldn't buy an everyday item of food in your local supermarket?
Wednesday 13 August 2008
O.R. and the infrastructure
90 lengths today! (see yesterday's blog)
This week I watched the first episode in a new BBC TV series, Britain from Above. The opening programme focused on the infrastructure which lies behind life during a typical day in Britain. So there were mentions of transport, electricity supply, water supply and treatment, communications and so on. The filming was of a very high quality, and there were some good computer graphics, although quite often there was too little time to appreciate the message. The programme tended to move from topic to topic, trying to hold the viewer's attention -- and assumed a limited attention span.
I very much enjoyed reading Infrastructure: The Book of Everything for the Industrial Landscape which looks at the engineering behind a nation's infrastructure, and the BBC programme touched on this. But it also looked at aspects of control, and I wondered what would have happened if the presenter had been familiar with the work of O.R. scientists. For those who know how ubiquitous O.R. is, the programme emphasised that O.R. is the hidden science behind many things.
Sadly, for commercial reasons, much of the practical work of O.R. professionals in practice is never published. Why should you tell the world what you have done, and how you have done it, when your results could be exploited by your competitor?
To take one example from the programme, one that caught the attention of several commentators. At the end of the programme "Eastenders", there is a great surge in demand for electricity as well over a million kettles are switched on across Britain. The electricity industry has to cater for this demand, and we saw the man with the responsibility watching the demand rise, and bringing hydroelectric power stations "online" to cope with the demand. More power was bought from France. The same electricity industry uses O.R. to deal with the varying demand, with models (some of which are as simple as large linear programs) that show when diferent means of generating power should be used. When I talked about this with a researher in the industry, he spoke about the sudden demands for power during and at the end of TV programmes, and also of the difficulties that are created by having a cheap night-time tariff for electricity. Many users have timers which switch on appliances at the start of this tariff.
This week I watched the first episode in a new BBC TV series, Britain from Above. The opening programme focused on the infrastructure which lies behind life during a typical day in Britain. So there were mentions of transport, electricity supply, water supply and treatment, communications and so on. The filming was of a very high quality, and there were some good computer graphics, although quite often there was too little time to appreciate the message. The programme tended to move from topic to topic, trying to hold the viewer's attention -- and assumed a limited attention span.
I very much enjoyed reading Infrastructure: The Book of Everything for the Industrial Landscape which looks at the engineering behind a nation's infrastructure, and the BBC programme touched on this. But it also looked at aspects of control, and I wondered what would have happened if the presenter had been familiar with the work of O.R. scientists. For those who know how ubiquitous O.R. is, the programme emphasised that O.R. is the hidden science behind many things.
Sadly, for commercial reasons, much of the practical work of O.R. professionals in practice is never published. Why should you tell the world what you have done, and how you have done it, when your results could be exploited by your competitor?
To take one example from the programme, one that caught the attention of several commentators. At the end of the programme "Eastenders", there is a great surge in demand for electricity as well over a million kettles are switched on across Britain. The electricity industry has to cater for this demand, and we saw the man with the responsibility watching the demand rise, and bringing hydroelectric power stations "online" to cope with the demand. More power was bought from France. The same electricity industry uses O.R. to deal with the varying demand, with models (some of which are as simple as large linear programs) that show when diferent means of generating power should be used. When I talked about this with a researher in the industry, he spoke about the sudden demands for power during and at the end of TV programmes, and also of the difficulties that are created by having a cheap night-time tariff for electricity. Many users have timers which switch on appliances at the start of this tariff.
Tuesday 12 August 2008
Measurements in operational research
Today I swam for the hundredth time this year in the city swimming pool here in Exeter. It is a 25 metre pool, and -- according to my spreadsheet -- I have swum 8300 lengths this year. (I deliberately swam enough lengths today to make the total a round number, and the mean an integer.)
But, how far have I actually swum in that pool this year? I keep a spreadsheet for these swims, and that is updated every time I swim, so the number of swims in the city pool is reliable. I have swum once elsewhere, and we are discounting that five minute splash in a cold, open air pool. There are three obvious sources of error.
