The Palgrave Handbook of Operations Research

Preface of the Handbook

Operations Research (OR) is a fast-evolving field, which is having a significant impact on its neighbouring disciplines of Business Analytics and Data Science, and on contemporary business and management practices. It can be a difficult field to navigate, because of the multiplicity of academic journals devoted to it, and the need for extensive prior knowledge to comprehend the more technical articles in OR. We have therefore set ourselves the ambitious task of synthesising the state of the art in major aspects of Operations Research, as well as showcasing some of the latest developments, both in theory and in practical application.

Views differ on what should be included within the scope of Operations Research. Most would agree that optimisation (including heuristics) and simulation methods, that can be used for Prescriptive Analytics, would fall under the banner of OR. But what about forecasting methods that can be used for Predictive Analytics? At a more fundamental level, should ‘soft OR’ be in scope or out of scope? We have taken the view that an inclusive stance is the most helpful, both for theory and practice. Real-world problems often require consideration from both ‘softer’ and ‘harder’ perspectives and need consideration of both predictive and prescriptive problems.

In accordance with our inclusive approach to OR, we have divided the book into six parts, more or less evenly balanced, making up 28 chapters. Part 1 covers Discrete Optimisation (DO); part 2 deals with Continuous Optimisation (CO); part 3 discusses Heuristic Search Optimisation (HSO); part 4 covers Forecasting, Simulation and Prediction (FSP); and part 5 treats Problem Structuring and Behavioural OR (PSB). The book finishes with part 6, which presents some ‘Recent OR Applications’ (RORA), including big data.

The part on Discrete Optimisation, which also includes combinatorial optimisation, contains five chapters. The first chapter covers bilevel discrete optimisation with a focus on computational complexity, by Kochetov, Plyasunov and Panin. This is followed by location problems with uncertainty, namely single source, covering and hub hubs, by Azizi, Sergio and Irawan. The next chapter presents a survey on integrated routing problems, including inventory routing and location routing, by Guastaroba, Mor and Speranza. Models and applications for various knapsack problems are discussed by Wilbaut, Hanafi, Coelho and Lucena. The fifth and last chapter in this part of the book is on models and algorithms for rank aggregation by Alcaraz, Landete and Monge.

Combinatorial Optimisation, which also includes global optimisation, has five chapters starting with the work of Jones and Florentino on multi-objective optimization, with an emphasis on compromise programming and its applications. Chapter 7 is given by T. Drezner, where she covers competitive location problems. Fernandez and Toth discuss the interval tools that are used in branch and bound including potential software. This is followed by chapter 9, on continuous location problems, by Z. Drezner. Recent developments and challenges in data envelopment analysis (DEA) are presented by Emrouznejad, Yang, Khoveyni and Michali in the last chapter of this part.

Heuristic Search Optimisation five chapters, starting with an overview on heuristics and metaheuristics by Salhi and Thompson, followed by a chapter on formulation space search by Mladenovic, Brimberg and Urosevic. Chapter 13 deals with an evolutionary approach, namely, sine cosine algorithm which is produced by Rawat, Singh and Bansal. A new methodology on heuristic search called less is more is introduced by Mladenovic , Z.Drezner, Brimberg and Urosevic while the last chapter in this part outlines the new era of metaheuristics including hybridisation and learning by Salhi and Thompson.

Forecasting, Simulation and Prediction deals with stochasticity in general and contains six chapters. The first chapter, chapter 16, is by Goodwin and Fildes on forecasting with judgment, followed by an analysis on simulation with input uncertainty, produced by Barton, Lam and Eunhye. Chapter 18 is given by Xu and Zhang on fuzziness for multi-attribute decision making. An introductory chapter on measures in reliability is delivered by Wu and Coolen, followed by chapter 20, which deals with queueing theory, with an emphasis on variable speeds by, Yajima and Phun-Duc. The last chapter in this part discusses forecasting and its beneficiaries by Rostami-Tabar and Boylan.

Problem Structuring and Behavioural OR covers the ‘softer’ aspects of OR. It consists of three chapters, starting with the work of Kunc and Katsikopoulos on recent developments and future perspectives on behavioural OR, followed by a chapter on problem structuring methods (PSM) by Franco and Rouwette. The last chapter of this part, chapter 24, is presented by Lami and White, and also discusses PSM but with a focus on its impact and relevance in the digital age.

The last part, on Recent OR Applications, contains four chapters. It starts with a discussion on recent advances in big data analytics by Li, Kong, Zheng and Pan, followed by a chapter on OR models for humanitarian logistics by Ghavamifar and Torabi. A chapter on the delivery with drones with an emphasis on the last mile is given by Chen and Demir, followed by the last chapter of the book, chapter 28, on an application of radiotherapy using DEA by Ehrgott, Raith, Shentall, Simpson and Stubington.

This handbook aims to cover a wide spectrum of OR, though we appreciate there are areas that we would love to have covered but could not due to authors’ circumstances, especially some theoretical areas of OR and other applications. We are also pleased and proud to note that our contributing authors are drawn from a diverse pool of people, coming from a large number of countries, gender, religion and seniority.

We hope this edited Handbook on OR will be a useful addition to researchers, students and practitioners alike.