Book Reviews

Not for Free: Revenue Strategies for a New World

Berman, Saul J. 2011. Not for Free: Revenue Strategies for a New World. Harvard Business Review Press, Boston. 240 pp. $29.95.

We are seeing an increasing array of products for which customers will not pay directly; yet these products are “not for free”; providers need revenue to cover the costs of supplying the products. This book is loaded with examples of these kinds of products and of how firms provide (or might provide) these “free” products and make a profit.

The author, Saul J. Berman, is the Vice President and Global Strategy Consulting Lead Partner of IBM Global Business Solutions. He holds a PhD in management and information systems, an MBA in production systems and operations research (OR) from Columbia Business School, and a BSc in economics from the Wharton School. However, this book is a fairly typical strategy and marketing work with little indication that the author has OR in his background.

The book has four main chapters: segmentation, pricing innovation, payer innovation, and package innovation.

Market segmentation is the driver of scientific pricing and revenue management (RM); this is potentially interesting material for the OR community, although this chapter takes a traditional marketing approach to segmentation. After a brief discussion of the history of segmentation (from a marketing perspective), the content of the chapter reports the three important behavioural consumer segments in media found in an IBM study: massive passives, gadgeteers, and kool kids. In brief, massive passives watch television in their living rooms; gadgeteers actively adopt new technologies; and kool kids are highly skilled at customizing their media experiences. The author goes on to relate many examples from the media business to make the case that different marketing approaches are required to reach each of these three segments.

Pricing innovation is about separating the unit price from the product. The author discusses subscriptions, variable pricing, dynamic pricing, pricing by parts, à la carte and bundled pricing, and rentals as examples of innovative thinking in pricing when applied to atypical products (e.g., renting prom dresses). Although the mention of variable and dynamic pricing might suggest a link to RM, this link is missing; the section on dynamic pricing is just over one page in length. The author provides many examples, most successful, some less so, of using these different pricing innovations, and he attempts to offer strategic advice to the would-be price innovator.

Payer innovation involves finding business models in which the party who pays for the product is not the user. An obvious example is the use of paid advertising to support free media services (e.g., commercial television or World Wide Web information). Examples of ad-supported revenue models make up most of this chapter with some twists; examples include websites at which free users (i.e., nonsubscribers) are exposed to ads but subscribers can avoid them. Social networking is also examined; in particular, Facebook's difficulties in developing a revenue model that monetises the website's popularity are discussed. White labelling is included as an example of payer innovation; however, in RM this would be seen as the creation of a price-sensitive market segment with custom pricing for this segment.

Package innovation includes componentization (breaking up a product into smaller pieces so that the customers can assemble the product and pay only for what they need), value integration (enhancing the product, for example, by selling electronics with a service contract), and value extension (expanding the product into adjacent markets, for example, Martha Stewart and her broad range of personality-driven brands.) Interfaces readers might be interested in the Analytics as an Ad Service section, which discusses Google's offering of analytics services to advertisers as a way of increasing their ad spending on Google and YouTube websites.

I found this book to be stimulating and interesting, but a difficult read. The many business examples included are interesting, and the stimulation comes from attempting to link the strategic content of this book about pricing to analytical work in RM and pricing (something the author makes no attempt to do). The difficulty with reading this book is because of its weak structure coupled with example followed by example; the result is a lack of flow. Each chapter concludes with a Pitfalls section and a Getting Started strategic recipe for firms considering implementing the ideas discussed in the chapter; however, the content of these sections seems to appear out of the blue and is not developed from the material in the chapter. The Getting Started sections are lists of questions that executives must ask or are expected to ask in this new world; however, many of these questions appear to come from a basic introduction to strategy and add little value to the theme of the book; examples include “what industry do you play in now?" (p. 169) and "where might you like to play in five years?” (p. 169) in the chapter on package innovation.

Some questions raised have to do with analytics competencies. “Do you have the analytics in place to identify trends in behavior and usage of products?” (p. 169) is an example. Although it is hopeful to see questions about the firm's analytics in this kind of general management book, the reality is that nothing in this book would help senior executives answer this question, assess the value of analytics, or determine whether their companies need to invest in developing enhanced analytical competencies.

The book may be useful to Interfaces readers who are interested in RM or supply chains. Many OR research articles in these areas include “let the price of the product be $p/unit,” although in RM research $p/unit is often a variable to be optimized. This book strongly makes the point that many products now have no unit price; yet pricing decisions must be made and revenues are required if the product is to continue to be available. One takeaway from this book is that many research and analytics application opportunities are available in this new world of “not for free.”

