INFORMS TutORials

The chapters in this year’s TutORials volume span a number of exciting topics in modeling, optimization, and decision making for contemporary OR/MS problems. The areas covered include machine learning, chronic disease modeling, food safety, financial networks, and production problems; methodological advances are presented for deterministic and stochastic optimization, as well as several heuristic approaches. Expert advice from practitioners involved in real-world applications is also provided.

TutORial: Nature-Inspired Heuristics

By Craig Tovey. - Evolutionary Programming, Genetic Algorithms, Simulated Annealing, Ant Colony Optimization, and Particle Swarm Optimization are among the earliest optimization heuristics inspired by animate and inanimate phenomena in the natural world. Some of the more recently invented methods...

TutORial: A Guide to Optimization Based Multi-Period Planning

By Linus Schrage. - Many organizations use multi-period planning models that involve optimization to decide things like the best production or investment levels in multiple periods into the future. There are a wide variety of features a user would like to have in such models. How those features...

TutORial: Tabu and Scatter Search: Principles and Practice

By Manuel Laguna. - This tutorial focuses on the metaheuristics known as tabu search and scatter search. Tabu search has dramatically changed our ability to solve a host of problems in applied science, business, and engineering. The adaptive memory designs of tabu search have provided useful alt...

TutORial: Coalescing Data and Decision Sciences for Analytics

By Suvrajeet Sen, Yunxiao Deng, and Junyi Liu. - The dream of analytics is to work from common data sources, so that all of its facets (descriptive, predictive, and prescriptive) are supported via a coherent data-driven vision. This vision of analytics is what we refer to as “Integrative Analytic...

TutORial: Risk-Averse Stochastic Modeling and Optimization

By Nilay Noyan. - The ability to compare random outcomes based on the decision makers' risk preferences is crucial to modeling decision making problems under uncertainty. In this tutorial, the primary focus is on the stochastic preference relations based on the widely-applied risk measure condit...

TutORial: Stochastic Gradient Descent: Recent Trends

By Raghu Pasupathy, Farzad Yousefian, and David Newton. - Stochastic Gradient Descent (SGD), also known as stochastic approximation, refers to certain iterative structures used for solving stochastic optimization and root finding problems. Owing to several factors, SGD has become the leading met...

TutORial: Bayesian Optimization

By Peter Frazier. - Bayesian optimization is widely used for tuning deep neural networks and optimizing other black-box objective functions that take a long time to evaluate. In this tutorial, we describe how Bayesian optimization works, including the Bayesian machine learning model it uses to m...