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Über dieses Buch

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.



Chapter 1. Introduction: A Long View of Research and Practice in Operations Research and Management Science

Operations Research (O.R.) is rooted in three fields: military operations, economics, and computer science. Operations Research (O.R.)—or, Operational Research—as a field was formally created by scientists in the UK, in particular by researchers working for the Royal Air Force. At the same time, there were parallel efforts in the US to examine ways of making better decisions in the different areas of military operations during WWII [15]. Still, research in operations already had a long history in England rooted in economics, going back to Charles Babbage’s study of the pin industry (that following Adam Smith’s “division of labor” study of the same industry) and of the postal system resulting in “penny post” that continues to be the model in most countries, thus justifiably earning Babbage the “father of operational research” [23]. It is interesting that Babbage also designed the analytic engine, essentially a programmable computer, because modern O.R.’s insistence on mathematical theory lie in the work of von Neumann and Alan Turing among others who laid down the foundations of the modern computer and of computer science. This book, with a long view of research and practice in O.R., reflects these three roots of operations research.

ManMohan S. Sodhi, Christopher S. Tang

A Long View of the Past


Chapter 2. Economic Planning Models for India in the 1960s

In the 1960s two major linear programming models were constructed to provide guidance for planning the economic development of India. These multi-sectoral, multiperiod models, although modest in size compared to present linear programming applications, were regarded as large according to the standards and computing capabilities of that time. We review the experiences with these two applications and discuss how they demonstrate the need for Geoffrion’s subsequent research in large-scale mathematical programming, data aggregation in models, and structured modeling.

Donald Erlenkotter

Chapter 3. The Persistence and Effectiveness of Large-Scale Mathematical Programming Strategies: Projection, Outer Linearization, and Inner Linearization

Geoffrion [19] gave a framework for efficient solution of large-scale mathematical programming problems based on three principal approaches that he described as

problem manipulations

: projection, outer linearization, and inner linearization. These fundamental methods persist in optimization methodology and underlie many of the innovations and advances since Geoffrion’s articulation of their fundamental nature. This chapter reviews the basic principles in these approaches to optimization, their expression in a variety of methods, and the range of their applicability.

John R. Birge

Chapter 4. Multicommodity Distribution System Design by Benders Decomposition * † ‡

A commonly occurring problem in distribution system design is the optimal location of intermediate distribution facilities between plants and customers. A multicommodity capacitated single-period version of this problem is formulated as a mixed integer linear program. A solution technique based on Benders Decomposition is developed, implemented, and successfully applied to a real problem for a major food firm with 17 commodity classes, 14 plants, 45 possible distribution center sites, and 121 customer zones. An essentially optimal solution was found and proven with a surprisingly small number of Benders cuts. Some discussion is given concerning why this problem class appears to be so amenable to solution by Benders’ method, and also concerning what we feel to be the proper professional use of the present computational technique.

A. M. Geoffrion, G. W. Graves§

Chapter 5. Structured Modeling and Model Management

We discuss Geoffrion’s contribution to model management and the practice of modeling through his structured modeling formalism. We review the trajectory of structured model management research, enumerating the contributions and limitations of both structured modeling and model management in general. We summarize by suggesting how Geoffrion’s work could be leveraged to contribute to a next generation of model management.

Daniel Dolk

Chapter 6. Retrospective: 25 Years Applying Management Science to Logistics

A management science practitioner recounts his 25 years of providing the corporate world with logistics optimization software and consulting. Clients included a substantial portion of the world’s largest businesses as well as the US Department of Defense and General Services Administration. Significant contributions were made to the profitability and return on assets of these client organizations. At the same time the members of the author’s company contributed to the ongoing development of optimization technology and large-scale data management to support logistics modeling. These efforts led to the publication of dozens of articles in first-rate logistics and management science journals as well as the election of two of the company’s principals to the National Academy of Engineering.

Richard Powers

Chapter 7. Optimization Tradecraft: Hard-Won Insights from Real-World Decision Support*

Practitioners of optimization-based decision support advise commerce and government on how to coordinate the activities of millions of people who employ assets worth trillions of dollars. The contributions of these practitioners substantially improve planning methods that benefit our security and welfare. The success of real-world optimization applications depends on a few trade secrets that are essential, but that rarely, if at all, appear in textbooks. This paper summarizes a set of these secrets and uses examples to discuss each.

Gerald G. Brown, Richard E. Rosenthal

A Long View of the Future


Chapter 8. Challenges in Adding a Stochastic Programming/Scenario Planning Capability to a General Purpose Optimization Modeling System

We describe the stochastic programming capabilities that have recently been added to LINDO application programming interface optimization library, as well as how these stochastic programming capabilities are presented to users in the modeling systems: What’s


! and LINGO. Stochastic programming, which might also be suggestively called Scenario Planning, is an approach for solving problems of multi-stage decision making under uncertainty. In simplest form stochastic programming problems are of the form: we make a decision, then “nature” makes a random decision, then we make a decision, etc. A notable feature of the implementation is the generality. A model may have integer variables in any stage; constraints may be linear or nonlinear. Achieving these goals is a challenge because adding the probabilistic feature makes already complex deterministic optimization problems even more complex, and stochastic programming problems can be difficult to solve, with a computational effort that may increase exponentially with the number of stages in the “we, nature” sequence of events. An interesting design decision for our particular case is where a particular computational capability should reside, in the front end that is seen by the user or in the computational engine that does the “heavy computational lifting.”

