Comments for Operations Research Forum http://orforum.blog.informs.org Sun, 20 Jul 2008 00:11:40 +0000 http://wordpress.org/?v=wordpress-mu-1.1.1 Comment on The OR/MS Ecosystem: Strengths, Weaknesses, Opportunities and Threats by Parasuram Balasubramanian http://orforum.blog.informs.org/2008/04/23/the-orms-ecosystem-strengths-weaknesses-opportunities-and-threats/#comment-7811 Parasuram Balasubramanian Tue, 27 May 2008 21:32:54 +0000 http://orforum.blog.informs.org/2008/04/23/the-orms-ecosystem-strengths-weaknesses-opportunities-and-threats/#comment-7811 This pertains to the article titled OR/MS Ecosystem( Apr 2008 issue of Operations Research). The authors have articulated the concerns about the future of O.R. well. Yet it has been presented with an academic perspective. Pl. see Chapter 26 of Handbook of OR/MS ( Ed: Ravindran) published by CRC press in Dec 2007. The chapter is on " Future of O.R. A Practitioner's Perspective". The undersigned has written this chapter based on my three decades of industry experience. The misfit between the role of O.R.professionals vis a vis the functional executives, the gap in skills relating to leading a multidisciplinary team, inability to resolve the unit versus corporate goals etc have been identified as the reason for declining popularity of OR in industry. But the article also points to the path for a bright future for OR professionals by choosing to return to the founding concepts; by adopting and leveraging internet and data base technologies; by extending the reach of Analytics to the dilemmas of future and by developing applications in bio/life sciences and other newer technologies. regards Parasuram Balasubramanian Founder & CEO Theme Work Analytics 3000, Kent Avenue West Lafayette, In 47906 balasubp@gmail.com This pertains to the article titled OR/MS Ecosystem( Apr 2008 issue of Operations Research).

The authors have articulated the concerns about the future of O.R. well. Yet it has been presented with an academic perspective.

Pl. see Chapter 26 of Handbook of OR/MS ( Ed: Ravindran) published by CRC press in Dec 2007. The chapter is on ” Future of O.R. A Practitioner’s Perspective”. The undersigned has written this chapter based on my three decades of industry experience.

The misfit between the role of O.R.professionals vis a vis the functional executives, the gap in skills relating to leading a multidisciplinary team, inability to resolve the unit versus corporate goals etc have been identified as the reason for declining popularity of OR in industry.

But the article also points to the path for a bright future for OR professionals by choosing to return to the founding concepts; by adopting and leveraging internet and data base technologies; by extending the reach of Analytics to the dilemmas of future and by developing applications in bio/life sciences and other newer technologies.

regards

Parasuram Balasubramanian
Founder & CEO
Theme Work Analytics
3000, Kent Avenue
West Lafayette, In 47906
balasubp@gmail.com

