ILS 2014, a buffet of logistics and OR

This post was originally written for OR Complete. I re-post it here in case you missed the original posting.

ILS – a short name for the International Conference on Information Systems, Logistics and Supply Chain, is a bi-annual conference focusing on global supply chain and logistics management, with a focus on information systems and decision support systems. The attendance comes mainly from European and North American researchers, but there were also participants from Tunisia, Morocco and Korea.  It’s a small but very specialized scientific conference, attracting between 50 to 100 presentations. The number is limited mostly because in order to present, you must submit a 8 to 10-page paper, which is subject to a double peer-review. [Continue reading]

Uncertainty, Models, and the Chaos Out There

Future is dominated by uncertainty. In terms of supply chain management – my main field of application – it comes in terms of variation in lead times or demand levels, or in the form of severe events such as hurricanes which can severely disrupt a supply chain. Optimization can help to cope with uncertainty, but I often get the feeling that we don’t completely “get it”.

Mathematical programming models have been used in supply chain planning for about 50 years. The vast majority of these models are deterministic, and that has now been described as a major weakness. Sure, that plan is optimal, but if demand, prices, or lead times change, then it’s not optimal anymore. A deterministic model may not result in a more resilient or robust plan.

Of course, many models have also been proposed to deal with uncertainty. Stochastic and robust optimization is now applied to many problems, and thanks to the significant improvement of mathematical programming solvers, we are actually able to solve several of these models. These models are also based on several assumptions, mostly some form of characterization about what is uncertain. By reading – and sometimes writing – those papers, I feel the touch of classical modeling, where everything is in order, well-structured and controlled.

I’ve had the opportunity to talk with supply chain planners and logistics managers in the industry, and I get a very different feeling. It’s the Wild West out there – everything is moving fast and furiously, and people do their best to cope with change as it comes by. They spend the little time they have left into analysis and trying to anticipate what is going to be the next big shift. And it’s not because they lack skills or knowledge in planning. While interesting and important – long-term risk planning doesn’t help meeting your monthly targets, but integrating this last-minute new customer into next week’s supply chain planning does.

In fact, the only industries I’ve been in touch with that seem to do extensive risk planning are the energy and military sectors. It’s a natural fit:  they have the resources and the time to do extensive analysis, and the risks are so huge that it warrants the effort. North America should survive the destruction of a Wal-Mart distribution center, but a nuclear plant meltdown or a major crash of the power grid is another matter. For these industries, the methodologies developed in academia to deal with risk and uncertainty seems to be a good fit.

As for the supply chain manager, I’m not sure research has yet found the right set of tools to help him/her actually deal with the chaos out there. Academic notions of risk, dynamic models and planning under uncertainty fall short to describe what they are dealing with. Sure, I’d like to bridge that particular gap between theory and practice, but I believe we need different tools to begin with.

Seeds and Parallel MIP solvers, part I


Over recent years, mixed-integer programming (MIP) solver developers worked really hard to provide parallel codes that are both fast and quite stable. A few years ago, using a parallel code resulted in huge variations in run times: successive runs of … [Continue reading]

Building the perfect MIPping machine [dual]

Yesterday’s post explained why I prefer run solver on fast machines over big machines and gave a short example why. In today’s post, I provide some very personal guidelines on how to determine what kind of machine you might need to solve MIPs.  … [Continue reading]

Do you need a bigger computer to solve this MIP? [primal]

When I have difficult time solving a mixed-integer optimization problem (MIP), one of the most common reflexes is to run the problem on a bigger or faster machine.  However, when solving MIPs, throwing more processor power at a problem will not … [Continue reading]

Things you learn in a Ph.D. that are not written in textbooks

Those are things I learned during the first phase of my experimentation over the course of my Ph.D (year 2 of 4) There is a story behind each of this statements; I may blog about some of them if there is interest in it and I find the time to write … [Continue reading]

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 … [Continue reading]

Supply chain network design in 500 words

I've spent more time working on supply chain network (SCN) design problems than any other. This post summarizes the topic. What is supply chain network design? SCN design is a strategic problem arising in logistics and supply chain management. … [Continue reading]

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 … [Continue reading]

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 … [Continue reading]