Timeline of Developments

Developed the first interactive optimization model ("SuperSPIN") for network design for less-than-truckload motor carriers. It was adopted by almost the entire LTL industry, and helped to stabilize LTL trucking in the post-deregulation era. SuperSPIN is still in production 40 years later (marketed by Manhattan Associates).

Edelman finalist with SuperSPIN implemented at Yellow Freight System.

CASTLE Laboratory was founded with the hiring of Hugo Simao to develop an operational linehaul planning system for Yellow Freight System. First implemented in 1992, the system is still running 30 years later (with a major upgrade implemented in 2018). CASTLE would grow to handle projects in trucking (LTL, truckload, parcel), rail (primarily locomotive optimization, management of high value spare parts), before evolving into energy, e-commerce and health.

Developed the "LQN" methodology for fleet management, the predecessor of approximate dynamic programming for high-dimensional applications. This methodology used a linear approximation of value functions, a technique that is still being used to optimize fleets of drivers.

Transport Dynamics was founded by Derek Gittoes.

Introduced the idea of merging historical patterns in a large-scale optimization model, blending low-dimensional rules with high-dimensional algorithms. This idea was a major breakthrough since it helped to incorporate simple patterns of behavior captured using machine learning with optimization-based methods that could handle high-dimensional applications such as managing drivers.

Greg Godfrey develops the CAVE algorithm and adapts it to resource allocation problems with multi-period travel times. This logic remains one of the core tools of the lab for a wide range of resource allocation problems.

Peter Frazier introduces the "knowledge gradient," launching a new direction in optimal learning.

PENSA developed an array of solution methods for a wide range of energy storage problems using the universal modeling framework, drawing on all four classes of policies to handle the diverse characteristics of energy storage problems. The complete list of publications is available here.

Ilya Ryzhov bridges online and offline learning using the knowledge gradient.

Optimal Learning is published by Wiley, introducing an entirely new class of policies for information collection tuned to the needs of business, science and engineering, geared to an undergraduate audience.

Wrote "Jungle of Stochastic Optimization" for the Informs TutORial series. First time the four classes of policies were presented formally for solving stochastic optimization problems. Follow-on papers in 2016 (also for the TutORials series) and 2019 (for European J. of Operational Research) established the unified framework for sequential decision problems. This field came to be known as "sequential decision analytics".

Compiled 10+ years of research into energy storage, which is the first to fully research all four classes of policies in the context of a wide range of storage applications, including the first formal model to properly handle rolling forecasts.

Published Reinforcement Learning and Stochastic Optimization: A unified framework for sequential decisions. The first book to unify 15 fields of stochastic optimization using a single universal modeling framework that can be used to model any sequential decision problem. The book then identifies four classes of policies that include every possible method for making decisions.

Posted A Modern Approach to Teaching Optimization which presents a fundamentally new approach for teaching an introduction to optimization course for undergraduates or masters.