]]> Comment on The OR/MS Ecosystem: Strengths, Weaknesses, Opportunities and Threats by Jon Caulkins http://orforum.blog.informs.org/2008/04/23/the-orms-ecosystem-strengths-weaknesses-opportunities-and-threats/#comment-7600 Jon Caulkins Thu, 22 May 2008 13:34:40 +0000 http://orforum.blog.informs.org/2008/04/23/the-orms-ecosystem-strengths-weaknesses-opportunities-and-threats/#comment-7600 One bit of evidence concerning the gap between INFORMS and practitioners is that we use the term "practitioners" as if it referred to a homogeneous group. I don't know what the best typology is, but suspect there are at least three groups: 1) Practitioners who use relatively sophisticated OR models to embed OR within ongoing operational decision making. (I believe yield management and crew scheduling at airlines would be examples.) 2) OR analysis that informs strategic or executive decision making. 3) Run of the mill, generic managerial decision making. (Which applicant to hire, what price to charge, how much to budget for an activity, etc.) My sense is that the profession does best at supporting the first type of practitioner. We (sometimes grudgingly) acknowledge that most managerial decision making is not of that sort, so we talk about moving OR up within the organizational hierarchy to inform strategic decision making. However, I wonder if in doing so we underestimate the collective importance of injecting a little more quantitative analysis into the very large number of routine decisions, including the possibility that 30 year olds who find OR useful for routine decisions might be more likely to call upon OR 20 years later when they are in the executive suite. I don't think any other discipline has claimed the intellectual space of scientific study (e.g., with lab experiments and field studies) of what is the best way to improve routine decision making by calling upon math and quantitative analysis. We invent tools and methods. For "high-end" applications we compete them against each other (e.g., documenting improvements in computational running time), but we do less of this for methods that support routine decision making. For example, our journals do not often report empirical evidence about the relative effectiveness of different approaches (e.g., teaching soft systems vs. spreadsheet modeling vs. traditional OR tools textbook or something yet to be invented) at improving routine decision making. Doing so might (1) bring us closer to practice, (2) reduce pressures to be "too mathematical", and (3) preserve OR's generality, since routine decision making is relevant for managers in marketing and production, business and non-profit, etc. One bit of evidence concerning the gap between INFORMS and practitioners is that we use the term “practitioners” as if it referred to a homogeneous group. I don’t know what the best typology is, but suspect there are at least three groups:
1) Practitioners who use relatively sophisticated OR models to embed OR within ongoing operational decision making. (I believe yield management and crew scheduling at airlines would be examples.)
2) OR analysis that informs strategic or executive decision making.
3) Run of the mill, generic managerial decision making. (Which applicant to hire, what price to charge, how much to budget for an activity, etc.)
My sense is that the profession does best at supporting the first type of practitioner. We (sometimes grudgingly) acknowledge that most managerial decision making is not of that sort, so we talk about moving OR up within the organizational hierarchy to inform strategic decision making. However, I wonder if in doing so we underestimate the collective importance of injecting a little more quantitative analysis into the very large number of routine decisions, including the possibility that 30 year olds who find OR useful for routine decisions might be more likely to call upon OR 20 years later when they are in the executive suite.
I don’t think any other discipline has claimed the intellectual space of scientific study (e.g., with lab experiments and field studies) of what is the best way to improve routine decision making by calling upon math and quantitative analysis. We invent tools and methods. For “high-end” applications we compete them against each other (e.g., documenting improvements in computational running time), but we do less of this for methods that support routine decision making. For example, our journals do not often report empirical evidence about the relative effectiveness of different approaches (e.g., teaching soft systems vs. spreadsheet modeling vs. traditional OR tools textbook or something yet to be invented) at improving routine decision making.
Doing so might (1) bring us closer to practice, (2) reduce pressures to be “too mathematical”, and (3) preserve OR’s generality, since routine decision making is relevant for managers in marketing and production, business and non-profit, etc.

]]> Comment on The OR/MS Ecosystem: Strengths, Weaknesses, Opportunities and Threats by Paul Rubin http://orforum.blog.informs.org/2008/04/23/the-orms-ecosystem-strengths-weaknesses-opportunities-and-threats/#comment-6376 Paul Rubin Tue, 29 Apr 2008 01:17:51 +0000 http://orforum.blog.informs.org/2008/04/23/the-orms-ecosystem-strengths-weaknesses-opportunities-and-threats/#comment-6376 Those of us academics not particularly engaged in consulting are fairly well sheltered from real-world problems. Meanwhile, I believe it is generally agreed that there is a substantial population of potential end-users who do no recognize that OR methods might be fruitfully applied to their problems (and often have never heard of OR). Occasionally someone from the general public pops up on sci.op-research describing a problem and asking if anyone can classify it or suggest approaches, but that is rather rare. Perhaps if we could create and publicize a forum where anyone could post general descriptions of problems (should I say "messes"?) and members of the OR community could offer suggestions, we might bridge the gap a bit? Or is this already being done somewhere? Those of us academics not particularly engaged in consulting are fairly well sheltered from real-world problems. Meanwhile, I believe it is generally agreed that there is a substantial population of potential end-users who do no recognize that OR methods might be fruitfully applied to their problems (and often have never heard of OR). Occasionally someone from the general public pops up on sci.op-research describing a problem and asking if anyone can classify it or suggest approaches, but that is rather rare. Perhaps if we could create and publicize a forum where anyone could post general descriptions of problems (should I say “messes”?) and members of the OR community could offer suggestions, we might bridge the gap a bit? Or is this already being done somewhere?

