OR Practice Methodology: Assumptions & Concepts

15 May 2008 by Sanjay Saigal.

In a recent article, I examined the need for the practice of Operations Research to be driven by a formal methodology. The primary motivating factor is the increasing mainstreaming of OR (or more general Advanced Analytics) techniques in Enterprise IT. This in turn is forcing system developers and consultants to proactively manage scalability and management of risk by “industrializing” OR delivery.

The typical use of this word in OR-related discussion concerns specific techniques – usually mathematical or statistical in nature – for solving reasonably well-posed problems. (Fairly typical of this usage is the paper titled “A methodology for integrating cell formation and production planning in cellular manufacturing”, published in Annals of Operations Research.) This OR is replete with (so-called) methodologies to solve problems fitting into structured classes. However, in most cases, “methodology” is just a nickel cigar encased in a five dollar label. What is under discussion is essentially a “method” or “technique” (or perhaps a class of such methods.). When speaking of an OR Practice Methodology, we’re interested not in the tools but in the practical principles and artifacts governing the deployment of OR techniques and methods.

The focus of this series of articles is on embedding OR in Enterprise IT. (While OR value is often delivered through one-off analyses, the role of a practice methodology in that purely consultative model is less clear.) In the Enterprise IT context, software engineering provides a sound basis for exploration. Methodologies such as Agile Programming, eXtreme Programming (XP), CMMI and Rational Unified Process (RUP) provide the IT project manager with a variety of risk control frameworks and usable artifacts for standard software development. However, they do not directly address the needs of the OR practitioner. Neither are they well-understood or used in the OR community.

Attempts on OR-specific methodologies have failed to gain traction in the practice community. The CHIC methodology for constraint programming was extended in the late nineties to large-scale combinatorial optimization problems and named CHIC2. However, I do not believe that CHIC2 was ever taught or used anywhere outside its academic seedbed in Europe.

More recently, French software vendor Ilog has extended its proprietary methodology for rule-based systems – ISIS – to optimization. Being a proprietary system that is viewed as a competitive advantage by Ilog, a list of OR-specific ISIS features has not been released. I do believe that ISIS has not, in any meaningful way, been used to develop a large-scale OR application. Ilog’s view of OR is limited to optimization, and thus ISIS is unlikely to be applicable to OR as a whole. But if opened to public view, it is the most promising methodological effort that I am aware of.

In the next article in this series, I will discuss practice methodology from the perspective of INFORMS, the leading professional society for OR.

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