Peter C. Bell

Richard Ivey School of Business, University of Western Ontario, London, Ontario N6A 3K7, Canada, [email protected]

Queueing Networks: A Fundamental Approach

Boucherie, Richard J., Nico M. Van Dijk, eds. 2011. Queueing Networks: A Fundamental Approach. Springer, New York. 798 pp. $199.00.

Queueing Networks: A Fundamental Approach is a 798-page, 18-chapter, five-part tour de force in which 29 authors deliver comprehensive accounts in their areas of expertise. This handbook aims to “highlight fundamental, methodological and computational aspects of networks of queues to provide insight and unify results that can be applied in a more general manner" (pp. v–vi). The five parts chosen by the editors for delivery are (1) exact analytical results, (2) monotonicity and comparison results, (3) diffusion and fluid results, (4) computational and approximate results, and (5) selected applications.

The tradition of using elegant and powerful mathematics to provide valuable insights into real-life problems is important to this area of research and to these authors. In reading the handbook, I quickly found that key ideas with which I was familiar, such as product form solutions, decomposition, Erlang loss models, Little's law, and Poisson arrivals see time averages (PASTA), were special cases of much more extensive sets of results and theories. The aspiration of the handbook is that the cross-fertilization and development of these types of ideas will continue to pay dividends in a real world in which networks abound in areas as diverse as telecommunications, data networks, manufacturing, and healthcare.

The chapters differ in style. Some present tutorials on fundamental approaches and ideas, rapidly building up the reader's understanding of the topic from a basic starting point. Others are more research-oriented, expecting much more of the reader in terms of background. All the chapters are exceedingly well-referenced. Each covers a significant amount of ground. No chapter is for the faint-hearted!

My guess is that few people will read this handbook from cover to cover. However, I believe that many researchers or potential researchers will find it to be a valuable repository of key ideas and methods and to be thought-provoking, both in its attempts to unify results and in the way that it gives researchers the opportunity to unify in their own minds results that interest them.

As a researcher with a particular interest in using queueing network models for tackling real-life queueing networks, especially in the field of healthcare, I am impressed by the amount of information that I did not know. This provokes me to pose the following questions of queue network researchers, including the authors.

• Can we make more use of the extensive body of work in this handbook to help us better and (or) more efficiently understand the behavior of real-life queues and see opportunities for managing them better?

• What scope is there for the largely steady-state theories, results, and insights presented in the handbook to make a greater contribution in understanding and managing time-dependent queueing networks?

• What is the role of simulation modelling alongside the methods described in the handbook for furthering our understanding of and insights about queueing networks?

Dave Worthington

Department of Management Science, Management School, Lancaster University, Lancaster LA1 4YX, United Kingdom, [email protected]

Rational Decision Making

EisenfÜhr, Franz, Martin Weber, Thomas Langer. 2010. Rational Decision Making. Springer-Verlag, Berlin. 461 pp. $39.95.

Decision analysis, or prescriptive decision theory, aims to develop tools and theories to help people think through and ultimately make complex decisions. Perhaps because of its interdisciplinary nature—it is a patchwork stitched from economics, mathematics, psychology, and information systems—or because many of its secrets are best learned by experience, textbooks on the subject are relatively rare. I learned decision analysis from four main sources: Keeney and Raiffa (1993), Von Winterfeldt and Edwards (1986), French (1986), and Belton and Stewart (2002). Although all were or are now considered classics in the field, none was written as a textbook. I struggled through them as, I imagine, most new entrants to the field do today. My struggle might have been considerably easier had Rational Decision Making been available.

This book is the first English translation of the authors' German-language textbook, which is now in its fifth edition. It focuses almost exclusively on value and utility function methods. Its limitation is that it hardly mentions other approaches (e.g., outranking or aspiration-based methods); however, within its scope, it provides a comprehensive survey of existing theory and applications. Although the book targets a student audience, it is also clearly written for practical decision makers—managers, politicians, and leaders. Almost every page is interspersed with short examples that help to clarify conceptual terms and highlight the practical significance of the theoretical results. It includes a selection of up-to-date research and many techniques one might not expect to find in an introductory textbook; for example, it mentions even swaps or nonexpected utility theories. Although these interludes into more advanced areas might be confusing to students (I felt that they were generally too short to be self-contained descriptions), they should be useful to the broader audience. Extensive references to further reading are also provided.