]]> Comment on The OR/MS Ecosystem: Strengths, Weaknesses, Opportunities and Threats by Michael Trick http://orforum.blog.informs.org/2008/04/23/the-orms-ecosystem-strengths-weaknesses-opportunities-and-threats/#comment-6147 Michael Trick Thu, 24 Apr 2008 21:50:47 +0000 http://orforum.blog.informs.org/2008/04/23/the-orms-ecosystem-strengths-weaknesses-opportunities-and-threats/#comment-6147 My post announcing the paper has spawned some discussion at the USENET group sci.op-research . See that discussion <a HREF="http://groups.google.com/group/sci.op-research/browse_thread/thread/565a7154b669a702" rel="nofollow">here</A> but it would be great to see more discussion here. Logs say 200 people have downloaded the papers and commentaries today alone, so there are lots of people interested in this topic! My post announcing the paper has spawned some discussion at the USENET group sci.op-research . See that discussion here but it would be great to see more discussion here. Logs say 200 people have downloaded the papers and commentaries today alone, so there are lots of people interested in this topic!

]]> Comment on The OR/MS Ecosystem: Strengths, Weaknesses, Opportunities and Threats by Brenda Dietrich http://orforum.blog.informs.org/2008/04/23/the-orms-ecosystem-strengths-weaknesses-opportunities-and-threats/#comment-6136 Brenda Dietrich Thu, 24 Apr 2008 14:52:03 +0000 http://orforum.blog.informs.org/2008/04/23/the-orms-ecosystem-strengths-weaknesses-opportunities-and-threats/#comment-6136 The SWOT article observations regarding the typical content of OR/MS publications are consistent with my own observations and experiences regarding the INFORMS journals’ acceptance philosophy. But OR practitioners in industry and government also face obstacles related to the publication process, in which reviewers, even when finding the material to be new and technically correct, seem to feel obligated to insist that the paper be in some way expanded to include additional literature review, additional computational experiments, or consideration of alternate model assumptions. This feedback may well result in a better paper, but in the project-based model used in much or industrial OR, it acting on the recommendations is simply not feasible. Time to write a scholarly paper is not usually included in an industrial OR project plan; it’s just not something most clients are willing to pay for. Thus, when a project is completed, the team moves on to new work. A few ambitious team members may use their non-work hours to draft a paper describing the work, typically including a description of the business problem being addressed, a brief survey of some relevant literature, a model formulation, a description of one or more algorithms, some discussion of computational experience, and a discussion or the deployment and its impact. This draft then has to be reviewed and sometimes redacted by the project team and by management within both the performing and receiving organizations. By the time the paper is submitted for publication the project has been over for several months. The review process, on average, takes several additional months. Thus by the time the reviews suggesting extensive additional work are received by the authors, a year or more has elapsed since project completion. Some of the suggestions, such as inclusion of more recent references, may simply require a few addition hours of the author’s time. Others, such as additional computational experiments (especially with alternate software packages) or consideration of alternate model assumptions or data sets, essentially define a new project – a project with no funding or clear return on investment. It is highly unlikely that these suggestions will be addressed by the non-academic authors. Having never worked in academia, I don’t know how such requests are dealt with by academic authors, but my sense is that the additional work often becomes part of a student’s research project. A not uncommon response by industrial authors to such suggestions is to file the paper and the reports in either the bottom drawer (or worse) and return to work on the next project. After a few iterations of this process, may practitioners simply stop trying to publish their work in INFORMS journals. Even the INFORMS journal Interfaces, “dedicated to improving the practical application of OR/MS to decisions and policies in today’s organizations and industries” has an editorial board that is dominated by academics. A quick review of recent issues (outside of the Edelman issue) revealed that a significant percentage of articles have at least one academic co-author. While I applaud the collaboration evidenced by such papers, the lack of reporting on how OR is actually used and at times misused in industry, and the absence of discussion of quantitative business problems that are not yet well addressed by available OR tools seems to indicate a weak link in our profession. The SWOT article observations regarding the typical content of OR/MS publications are consistent with my own observations and experiences regarding the INFORMS journals’ acceptance philosophy. But OR practitioners in industry and government also face obstacles related to the publication process, in which reviewers, even when finding the material to be new and technically correct, seem to feel obligated to insist that the paper be in some way expanded to include additional literature review, additional computational experiments, or consideration of alternate model assumptions. This feedback may well result in a better paper, but in the project-based model used in much or industrial OR, it acting on the recommendations is simply not feasible.