First, I may have miscounted. There is no mechanism for recording the lengths except my head, and I know that sometimes I lose track. But to counter this source of error, I usually swim with my wife, and she also counts lengths and we can generally verify the number of lengths each has swum (she is a little faster); I also know how fast I swim, so the clock on the wall of the pool gives a safeguard against gross error. As I swim an even number of lengths, my count is likely to be in error by \plusminus 2 if at all. I would hazard that I have made an error on at most 5 occasions. So the count of the lengths is 8300 \plusminus 10
Second, I do not always stay in the same lane of the pool, so I don't swim exactly the length of the pool. But simple geometry tells me that even if I swim slightly off straight, the difference between what I swim and the length of the pool is very small. We are talking about a variation of \plus 0.05% at most, say 4 lengths.
Third, I trust the pool builders to have measured correctly. But here is the most intriguing source of error. The pool is not exactly 25 metres. There is a tolerance because it was surveyed with measuring line when it was built. Everyone believes that it is exactly 25 metres, but the tolerance is probably \plusminus 150cm (6 inches) -- about 0.5%
So, all in all, I have not swum exactly 207.50 kilometres this year. The extreme range is
8294 * 0.024850 to 8314 * 0.025150 kilometres, i.e. (206.1 to 209.1) But that is the extreme range, and the confidence interval is smaller -- an exercise for the reader.
Why does this matter?
One, the Olympic Games are currently happening. How accurately are lengths of tracks and pools measured? The times of races are recorded very accurately, because we are very good at recording time. But how much tolerance is there in the distances?
Secondly, for O.R. professionals, how often do we believe in spurious accuracy of data? When I learnt about L.P. in the oil industry, we were told a cautionary tale, of the analysts who checked their data; one measurement of viscosity of crude oil was always given as an integer, a small integer. This was then processed through the L.P. model. Where did this value come from, they asked. As one should, they checked. The data was supplied by an experienced worker, who dipped his thumb and forefinger into the crude oil, rubbed them together, and pronounced the measurement. Now, far too often, I see papers submitted for journals where there are tables of results quoted to six or more significant figures. Where did these come from? Usually from the analysis of a few dozen observations that were each measured to two or three significant figures. The best models in the world cannot conjure more accuracy from the model than was in the source data; but all too often we forget that, at our peril.
The results are only as good as the data.
But, how far have I actually swum in that pool this year? I keep a spreadsheet for these swims, and that is updated every time I swim, so the number of swims in the city pool is reliable. I have swum once elsewhere, and we are discounting that five minute splash in a cold, open air pool. There are three obvious sources of error.
First, I may have miscounted. There is no mechanism for recording the lengths except my head, and I know that sometimes I lose track. But to counter this source of error, I usually swim with my wife, and she also counts lengths and we can generally verify the number of lengths each has swum (she is a little faster); I also know how fast I swim, so the clock on the wall of the pool gives a safeguard against gross error. As I swim an even number of lengths, my count is likely to be in error by \plusminus 2 if at all. I would hazard that I have made an error on at most 5 occasions. So the count of the lengths is 8300 \plusminus 10
Second, I do not always stay in the same lane of the pool, so I don't swim exactly the length of the pool. But simple geometry tells me that even if I swim slightly off straight, the difference between what I swim and the length of the pool is very small. We are talking about a variation of \plus 0.05% at most, say 4 lengths.
Third, I trust the pool builders to have measured correctly. But here is the most intriguing source of error. The pool is not exactly 25 metres. There is a tolerance because it was surveyed with measuring line when it was built. Everyone believes that it is exactly 25 metres, but the tolerance is probably \plusminus 150cm (6 inches) -- about 0.5%
So, all in all, I have not swum exactly 207.50 kilometres this year. The extreme range is
8294 * 0.024850 to 8314 * 0.025150 kilometres, i.e. (206.1 to 209.1) But that is the extreme range, and the confidence interval is smaller -- an exercise for the reader.
Why does this matter?
One, the Olympic Games are currently happening. How accurately are lengths of tracks and pools measured? The times of races are recorded very accurately, because we are very good at recording time. But how much tolerance is there in the distances?