The approach adopted throughout the book is to develop a number of reasonable sets of axioms about decision-making behavior and to then discuss specific preference theories that can be derived from each set of axioms. This incremental approach makes it clear that one correct prescriptive decision model does not exist: different decision makers may adopt different axioms; the role of decision analysis is to help decision makers to think through these choices and arrive at better understandings of their own preferences and the options available to them. I found a recurrent theme of the book particularly instructive—it constructs simple, transparent models (wherever possible) and then discusses the important role that problem structuring plays in these constructions. The authors point out several apparent complexities (e.g., violations of preferential independence and aggregations of group judgments), which can be more fruitfully resolved by restructuring the problem than by adopting more complex decision models.

This book is well structured and develops the material in a logical way. Chapters 1–4 cover the structuring of the decision problem. Problem structuring is a vast topic which, because of the complexity of most decision problems, is difficult to convey in a textbook. The authors cover the basics solidly enough, although the scope is a little limited for my liking; they place too much emphasis on more mechanistic tree-like formulations and too little on what might be called the “United Kingdom” school of problem-structuring methods, which are more oriented toward tools for facilitating discussion, as the July 2006 special issue of the Journal of the Operational Research Society reviews.

Chapter 5 introduces the most basic form of decision making: a single objective and no uncertainty. Layers of complexity are gradually introduced: multiple objectives in Chapter 6, uncertainty in Chapter 9, both uncertainty and multiple objectives in Chapter 10, multiple time periods in Chapter 11, and multiple decision makers in Chapter 12. These chapters, particularly the ones on uncertainty, are wonderful introductions to the subject matter from which experienced analysts may also gain new insights and food for thought. The authors move steadily and systematically through the material, assisted by a continuous stream of well thought-out examples. They present results with relatively little mathematical formalism, often using heuristic argument rather than logical proof. Although this makes some sections verbose, the net effect is to make the book more readable and accessible than most others on the subject.

The book has three additional chapters. Two cover basic probability theory and simulating from probability distributions—useful skills for a decision analyst; however, these topics are covered relatively superficially and are treated at length in any number of textbooks. An instructor could easily omit these chapters and still use the rest of the book. A final chapter covers descriptive decision making. It describes empirical results on observed decision making and the preference theories developed to accommodate these observations. The chapter, which is an excellent summary, has the added bonus that it includes a number of current developments in the field.

To summarize, this book can capably serve as a textbook for a semester course on prescriptive decision theory, with a specific focus on value and utility functions, at either a senior undergraduate or early graduate level. It can also be a useful introduction to the topic for managers and other practical decision makers.

Ian Durbach

Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa, [email protected]

Trends in Multiple Criteria Decision Analysis

Greco, Salvatore, Matthias Ehrgott, JosÉ Rui Figueira, eds. 2010. Trends in Multiple Criteria Decision Analysis. Springer Science + Business Media, New York. 481 pp. $189.00.

Multiple criteria decision analysis (MCDA), a family of methodologies developed to analyze and support decision making with multiple objectives, is now a mature field. It encompasses both multiattribute utility theory (MAUT)-based methods (Keeney and Raiffa 1993), multiobjective optimization algorithms, methods based on weaker preference relationships (e.g., outranking operators), and methods adopting non-MAUT principles (e.g., the analytic hierarchy process (AHP) and fuzzy MCDA), as Belton and Stewart (2002) discuss.

In recent years, the numbers of published papers and real-world applications in the MCDA field have grown exponentially. A relatively recent Management Science paper (Wallenius et al. 2008) highlights developments in the field since its inception and suggests several research directions.

Within this context, Trends in Multiple Criteria Decision Analysis is welcome. It provides a forum for the contributing authors to explore new directions for MCDA, in some cases extending the directions that Wallenius et al. (2008) highlight. I describe my (somewhat personal) view of the book's key contributions below.

The first major contribution is the consideration of links between MCDA and decision-making problem solving. The chapter by Yu and Chen links the process of multicriteria evaluation with human behaviour, which the authors see as dynamic processes of goal setting and alternative evaluation. The chapter by Belton and Stewart considers the links between problem structuring—an often neglected but crucial issue in any real-world intervention—and multicriteria analysis. In addition to reviewing in detail the relevant literature, they suggest considering MCDA models mainly as problem-structuring tools, beyond their traditional role of evaluating decision alternatives.