Time to write a scholarly paper is not usually included in an industrial OR project plan; it’s just not something most clients are willing to pay for. Thus, when a project is completed, the team moves on to new work. A few ambitious team members may use their non-work hours to draft a paper describing the work, typically including a description of the business problem being addressed, a brief survey of some relevant literature, a model formulation, a description of one or more algorithms, some discussion of computational experience, and a discussion or the deployment and its impact. This draft then has to be reviewed and sometimes redacted by the project team and by management within both the performing and receiving organizations. By the time the paper is submitted for publication the project has been over for several months.

The review process, on average, takes several additional months. Thus by the time the reviews suggesting extensive additional work are received by the authors, a year or more has elapsed since project completion. Some of the suggestions, such as inclusion of more recent references, may simply require a few addition hours of the author’s time. Others, such as additional computational experiments (especially with alternate software packages) or consideration of alternate model assumptions or data sets, essentially define a new project – a project with no funding or clear return on investment. It is highly unlikely that these suggestions will be addressed by the non-academic authors. Having never worked in academia, I don’t know how such requests are dealt with by academic authors, but my sense is that the additional work often becomes part of a student’s research project.

A not uncommon response by industrial authors to such suggestions is to file the paper and the reports in either the bottom drawer (or worse) and return to work on the next project. After a few iterations of this process, may practitioners simply stop trying to publish their work in INFORMS journals.

Even the INFORMS journal Interfaces, “dedicated to improving the practical application of OR/MS to decisions and policies in today’s organizations and industries” has an editorial board that is dominated by academics. A quick review of recent issues (outside of the Edelman issue) revealed that a significant percentage of articles have at least one academic co-author. While I applaud the collaboration evidenced by such papers, the lack of reporting on how OR is actually used and at times misused in industry, and the absence of discussion of quantitative business problems that are not yet well addressed by available OR tools seems to indicate a weak link in our profession.

]]> Comment on The OR/MS Ecosystem: Strengths, Weaknesses, Opportunities and Threats by Michael Trick http://orforum.blog.informs.org/2008/04/23/the-orms-ecosystem-strengths-weaknesses-opportunities-and-threats/#comment-6131 Michael Trick Thu, 24 Apr 2008 13:19:53 +0000 http://orforum.blog.informs.org/2008/04/23/the-orms-ecosystem-strengths-weaknesses-opportunities-and-threats/#comment-6131 We very much would like this to be a conversation, so please feel free to add your thoughts about the paper, the commentaries, and the issues they raise. We very much would like this to be a conversation, so please feel free to add your thoughts about the paper, the commentaries, and the issues they raise.

]]> Comment on The OR/MS Ecosystem: Strengths, Weaknesses, Opportunities and Threats by Michael Trick’s Operations Research Blog » OR Forum on SWOT for Operations Research http://orforum.blog.informs.org/2008/04/23/the-orms-ecosystem-strengths-weaknesses-opportunities-and-threats/#comment-6116 Michael Trick’s Operations Research Blog » OR Forum on SWOT for Operations Research Thu, 24 Apr 2008 02:40:41 +0000 http://orforum.blog.informs.org/2008/04/23/the-orms-ecosystem-strengths-weaknesses-opportunities-and-threats/#comment-6116 [...] area of the journal Operations Research.  Written by ManMohan Sodhi and Chris Tang, it is entitled “The OR/MS Ecosystem: Strengths, Weaknesses, Opportunities, and Threats” and analyzes the state of the field, with a particular emphasis on the relationship with academia [...] […] area of the journal Operations Research.  Written by ManMohan Sodhi and Chris Tang, it is entitled “The OR/MS Ecosystem: Strengths, Weaknesses, Opportunities, and Threats” and analyzes the state of the field, with a particular emphasis on the relationship with academia […]

