A Long View of Research and Practice in Operations Research and Management Science

From the Foreword by Marshall Fisher, The Wharton School, University of Pennsylvania:

As generation of academics and practitioners follows generation, it is worthwhile to
compile long views of the research and practice in the past to shed light on research
and practice going forward. This collection of peer-reviewed articles is intended to
provide such a long view.
This book contains a collection of chapters written by leading scholars/practitioners
who have continued their efforts in developing and/or implementing innovative
OR/MS tools for solving real world problems. In this book, the contributors share
their perspectives about the past, present and future of OR/MS theoretical development, solution tools, modeling approaches, and applications. Specifically, this book collects chapters that offer insights about the following topics:
. Survey articles taking a long view over the past two or more decades to arrive
at the present state of the art while outlining ideas for future research. Surveys
focus on use of a particular OR/MS approach, e.g., mathematical programming
(LP, MILP, etc.) and solution methods for particular family of application, e.g.,
distribution system design, distribution planning system, health care.
. Autobiographical or biographical accounts of how particular inventions (e.g.,
Structured Modeling) were made. These could include personal experiences in
early development of OR/MS and an overview of what has happened since.
. Development of OR/MS mathematical tools (e.g., stochastic programming, optimization
theory).
. Development of OR/MS in a particular industry sector such as global supply
chain management.
. Modeling systems for OR/MS and their development over time as well as speculation
on future development (e.g., LINDO, LINGO, and What'sBest!)
. New applications of OR/MS models (e.g., happiness)
The target audience of this book is young researchers, graduate/advanced undergraduate
students from OR/MS and related fields like computer science, engineering,
and management as well as practitioners who want to understand how OR/MS
modeling came about over the past few decades and what research topics or modeling
approaches they could pursue in research or application.