The second contribution addresses the role of robustness analysis from a conceptual and an algorithmic perspective. Given the growing level of uncertainty we experience and the often conflicting value systems of decision makers and stakeholders involved in making decisions, the increasing importance of robustness in decision making is not surprising. Aissi and Roy's chapter provides a comprehensive and thoughtful discussion of robustness and an excellent conceptual roadmap. Lahdelma and Salminen suggest stochastic multicriteria acceptability analysis from an algorithimic perspective—a method to simulate problems with imprecise or missing information and assess, I argue, the robustness of decision options.

The third main trend discussed in the book deals with the links between MCDA and artificial intelligence (AI). This would seem a rich field for future innovations, given the strong tradition of MCDA in modelling preferences and decisions, coupled with AI's aim of replicating decision making and automating decision processes. Two chapters illustrate such avenues. Greco, Slowinski, Figueira, and Mousseau suggest how robust ordinal regression could be used to infer preference parameters (e.g., value functions) from ordinal preference information. This type of analysis may be particularly useful for learning and modelling revealed preferences of decision makers and customers, such as the ones derived from online shopping and other types of nonsupported decisions. Ouerdane, Maudet, and Tsoukias suggest another facet of the MCDA and AI link—the use of argumentation theory, an AI reasoning formalism, to develop explanations and justifications for choices. They suggest several potential application areas for argumentation-based decision support (e.g., in communicating experts' decisions and in mediating public debate).

Another significant contribution is the review of MCDA for group decisions and negotiation by Kilgour, Chen, and Hipel. Given the omnipresent nature of groups in organizational and societal decisions, this is clearly a crucial topic for MCDA research and applications. The authors discuss the differences between negotiation among parties and decision making. Although negotiation and group decision making share some commonalities (e.g., different preferences for alternatives), they also have some distinct characteristics; these include having a common ultimate goal, which is usual in group decision making, or not having such a goal, which is typical in negotiations. The authors provide relevant references to literature and highlight directions for future research.

Two chapters in the book address more recent developments in MCDA. Deb reviews evolutionary multiobjective optimization (EMO), a set of algorithms that employ evolutionary heuristics to find efficient solutions and may deal with problems that have intricate Pareto fronts. His discussion of decision making and EMO (e.g., on modeling preferences and dealing with a large number of objectives in EMOs), which are crucial for supporting real-world decision making with multiple objectives, is particulary useful. The other recent development that has also attracted growing attention in the MCDA community is the use of MCDA and geographic information systems, as Malczewski describes. In this context, MCDA can help in assessing regions and areas, combining geographical information and decision makers' values in a powerful way.

Two chapters address MCDA-based fuzzy sets. De Baetes and Fodor discuss the modelling of fuzzy preference structures; Mesiar and Vavrikova consider fuzzy sets in multicriteria analysis.

A distinctive feature of this book is how the authors freely explore their subjects and set up their specific directions for further research within the field they have chosen. This is particularly noticeable in two rather controversial chapters in which Barzilai and Wierzbicki criticize standard decision theory and suggest alternative approaches.

In conclusion, I recommend this book to researchers and those who are interested in new ideas, approaches, and application areas in the MCDA field. Greco, Ehrgott, and Figueira have made a courageous move by editing several insightful chapters that may help to shape some directions for MCDA research in this decade.

Gilberto Montibeller

Department of Management, London School of Economics and Political Science, London WC2A 2AE, United Kingdom, [email protected]

You're Next!

Green, Terry. 2011. You're Next! Marshall Cavendish, Singapore. 240 pp. $32.00.

You're Next!, which is also published in paperback under the title Cashier Number 3, Please!, is a queueing book without mathematics. You will not find Markov, Poisson, or Kendall mentioned in this book, although Erlang is mentioned in a historical context. Instead, Terry Green, who is on the board of the consulting firm Qmatic (www.qmatic.com), gives his experiences as a queueing management consultant. Moreover, he manages to do this in a highly entertaining and instructive manner. His book is full of charming anecdotes about actual consulting experiences with reluctant managers. It is great when the good guys (queueing consultants) manage to win over the tough-to-convince, leave-things-alone type managers. I can't wait for the TV series based on this book.

The book is full of the author's philosophy on improving queueing systems, which he has gleaned from years of observations and consulting on the management of people queues. Academics are often one step removed from the reality of real queueing problems. Not so for Terry Green. He has experience with queueing issues at the post offices in the United Kingdom and the United States, major banks, car dealers, catalogue stores, retail stores, shoe departments, and many other settings. The solutions he proposes for some of these situations are simple, but deceptively so. They must take into account the way the customer thinks; however, they must also anticipate the entire range of difficulties that the customer might encounter.