]]> Comment on Simple Models of Influenza Progression within a Heterogeneous Population by Mark Temple http://orforum.blog.informs.org/2007/06/15/influenza-progression/#comment-1216 Mark Temple Tue, 10 Jul 2007 14:58:09 +0000 http://orforum.blog.informs.org/2007/06/15/influenza-progression/#comment-1216 May a humble medic enter the ring? In my professional life, I never heard any medic talk about R0, until recently. Many, even now, do not know the concept, yet these practical epidemiologists control outbreaks every working day. The mathematics is very nice, some would say beautiful, but I was trained to respect elegance rather than worship it. The trouble with R0, it seems to me, is that is raises the mathematical simplification into a false vision of reality. The truth is we are wedded to it as without it the mathematics becomes intractable. Since for most of us it is already in that state from the first integral, perhaps it should be dumped in the practical world of controlling an outbreak. In reality outbreaks were controlled before mathematical models existed, and the predictions of the SIR model were not fulfilled in the sucessful eradication of Smallpox. If I am wrong and out of line, please correct me gently:I will be wiser, but pray first consider that you too may be wrong. May a humble medic enter the ring?
In my professional life, I never heard any medic talk about R0, until recently. Many, even now, do not know the concept, yet these practical epidemiologists control outbreaks every working day. The mathematics is very nice, some would say beautiful, but I was trained to respect elegance rather than worship it.
The trouble with R0, it seems to me, is that is raises the mathematical simplification into a false vision of reality. The truth is we are wedded to it as without it the mathematics becomes intractable. Since for most of us it is already in that state from the first integral, perhaps it should be dumped in the practical world of controlling an outbreak. In reality outbreaks were controlled before mathematical models existed, and the predictions of the SIR model were not fulfilled in the sucessful eradication of Smallpox.
If I am wrong and out of line, please correct me gently:I will be wiser, but pray first consider that you too may be wrong.

]]> Comment on Thirteen Reasons Why the Vickrey-Clarke-Groves Process is Not Practical by P.J. Healy http://orforum.blog.informs.org/2007/04/17/thirteen-reasons/#comment-2 P.J. Healy Mon, 12 Mar 2007 19:51:06 +0000 http://orforum.blog.informs.org/2007/04/17/thirteen-reasons/#comment-2 Professor Rothkopf is correct; other Nash equilibria of the VCG auction, though still efficient, can lead to different revenue levels for the seller. For an efficiency-minded economist it doesn't matter who pays what to whom, as long as the object goes to the bidder with the highest value. But to a seller choosing the auction format, expected revenue is obviously a big deal, and this will impact the choice of auction format. Note, however, that revenue may go either way here: there are also Nash equilibria that lead to higher revenues than the dominant strategy equilibrium (for example, the high-value bidder bids his value v and everyone else bids a penny less than v; also, see Andreas Blume's papers characterizing all Nash equilibria of VCG auctions). So, we're back to the age-old equilibrium selection problem, and running experiments (laboratory or field) seems to be the sensible approach to figure out what revenue sellers should really expect. Professor Rothkopf is correct; other Nash equilibria of the VCG auction, though still efficient, can lead to different revenue levels for the seller. For an efficiency-minded economist it doesn’t matter who pays what to whom, as long as the object goes to the bidder with the highest value. But to a seller choosing the auction format, expected revenue is obviously a big deal, and this will impact the choice of auction format.
Note, however, that revenue may go either way here: there are also Nash equilibria that lead to higher revenues than the dominant strategy equilibrium (for example, the high-value bidder bids his value v and everyone else bids a penny less than v; also, see Andreas Blume’s papers characterizing all Nash equilibria of VCG auctions). So, we’re back to the age-old equilibrium selection problem, and running experiments (laboratory or field) seems to be the sensible approach to figure out what revenue sellers should really expect.

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