Secondly, for O.R. professionals, how often do we believe in spurious accuracy of data? When I learnt about L.P. in the oil industry, we were told a cautionary tale, of the analysts who checked their data; one measurement of viscosity of crude oil was always given as an integer, a small integer. This was then processed through the L.P. model. Where did this value come from, they asked. As one should, they checked. The data was supplied by an experienced worker, who dipped his thumb and forefinger into the crude oil, rubbed them together, and pronounced the measurement. Now, far too often, I see papers submitted for journals where there are tables of results quoted to six or more significant figures. Where did these come from? Usually from the analysis of a few dozen observations that were each measured to two or three significant figures. The best models in the world cannot conjure more accuracy from the model than was in the source data; but all too often we forget that, at our peril.
The results are only as good as the data.
Wednesday 23 July 2008
IFORS 2008 in Sandton South Africa
I have spent the first two and a half weeks of July in South Africa. The main reason for going there was to attend the conference of the International Federation of Operational Research Societies (IFORS from now on) which was held in Sandton, a suburb of Johannesburg (Jo'burg) from Sunday 13th July to Friday 18th July. These IFORS conferences are held every three years, and this was the 18th of them -- and the sixth that I have attended. The conference also marked the 50th anniversary of IFORS. And it was the first time the conference has been held in Africa.
Mike Trick was there and he is also creating a blog (http://mat.tepper.cmu.edu/blog/?p=297) so I won't repeat what he has already written.
The conference facilities were pretty good; one new feature for me was that the computers for presentations were set up with folders for each session and speaker, so the team of students who were "go-fers" could download your presentation from a memory stick in advance and you would know where to find it. (How different from the days when one travelled with a wallet of overhead transparencies! Mind you, when I went to one conference in Jerusalem, the airport security inspector demanded that I produce my slides to him before I went to the aircraft to demonstrate that I was genuine; I wonder what would he do now?)
But I often tend to look at things with an OR professional's eye, and the convention centre had room for improvement ("Science of better")
(1) The design was weird -- access to upper floors was generally by escalators, and these were at the xtreme sides of the ground floor foyer -- not very convenient.
(2) There wasn't an obvious channel for feedback when things went wrong for participants -- if a light-bulb needs to be replaced, who do you tell? There were numerous staff around, but they did not have identifiable roles.
(3) We had a splendid banquet, but the main course and dessert were buffet style, even though there was a surfeit of waiting staff. 600 people had to negotiate their way around the tables to the buffet ... not easy.
(4) The conference organisers had a desk for queries during the conference -- a classicly designed bad queue led to this; the number of servers was uncertain, as some people came to the desk and then went away again, and the users (Customers) formed an indeterminate number of lines to the desk, and jockeyed. Service times were extremely variable -- a rope barrier and one line of users would have been much better.
More another day!
Mike Trick was there and he is also creating a blog (http://mat.tepper.cmu.edu/blog/?p=297) so I won't repeat what he has already written.
The conference facilities were pretty good; one new feature for me was that the computers for presentations were set up with folders for each session and speaker, so the team of students who were "go-fers" could download your presentation from a memory stick in advance and you would know where to find it. (How different from the days when one travelled with a wallet of overhead transparencies! Mind you, when I went to one conference in Jerusalem, the airport security inspector demanded that I produce my slides to him before I went to the aircraft to demonstrate that I was genuine; I wonder what would he do now?)
But I often tend to look at things with an OR professional's eye, and the convention centre had room for improvement ("Science of better")
(1) The design was weird -- access to upper floors was generally by escalators, and these were at the xtreme sides of the ground floor foyer -- not very convenient.
(2) There wasn't an obvious channel for feedback when things went wrong for participants -- if a light-bulb needs to be replaced, who do you tell? There were numerous staff around, but they did not have identifiable roles.
(3) We had a splendid banquet, but the main course and dessert were buffet style, even though there was a surfeit of waiting staff. 600 people had to negotiate their way around the tables to the buffet ... not easy.
(4) The conference organisers had a desk for queries during the conference -- a classicly designed bad queue led to this; the number of servers was uncertain, as some people came to the desk and then went away again, and the users (Customers) formed an indeterminate number of lines to the desk, and jockeyed. Service times were extremely variable -- a rope barrier and one line of users would have been much better.
More another day!