Mustafa Atlihan, Kevin Cunningham, Gautier Laude, Linus Schrage

Chapter 9. Advances in Business Analytics at HP Laboratories

HP Labs’ Business Optimization Lab is a group of researchers focused on developing innovations in business analytics that deliver value to HP. This chapter describes several activities of the Business Optimization Lab, including work in product portfolio management, prediction markets, modeling of rare events in marketing, and supply chain network design.

Chapter 10. Global Trade Process and Supply Chain Management

As a result of increased globalization of industrial supply chains, effective supply chain management requires sound alignment with the global trade processes. The design of the global supply chain and the determination of the right level of postponement are both tied intimately to the prevailing network of trade agreements, regulations, and local requirements of the countries in which the company is operating in. Moreover, the dynamic changes and uncertainties of these agreements and requirements must be anticipated. In addition, the complexity of the cross-border trade processes results in uncertainties in the lead time and costs involved in global trade, which naturally forms part of the consideration of global sourcing, and the resulting safety stocks or other hedging decisions. Governments, exporters, importers, carriers, and other service providers have to work together to reduce the logistics frictions involved in the global trade processes. The benefits accrue not only to the exporters, importers, and the intermediaries but ultimately they could foster bilateral trade. The only way to reduce the frictions is to gain a deep understanding of the detailed process steps involved to improve upon it by using information technologies and potentially re-engineer the processes. But the payoffs to such investments can be huge. This chapter provides some preliminary discussion of the inter-relationships between global trade processes and supply chain management, with the objective to stimulate research in this area.

Hau L. Lee

Chapter 11. Sustainable Globally Integrated Enterprise (GIE)

In this chapter, we present the globally integrated enterprise (GIE) as an emerging business model with strong implications for how companies run and operate their global supply-and-demand chains. The GIE shifts the focus from an efficiency-driven model to a value-driven one which leverages and integrates global capabilities to deliver value speedily, seamlessly, and in a flexible way, while maximizing profits. A GIE is a complex organization that faces many challenges. The evolution of the supply chain in the last 20 years has paved the way for the Operation Research (OR)-enabled Sense-and-Respond Value Net that supports today’s GIE needs. We present a GIE case study of a business transformation journey. We then describe the next steps for GIEs to become more socially, economically, and environmentally responsible through the use of OR, business analytics, and IT.

Grace Lin, Ko-Yang Wang

Chapter 12. Cyberinfrastructure and Optimization

In 2002 the U.S. National Science Foundation created a Blue-Ribbon Advisory Panel on Cyberinfrastructure, which submitted in January of 2003 a report entitled “Revolutionizing Science and Engineering Through Cyberinfrastructure.” Subsequently, the NSF created an Office of Cyberinfrastructure (OCI) independent of its directorates in such traditional areas as biology, computer science, geosciences, physical science, and engineering. In the following 3 years the NSF sponsored workshops leading to nearly 30 reports (

) on the role of cyberinfrastructure in specific areas of research. This chapter describes a variety of projects that fall into the intersection of cyberinfrastructure with the study and practice of large-scale optimization. In general, these projects involve large-scale optimization problems in system design, production planning, and logistics. However, the notion of large-scale optimization occurs in other disciplines including physical and biological sciences, engineering, economics. As such, there is a benefit to establish a community whose members use the same modeling and algorithmic techniques and who can benefit from the same software and services.

Robert Fourer

Chapter 13. Perspectives on Health-Care Resource Management Problems

Research devoted to health-care applications has grown increasingly within operations research over the past 30 years, with over 200 presentations at the 2008 INFORMS conference. Resource management is of particular importance within healthcare because of the system’s unique objectives and challenges. We provide a perspective of the current health-care literature, focusing on recent papers in planning and scheduling and reviewing them along four dimensions: (1) who or what is being scheduled, (2) the time horizon of the scheduling or planning, (3) the level of uncertainty inherent in the planning, and (4) the decision criteria. With this perspective on the literature we observe that the problems at the extreme ends of the time dimension deserve more attention: long-term planning/slash staffing and real-time task assignment.

Jonathan Turner, Sanjay Mehrotra, Mark S. Daskin

Chapter 14. Optimizing Happiness

We consider a resource allocation problem in which time is the principal resource. Utility is derived from time-consuming leisure activities, as well as from consumption. To acquire consumption, time needs to be allocated to income generating activities (i.e., work). Leisure (e.g., social relationships, family, and rest) is considered a basic good, and its utility is evaluated using the Discounted Utility Model. Consumption is adaptive and its utility is evaluated using a reference-dependent model. Key empirical findings in the happiness literature can be explained by our time allocation model. Further, we examine the impact of projection bias on time allocation between work and leisure. Projection bias causes individuals to overrate the utility derived from income; consequently, individuals may allocate more than the optimal time to work. This misallocation may produce a scenario in which a higher wage rate results in a lower total utility.

Manel Baucells, Rakesh K. Sarin

Chapter 15. Conclusion: A Long View of Research and Practice in Operations Research and Management Science

Research, teaching, and practice of OR/MS are becoming increasingly disengaged from one another in the OR/MS ecosystem. This ecosystem comprises researchers, educators, and practitioners in its core along with end users, universities, and funding agencies. To understand the reasons for this disengagement better and to engender discussion among academics and practitioners on how to counter it, we present the ecosystem’s strengths, weaknesses, opportunities, and threats. Indeed, some of the strengths are captured by taking a long view of the past, as in the first half of the present compilation of chapters and the opportunities by taking a long view of the future, as in the second half of the present compilation.

ManMohan S. Sodhi, Christopher S. Tang
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