The only equation in this book is S = P − E, which is a shorthand statement indicating that customer satisfaction depends on the excess of perception over expectation. This might well also apply to those of us in teaching. Green includes a counterintuitive real-life example in which adding an extra server to the system increased the average waiting time per customer (that's bad); however, customer satisfaction increased (that's good).

For queueing academics who are asked to look at real queueing problems involving people, this book has lots of good advice. Green advocates collecting data by careful observation but trying to act invisible while collecting the data. For retail situations, he recommends taking what he terms a “retail safari” to see how successful and unsuccessful businesses run their operations (including the queueing aspects of these businesses). He points out that small differences in the implementation of queueing management systems can mean major differences in the results. He realizes that having a good solution for a queueing improvement is not enough. Convincing the decision makers to implement the solution is important. Allowing the decision makers to take some credit for the creation and implementation of the new system is critical.

Green generally advocates the use of linear queues (by which he means single-line multiple-server queues) because of the perception of fairness to customers. He notes that the shortest-processing-time ordering of customers rather than first-in-first-out will lower the average waiting time of customers; however, such systems should only be implemented when they are perceived as being fair by the customers; he gives a nice illustration of such a case.

The book has no index (after all, it is not a textbook); however, it includes a table of contents. I loved the bookmark that came with the paperback version. It shows a snaking queue of customers in different dress and poses. On closer observation, all the customers are Terry Green. A little humor goes a long way! The author clearly has a passion for the subject of queues. His claim that his voice has become the fourth most recognizable voice in the United Kingdom (for merely indicating which server is available next) is a recognition of the value of queueing management techniques and of their widespread use.

I suggest that readers who want additional information on this book visit the author's website (http://www.terry-green.co.uk). I read the book in two days, squeezing time for reading between my other tasks, and I welcomed each return to reading it. I enjoyed it immensely and recommend it to my fellow queueing theorists. So, hurry to line up to buy You're Next!

Myron Hlynka

Department of Mathematics and Statistics, University of Windsor, Windsor, Ontario N9B 3P4, Canada, [email protected]

Multiple Criteria Decision Making: From Early History to the 21st Century

KÖksalan, Murat, Jyrki Wallenius, Stanley Zionts. 2011. Multiple Criteria Decision Making: From Early History to the 21st Century. World Scientific Publishing Co., Singapore. 212 pp. $72.00.

The multiple criteria decision making (MCDM) community should thank the authors of Multiple Criteria Decision Making: From Early History to the 21st Century, all of whom are well-known in this community as scientists and leaders of the field, for their decision to write a history of the field. These experts have lived with MCDM and its history for many years (Wallenius and Zionts for over 40 years). Their collective memories cover its history since the 1960s, providing a valuable book for students, researchers, and anyone who is interested in learning about MCDM's history and leaders. The biographies of the leading MCDM scholars are unusual in that they include many human stories and photos that bring MCDM history and its people to life.

The date at which MCDM history begins is unclear. The authors start with Benjamin Franklin (1706–1790) and describe how he used a simple system to consider the arguments in favor of a decision and the arguments against it. The first chapter, The Early History of MCDM, mentions famous scientists, including Cantor, Condorcet, Edgeworth, and Pareto, who are early contributors to MCDM. It also gives credit to many decision scientists and economists, such as Ramsey, Savage, Nash, Debreu, von Neumann, Morgenstern, Churchman, Arrow, Frisch, and Roy, whose research has helped to develop the MCDM field to its current state.

The roots of multiple objective mathematical programming lie in mathematical programming. The book also describes the contributions of those people whose theoretical advances play an important role in modern MCDM. These include Dantzig, Kantorovich, Gass, Kuhn, Tucker, and Koopmans. If we must mention a father or fathers of modern MCDM approaches, perhaps we should give this honor to Abraham Charnes and William Cooper who developed and published goal programming in 1961. Much later research is based essentially on their ideas.

Following the discussion of MCDM's early history, three chapters address developments in the field in the 1970s, 1980s, 1990s, and beyond, describing the most important development steps within each period. The authors use their own memories and experiences to describe the details as no outsider could.