Tuesday 17 June 2008
Guesstimating
I recently read a review of the book:
Guesstimation: Solving the World's Problems on the Back of a Cocktail Napkin by Lawrence Weinstein & John A. Adam
The authors encourage their readers (and their students) to have a feel for the size of numbers, and developing the skill of estimating reasonably accurately the scale or size of some measurable event or situation. The publisher's website for the book gives some examples, as well as a pdf of the first chapter. Something in the latter intrigued me. Suppose that there is a lottery with a hundred million tickets. If all those tickets were piled high, what would the height be?
In the UK, there is a National Lottery with about fourteen million different entry tickets. Like many university lecturers, I have used it for examples of simple (and not so simple) probability and statistics. So I started to wonder how high the pile of cards would be for the UK National Lottery. Following Weinstein and Adam, you start by thinking how thick a ticket would be, and conclude it is somewhere between 0.1 mm and 0.2 mm (a pack of 500 sheets of paper for the computer printer is 5cm thick, and lottery tickets are thicker). If we work with the smaller figure, we are talking of a stack 1,400,000 mm high, or 1,400 metres, or 1.4 kilometres. The figure is more than this, but less than twice, so we may as well call it 2km. Now we have a sense of the small probability of winning. 2km is higher than the highest mountain in the UK. Put that stack down, along a straight road; now it will take 20 minutes to walk from one end to the other, with just one card being the winner.
Guesstimating the size of things has more serious applications than this, but I am pleased to see that a publisher thinks it is worth putting a book like this in the marketplace. O.R. people use guesstimates quite often, to get a feel for the rightness of an answer, or a feel for the problem. In the early days of O.R., in the UK in the second world war, Winston Churchill heard of a ship crossing the Atlantic with a load of dried egg and asked one of his scientific advisers to estimate from the tonnage of the ship how many eggs were in the ship. The serious business of war was held up while the adviser worked it out, on the back of an envelope. Weinstein and Adam would be proud!
Guesstimation: Solving the World's Problems on the Back of a Cocktail Napkin by Lawrence Weinstein & John A. Adam
The authors encourage their readers (and their students) to have a feel for the size of numbers, and developing the skill of estimating reasonably accurately the scale or size of some measurable event or situation. The publisher's website for the book gives some examples, as well as a pdf of the first chapter. Something in the latter intrigued me. Suppose that there is a lottery with a hundred million tickets. If all those tickets were piled high, what would the height be?
In the UK, there is a National Lottery with about fourteen million different entry tickets. Like many university lecturers, I have used it for examples of simple (and not so simple) probability and statistics. So I started to wonder how high the pile of cards would be for the UK National Lottery. Following Weinstein and Adam, you start by thinking how thick a ticket would be, and conclude it is somewhere between 0.1 mm and 0.2 mm (a pack of 500 sheets of paper for the computer printer is 5cm thick, and lottery tickets are thicker). If we work with the smaller figure, we are talking of a stack 1,400,000 mm high, or 1,400 metres, or 1.4 kilometres. The figure is more than this, but less than twice, so we may as well call it 2km. Now we have a sense of the small probability of winning. 2km is higher than the highest mountain in the UK. Put that stack down, along a straight road; now it will take 20 minutes to walk from one end to the other, with just one card being the winner.
Guesstimating the size of things has more serious applications than this, but I am pleased to see that a publisher thinks it is worth putting a book like this in the marketplace. O.R. people use guesstimates quite often, to get a feel for the rightness of an answer, or a feel for the problem. In the early days of O.R., in the UK in the second world war, Winston Churchill heard of a ship crossing the Atlantic with a load of dried egg and asked one of his scientific advisers to estimate from the tonnage of the ship how many eggs were in the ship. The serious business of war was held up while the adviser worked it out, on the back of an envelope. Weinstein and Adam would be proud!
Monday 16 June 2008
Multicriteria cities
According to a survey that seems to have been flashed around the world like a viral email, Copenhagen is the "best" place to live in 2008. The magazine "Monocle" (a "Lifestyle magazine" which is not in the journals abstracted for IAOR) took measurement on several criteria, weighted them and came up with a ranking which placed the Danish capital at number 1.
