1. Preface¶

A first version of this volume was born several years ago, in Italian, as the main reference material for an advanced course on optimization modeling for students in Management Engineering at my Università degli Studi di Firenze. That course is no more active and the material remained unused for quite a few years. I have decided now to reconsider it as I think that, in these days, modeling skills are fundamental, possibly even more than before. Differently from when I wrote my Italian book, nowadays data, data science, analytics are terms we can find everywhere. Every company is moving towards data-oriented approaches, mainly following the giant waves of machine learning and artificial intelligence. However, some companies are experiencing difficulties, as the return on investment seems to be below expectations. I have neither an explanation nor a recipe for this. I suspect there might be many reasons: from one side the too high expectation, but, from another side, the fact that managers forgot that most of modern data science and machine learning is devoted to predictive analytics. Having the possibility of learning from data so that future trends (demand, production, yield, interest rates, …) can be reliably and robustly estimated is absolutely wonderful. However, after a reliable forecast becomes available, managers still need to take decisions. And this is where optimization (operations research) comes into play.

Optimization is the tool for good decision making or, better, for good decision support. In order to be able to suggest rational and effective decisions the Operations Research scientific community have offered courses, developed methods, analyzed theoretical aspects of optimization. However I feel there is still a wide gap which separates academic research from day to day adoption. And, in my opinion, part of this is due to the lack of sufficient modeling skills. If we cannot model a problem, no natter how sophisticated our optimization algorithm are, we will not be able to solve it nor suggest any decision. We need to communicate our problem to a solver. And to do this we need a deep knowledge of models.

Modeling is a creative skill, which is best learned by doing; however, especially for more advanced models involving integer variables or graph theory, modeling practice should be paired with a deep theoretical knowledge, if we wish to be able to solve large scale problems.

My interest in modeling arose by chance, thanks to a very stimulating activity as a book reviewer that I performed in my early years in the academia. Among the many books I received, I got to review, at some time, the fundamental volume of Hans Paul Williams [33] (indeed it was a much earlier edition than the one cited here). That book opened a new world to me: up to that time I had been thinking to the algorithmic aspects of optimization as the most relevant ones. But that book made me understand that without suitable formulation no problem can ever be solved, whichever the refined algorithms we have available. This seemingly trivial observation has indeed many consequences. One of these is the fact that we need to be organized and disciplined in formulating an optimization model; formulating a linear optimization model is not something which can be done simply after having learned the details of the simplex algorithm: practice and modeling theory is needed before a robust, reliable, useful model can be produced. Williams’ book opened my eyes on this fascinating subject; more or less at the same time I also had the opportunity of learning and using GAMS - Generalized Algebraic Modeling System. This modeling tool let me understand that even to formulate a “simple” linear optimization problem it is not enough to be able to write the cost vector, the constraint matrix, the right hand side. Or, to be more precise: it would be enough. But the effort in preparing these data can be enormously reduced if a modeling language is used; moreover the readability and the possibility of maintaining large models is enormously enhanced when using a modeling language. This is even more true when dealing with non linear models. GAMS is nowadays not the unique modeling system available. I have been using AMPL - A Mathematical Programming Language, AIMMS, which are commercial products, or JuMP (Julia Mathematical Programming) and Pyomo (Python Optimization Modeling) which are open source. There are many others, most of which however are limited to specific classes of problems like, e.g., linear models only.

Given the availability of Williams’ book and of several software modeling languages, one may ask why I felt the need to write a book on optimization modeling. First of all, of course, I confess I like writing and teaching and this is something I would do even if useless… But, also, I think that it is quite unusual that for such an important topic the most authoritative textbook (if not the only one) is still the excellent book by Williams which although re-published several times, is more or less unchanged since the 80’s, when the first edition appeared. I think there is a space nowadays for a more modern view of that classical textbook, which is and remains a fundamental reference for me as well as many in the optimization community. But in these years many things happened: advances in software, in modeling environments, in programming languages, in application domains. And, among the many novelties, also the way in which we publish radically changed. I took the decision to publish my book as a web site, with a companion GitHub repo for models and data. The idea is that of a dynamic book, which can easily change thanks to the addition of models, data, modeling languages. For many different reasons, I became more familiar with the AMPL language than any other. But I would like to ask my readers to contribute with translations of my AMPL models in other modeling environments (and, of course, to suggest modification and corrections to those I wrote). There will be also printed editions of this book, for those who prefer having a book on paper, but the on line web edition will in any case be the preferred and most up-to-date one.

In writing this book I also decided, for the first time in my long academic life, not to write a book (or a paper) in LaTeX from beginning to end. It was a difficult choice, as I cannot think of any other way to write math. But modern markup languages, like Sphinx, make the effort in writing a web book so light that eventually I decided to use this system. Of course, however, all of the mathematical formulae and the figures have been composed in LaTeX, with the help of the tikz package.

Finally: in a modern book on modeling, it is impossible to avoid sharing the developed models. Most of modeling, in fact, can be learned not only by studying existing modeling techniques, but also by experimenting with models and real data. Thus I created a github repository devoted to the material presented in this book. I invite readers not only to browse these models and use them, but also to suggest modifications, improvements and, in particular, to add models in all possible modeling languages. At the moment of writing this preface, only AMPL models are fully available, as described in the book. Volunteers are more than welcome to contribute new or existing models implemented in Julia/Jump, Pyomo, GAMS, Aimms, SCIP or any other high level modeling language. This can be easily done through the github repository. I thank in advance any reader who will help to improve and extend this material.