OR in the field: two challenges

This post presents some thoughts about doing applied operations research together with companies. It’s a lot of work, to be sure, but it’s also a lot of fun. Some of my colleagues tease me about not being often at my office. While it’s true that I split my office time between the research center and the buisness school, I also spend a lot of time with logistics managers and planners in forest companies but also with government people. Most of my projects involve operations research in one way or another.

First, understand the problem

The first challenge is understanding the problem, as seen from the user’s perspective. I have yet to find someone whose problem is solving a straight-out-of-the-box CVRP. Logistics problems, in particular, often are interconnected, so inventory or production planning decisions often interact with other decisions such as transportation or sales.  As I mentioned in an earlier post, many users aren’t interested in a logistics plan that aims solely at minimizing direct cost. This is especially true in tactical or strategic planning, where I do most of my work. Beyond cost minimization, what the user really wants to achieve through tactical planning is often a low-cost solution that offers a “good” compromise between a certain number of other factors.

Second, build the right model

Once you have a good idea of what is important in the problem, I usually try to build the simplest possible model  that can possibly work. I do this for two reasons. First, the distribution of time needed to debug a complex model tends to be heavy-tailed, and it is less the case for simple models. Also, the flaws and shortcomings of simple models are easier to understand, explain and correct than those of bigger models. And yes, bigger models have flaws too. They just are more tricky to find and fix.

Quite often, a simple model is the right first model to build. Then add (or remove) complexity and features one at a time.

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