Operational researchers are familiar with problems of multiple criteria measurement. The cynical O.R. person will mutter about adding apples to oranges and trying to work out what the result is. Everyone will have their views on the best place to live, and what makes it so good. And that list will almost certainly not coincide exactly with the criteria used by the magazine. Let me admit that I like Copenhagen, perhaps because my late friend Ellen had a flat which was ten minutes walk from the gates of Tivoli Gardens, and so could hardly have been more convenient for visiting the place. Even without that personal experience, it is a very pleasant city, but my criteria would not have included (for instance) Monocle's number of international flights from the city airport, nor the ease of buying drinks at 1a.m..
So, seeing such analysis of multiple criteria optimisation, the O.R. person ought to reflect on how difficult it is to measure the "hard to measure" and on how to work with clients and decision-makers when some of the consequences of choice are determined by aesthetic and qualitative scales.
Operational researchers are familiar with problems of multiple criteria measurement. The cynical O.R. person will mutter about adding apples to oranges and trying to work out what the result is. Everyone will have their views on the best place to live, and what makes it so good. And that list will almost certainly not coincide exactly with the criteria used by the magazine. Let me admit that I like Copenhagen, perhaps because my late friend Ellen had a flat which was ten minutes walk from the gates of Tivoli Gardens, and so could hardly have been more convenient for visiting the place. Even without that personal experience, it is a very pleasant city, but my criteria would not have included (for instance) Monocle's number of international flights from the city airport, nor the ease of buying drinks at 1a.m..
So, seeing such analysis of multiple criteria optimisation, the O.R. person ought to reflect on how difficult it is to measure the "hard to measure" and on how to work with clients and decision-makers when some of the consequences of choice are determined by aesthetic and qualitative scales.
Tuesday 10 June 2008
Governments, swimming pools and models
The UK government has announced that it intends to subsidise swimming pools in England, with the aim of making entry free of charge to all the pools which are managed by councils. The subsidy will be introduced gradually, starting with the over-60s and under-16s. By 2012, everyone will be able to use public swimming pools free of charge. This excludes pools owned by companies, sports clubs, hotels and educational establishments. This is to try and encourage more people to take part in sport, and it is claimed that the most likely form of exercise for people to take up is swimming. There will also be money for new Olympic sized swimming pools.
Now I enjoy swimming, and go to the pool several times each week. When I started work at the university, one of the free perks of the job was being able to stroll to the open-air pool that was five minutes walk away from my office, and swim. The pool was free for staff and students. Now there is a charge, and I have moved my regular swimming to the public pool managed by Exeter city council. But, even though the pool was free, it didn't mean that everyone used it. Removing the charge for some goods or service doesn't automatically bring in more customers.
So I wonder what kind of modelling has been done by the UK government in advance of this announcement. The claim is that it will bring two million more people into regular exercise. As an O.R. person, I wonder what model yielded that figure, about 3% of the UK population. And how do you really measure "regular"? If the figure is accurate, what does it mean for the numbers of people using a typical swimming pool on a typical day? Most pools have lane swimming for serious swimming. Before 9am, Exeter's pool has two "fast" lanes, one "medium" and one "slow". The fast lanes are crowded when there are six or seven people in each, the medium one can take a few more, and swimming in the slow one is awkward when there are 20 in it. Can you recognise a queueing problem here? When does the congestion in a service system get so bad that arrivals turn away?
Swimming pools provide several further O.R. related questions. I used to ask one of my modelling classes how big the hot water tank that feeds the showers should be for a set of public showers. For simplicity, these showers often have no control over temperature, simply an on-off button or tap. So the water temperature cannot fluctuate too much. Therefore, the heating system must be able to maintain the water temperature within a small range, putting design limitations on it.
Another problem comes with lane swimming. There is a heuristic which says that it is safer if alternate lanes go in opposite senses, clockwise, anti-clockwise, clockwise ... across the pool. Why? Because adjacent lanes are swimming together, and a swimmer only needs to avoid those coming towards themselves on one side, not two. But overtaking in lane swimming is an art, which leads to models of congestion. Assuming that I am two metres tall, then if I make a turn after the person in front of me, then to overtake them, I need to swim an extra two metres in the time that it takes for us both to complete a length -- unless they give way. So you need to be in the region of 10% faster than the person ahead to complete overtaking in a normal pool. And if there is a third person behind, then that person will see congestion. It is rather like two similar speed trucks overtaking on a two or three lane road -- it takes time and there are people held up behind. Swimming has the complication of turning at the ends of the pool. But there's a research possibility: "The similarities and differences of lane swimming and overtaking trucks." You read it here first!
