When optimal is not enough

Currently I work with Professor Mikael Rönnqvist from Université Laval on an inventory and transportation management problem from a forest products company.  As a large scale commodity products company, much of its focus is on reducing logistics costs while improving its efficiency and service such as on-time deliveries. As such, we are developing a decision […]

OR-Enabled Collaborative Logistics

Today’s post is about collaborative logistics, one field of study in which I recently started to work in. It is a tricky field for many reasons, and I will try to explain why and how. Operations research is a key enabler of collaboration. Logistics is essentially management of the activities of transporting and holding materials from origins […]

There and back again

If your academic work is related to computation, chances are that once in a while you have to scrap a set of experiments and start over. It just happened to me again about two weeks ago. While I and a colleague were doing what is mostly a computation project, we discovered that one constraint was incorrectly […]

LP file format: uses and intricacies

LP is one of the most popular formats for explicit description of an optimization model (the other contender being MPS). It uses an algebraic format where you enter the problem’s objective function and constraints line by line. For the last few years I’ve been using that format quite regularly, and I’d say I both hate […]

Open question: tuning for hard instances

This is a rather short post on an open question, one I’ve been thinking about for some time. Automated tuning tools for MIP solvers have been around for the last few years. During that time, I have used these tools – and sometimes seen them used – to reduce run times for various types of […]

Solving sets of similar instances, Part I

In an industrial application, one has often to solve similar instances of the same mixed-integer linear programming (MIP) model. Furthermore, from one model to another, a large proportion of the data is similar. Because of the heuristic nature of MIP computation, these very similar instances could behave quite differently when one tries to solve them. […]

Smart models start small

There is only one good way to build large-size or complex optimization models: to start by a small model and adding elements gradually until you get the model you wanted in the first place. I have seen so many people (including myself) try to build large-size, complex models from scratch, only to spend countless frustrating […]

Closing the (MIP) Gap – Part II

Day #2 of the MIP 2013 Workshop reminded me that despite the numerous research efforts invested in mixed-integer programming in the last 60 years or so, much remains to be done. A lot of MIP models are still very difficult to solve. In MIP extensions such as nonlinear, multi-objective or multi-level programming, most large-sized problems […]

The Dark Side is strong within this year’s community

This post is written in the context of the 2013 Mixed Integer Programming workshop, held in Madison WI.  It is humorous rather than serious in nature, so please do not take this post too seriously. Day #1 from MIP 2013 workshop was rich and diverse in terms of technical content. Talks from speakers such as Tobias […]

Three value creation models in the OR field [dual]

I a previous primal post, I have described three generic types of value creation configurations: the shop, the chain and the network, as characterized in Stabell & Fjeldstad (1998). In this post, I apply these concepts to key players in the field of Operations Research. While names used are fictional, some players may recognize themselves in […]