Bridging the gap between academy and industry: applied Operations Research/Analytics


By Álvaro García Sánchez, professor, Technical School of Industrial Engineering, Technical University of Madrid.

Optimization problems in real world systems are a huge challenge and are a source of enjoyment and effort for Operation Research/Analytics (OR/A) modelers as well of satisfaction when delivering good quality solutions. Nevertheless, other compelling challenges go along with devising a good solving strategy itself during a project where there is a customer.

A first challenge is to gain the credibility from the manager who is running a system that may me improved thought OR/A models. When there is a chance for modeling a problem and gaining effectiveness and efficiency in some way, prior to modeling itself, first we need to prove or inspire trust in the incumbent manager. Past failures, the gap between the practitioners and the academic worlds and so many other reasons may be stoppers.

The good news is that, once this barrier is overcome, the floodgates are opened for an ongoing improvement process getting more and more from increasingly comprehensive models, more valid, etc. In this sense, projects with a reduced scope can be a great approach for the managers to have a taste of what can be gained from OR/A and from modelers to learn more about the problem, and be in better conditions for having an estimate in terms of time and money of how the whole problem can be addressed.

« Projects with a reduced scope can be a great approach for the managers to have a taste of what can be gained from operations research/analytics »

Another challenge is getting to know the actual problem, so that it can be described in terms of a set of requirements. The modeler has to make a great effort to talk the manager’s language and distill what is relevant from what it is not. A typical issue appears when the client suggests some rules that are normally applied to come up with a solution to their everyday problem.

For example, since a particular process in a factory does not always require a night-scheduled worker, although nothing forbids scheduling that task during any other time of the day. This is not an actual requirement, but something that turns the problem into a tractable one for the scheduler. The modeler needs to know what an actual requirement is and what is not, not arrogantly but humbly, since a lot of knowledge is typically within the mind of some people and cannot be found in a structured document.

Other aspects may also be a source of endeavor, such as gathering and treating raw relevant data, integrating models with existing information systems, devising usable interfaces or managing out of scope demands during the development of the project.

Over the last years, the OR/A world has gained experiences in accomplishing successful projects not only in strictly mathematical terms, but also in the above mentioned key aspects. Thus, practitioners are in better conditions to rely in optimization and simulation tools and techniques for better managing their system. A time of closer cooperation and richer projects lies ahead of us.