Now I enjoy swimming, and go to the pool several times each week. When I started work at the university, one of the free perks of the job was being able to stroll to the open-air pool that was five minutes walk away from my office, and swim. The pool was free for staff and students. Now there is a charge, and I have moved my regular swimming to the public pool managed by Exeter city council. But, even though the pool was free, it didn't mean that everyone used it. Removing the charge for some goods or service doesn't automatically bring in more customers.
So I wonder what kind of modelling has been done by the UK government in advance of this announcement. The claim is that it will bring two million more people into regular exercise. As an O.R. person, I wonder what model yielded that figure, about 3% of the UK population. And how do you really measure "regular"? If the figure is accurate, what does it mean for the numbers of people using a typical swimming pool on a typical day? Most pools have lane swimming for serious swimming. Before 9am, Exeter's pool has two "fast" lanes, one "medium" and one "slow". The fast lanes are crowded when there are six or seven people in each, the medium one can take a few more, and swimming in the slow one is awkward when there are 20 in it. Can you recognise a queueing problem here? When does the congestion in a service system get so bad that arrivals turn away?
Swimming pools provide several further O.R. related questions. I used to ask one of my modelling classes how big the hot water tank that feeds the showers should be for a set of public showers. For simplicity, these showers often have no control over temperature, simply an on-off button or tap. So the water temperature cannot fluctuate too much. Therefore, the heating system must be able to maintain the water temperature within a small range, putting design limitations on it.
Another problem comes with lane swimming. There is a heuristic which says that it is safer if alternate lanes go in opposite senses, clockwise, anti-clockwise, clockwise ... across the pool. Why? Because adjacent lanes are swimming together, and a swimmer only needs to avoid those coming towards themselves on one side, not two. But overtaking in lane swimming is an art, which leads to models of congestion. Assuming that I am two metres tall, then if I make a turn after the person in front of me, then to overtake them, I need to swim an extra two metres in the time that it takes for us both to complete a length -- unless they give way. So you need to be in the region of 10% faster than the person ahead to complete overtaking in a normal pool. And if there is a third person behind, then that person will see congestion. It is rather like two similar speed trucks overtaking on a two or three lane road -- it takes time and there are people held up behind. Swimming has the complication of turning at the ends of the pool. But there's a research possibility: "The similarities and differences of lane swimming and overtaking trucks." You read it here first!
Tuesday 27 May 2008
Coming into OR
I realise that I came into OR at an interesting time. I had studied mathematics as an undergraduate, and wondered what to do with the degree. (Both my parents had mathematics degrees; father was a scientific civil servant working with radar, mother had been a teacher; but neither career appealed to me.) A helpful careers advisor took me through some of the options, based on what I had told him of myself, and I duly applied -- and was accepted -- onto a one year postgraduate course in OR. At the time (early 1970's) there were still many of the pioneers of OR in UK industry and universities still around, and there was a good buzz of meetings and new ideas. The one-year course exposed me to the theory, but far more important, the philosophy of OR. I stayed on to do research, and then joined the staff at the University of Exeter where I have been ever since.
It was a pioneering time in Exeter, setting up an undergraduate programme in "Mathematical Statistics and OR" (MSOR for short) and we had some stimulating years with annual cohorts of 20 to 30 students, who wanted to "do something with their mathematical skills, but not a mathematics degree".
We developed links with industry and ran some fascinating projects; maybe more of these later, when I have time.
My postgraduate work centred on the water supply industry, and we encountered a problem which (like the supermarket cashiers problem) is simple to state, but leads to more complexity as one gets into it. A water supply reservoir has many purposes. First, to store water to supply the users. For that it ought to be full. Second, to restrain floods. For that it needs to have space in it, and not be full. Third, to provide recreation. For that, the level should not fluctuate much, under normal circumstances. So what should the reservoir manager's policy be about releasing water, both in the short term (when floods are imminent) and in the long term, when the weather is calm. How can forecasts help? The problem was nicknamed the "Noah and Joseph" problem, by reference to Noah who encountered floods (a short-term phenomenon) and Joseph who dealt with droughts (long-term).
It was a pioneering time in Exeter, setting up an undergraduate programme in "Mathematical Statistics and OR" (MSOR for short) and we had some stimulating years with annual cohorts of 20 to 30 students, who wanted to "do something with their mathematical skills, but not a mathematics degree".
We developed links with industry and ran some fascinating projects; maybe more of these later, when I have time.
My postgraduate work centred on the water supply industry, and we encountered a problem which (like the supermarket cashiers problem) is simple to state, but leads to more complexity as one gets into it. A water supply reservoir has many purposes. First, to store water to supply the users. For that it ought to be full. Second, to restrain floods. For that it needs to have space in it, and not be full. Third, to provide recreation. For that, the level should not fluctuate much, under normal circumstances. So what should the reservoir manager's policy be about releasing water, both in the short term (when floods are imminent) and in the long term, when the weather is calm. How can forecasts help? The problem was nicknamed the "Noah and Joseph" problem, by reference to Noah who encountered floods (a short-term phenomenon) and Joseph who dealt with droughts (long-term).
Introducing myself and OR
This is the first item in the Blog of the IAOR editor. So it is a place to do some introductions. I am David Smith and one of my responsibilities is to edit IAOR, the International Abstracts in Operations Research. As I shall often refer to OR, I'd better explain. OR is the abbreviation for Operations Research or Operational Research, depending on where in the world you live. There are many places which define OR, so I will not waste space describing the subject in detail, but simply give one illustration.
From time to time, people ask me what I do.
First answer: "I work at the university".
Some people change the subject; others ask: "What subject?"
Second answer: "I teach a branch of mathematics."
More people change the subject, or admit that they did not get on with mathematics; however, some ask a little more.
So I explain that OR is not really a branch of mathematics, but a subject in its own right, which uses mathematics and other ideas to answer questions for business and commerce, either "What's best?" or "What happens if ...?" And my standard, simple illustration is the local supermarket, and the number of cashiers on duty. The best number is somewhere between too small and too many; too few, and the queues get big, and the customers start to go to another store; too many, and there are no queues, but the cashiers are not working fully. So there must be a "Right number" -- the problem is mathematical. But the number depends on the time of day, day of the week, month of the year. So you need to forecast the number of customers who will shop on different days at different times. More mathematics. And you need to devise a shift system for the staff of the store so that the full-time and part-time employees have regular work patterns. More mathematics (or OR!) So what started as a simple question, "How many?", has become a much larger problem for the company.
Having explained that, my audience starts to realise that OR is useful in their world, and I can recount other applications that often surprise and fascinate them.
I meant to introduce myself, but it has turned into an introduction to explaining OR to my dinner guests.
From time to time, people ask me what I do.
First answer: "I work at the university".
Some people change the subject; others ask: "What subject?"
Second answer: "I teach a branch of mathematics."
More people change the subject, or admit that they did not get on with mathematics; however, some ask a little more.
So I explain that OR is not really a branch of mathematics, but a subject in its own right, which uses mathematics and other ideas to answer questions for business and commerce, either "What's best?" or "What happens if ...?" And my standard, simple illustration is the local supermarket, and the number of cashiers on duty. The best number is somewhere between too small and too many; too few, and the queues get big, and the customers start to go to another store; too many, and there are no queues, but the cashiers are not working fully. So there must be a "Right number" -- the problem is mathematical. But the number depends on the time of day, day of the week, month of the year. So you need to forecast the number of customers who will shop on different days at different times. More mathematics. And you need to devise a shift system for the staff of the store so that the full-time and part-time employees have regular work patterns. More mathematics (or OR!) So what started as a simple question, "How many?", has become a much larger problem for the company.
Having explained that, my audience starts to realise that OR is useful in their world, and I can recount other applications that often surprise and fascinate them.
I meant to introduce myself, but it has turned into an introduction to explaining OR to my dinner guests.
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