One chapter is devoted to MCDM conferences, society traditions, awards, and presidents. Several photos make the conference descriptions enjoyable to read. However, the biographies are the best part of the book. The reader who is not interested in the MCDM history might be interested in reading about the people who made its history. The authors know all these people personally; thus, they are able to describe each person like a friend.

I hope that other people enjoy reading the book as much as I did!

Pekka Korhonen

Department of Information and Service Economy, School of Economics, Aalto University, Aalto, Finland, [email protected]

A Strategy for Using Multicriteria Analysis in Decision-Making: A Guide for Simple and Complex Environmental Projects

Munier, Nolberto. 2011. A Strategy for Using Multicriteria Analysis in Decision-Making: A Guide for Simple and Complex Environmental Projects. Springer Science + Business Media, New York. 319 pp. $179.00.

The nature of the problem the book addresses, as the author clearly states, is “a person or a group of persons, based on the available information on quantitative data, assuming values on intangible and subjective issues, identifying and estimating potential risks, and confronted most of the time with uncertainties, must decide what the most convenient solution between several options is” (p. 2). Along these lines, the author declares the aim of the book to be “to develop a methodology to allow the decision maker (DM) to make a reasonable, educated and documented decision” (p. 2).

Mentioning some points about these statements is important. The first is that the solution is almost always the one that is the most convenient in the real world. The main reasons for this are twofold: projects often suffer from a lack of data combined with an unwillingness to use a methodology because of an ongoing time constraint in projects, and the human instinct is to always choose the easiest route. The author very specifically— and in many places in the book—addresses the reality that decision making in organizations is both an art and a science. Although it involves both quantitative and qualitative data, it is a human process. Computers and algorithms are only aids for making more enlightened decisions. After all the analyses and mathematical computations have been completed, the decision is rarely fully stripped of subjectivity. The author stresses this very crucial point many times. It is the book's most salient strength.

The second important point is that the author emphasizes documenting each decision. By far, the lack of documentation is the biggest gap in corporate decision making. Decisions are made; years pass; the individuals who made the decisions move on; new employees face more complex situations in which they must make decisions that depend on the logic of previous decisions; however—and this is the key point—no documentation exists on how, why, or when these earlier decisions were made. In other words, companies are living with no long-term memory and no history. Konrad Adenauer, the late German statesman, is alleged to have said that history is the sum total of the things that could have been avoided. Munier does an excellent job of emphasizing the importance of documentation, which is the formal process in which an organization creates its own history.

Chapter 1 lists and explains the elements of a plan for decision making. The components of a project (objectives, criteria and thresholds, alternative scores, modeling, gathering information, and analysis of data and information) are investigated. The highlight of the chapter is a section, How to Approach a Problem, in which the author proposes the creation of a decision model and declares that linear programming (LP) is the book's preferred tool.

Chapter 2 elaborates on one of the most difficult steps that must take place before a decision is made: gathering and processing data. The chapter evaluates the nature of the competing alternatives, their relationships, and their analyses. It also explains in great detail criteria to use and how to assess impacts.

Chapter 3 outlines several tools that can help the decision-making process. It illustrates each methodology using an actual case, a beneficial technique to the practitioner, and critiques each method by providing its advantages and disadvantages. The author covers multiattribute utility theory (MAUT), élimination et choix traduisant la réalité (ELECTRE), the preference ranking organization method for enrichment evaluations (PROMETHEE-GAIA), the analytic hierarchy process (AHP), and the technique for order preference by similarity to ideal situation (TOPSIS). In Chapters 4 and 5, the author covers LP for a single objective and features in formulating and solving decision problems, respectively. The chapter on LP does not assume that the reader has any prior knowledge of operations research (OR) and starts from OR's fundamentals. Chapter 5, by virtue of starting with a real-life example, emphasizes the strength of LP and the wealth of information it can provide. The book is clearly biased toward LP because, as the author states, “this technique is the only one that guaranties [sic] optimal solutions” (p. 3).

Sequential interactive model for urban systems (SIMUS), which is a variation of LP that provides a satisfactory result that considers all objectives and criteria in a multiobjective, multicriteria problem—not a single optimal solution to a problem, is the subject of Chapter 6. The author uses a case study, the airport expansion plan, which Vreeker et al. (2001) originally created, to powerfully demonstrate the advantages of SIMUS. However, a potential disadvantage of this method is the probable difficulty in quantifying numerous criteria for multiple alternatives. The example starts with an already-quantified decision matrix and evolves from there. However, populating the decision matrix data values is not an easy task. Nevertheless, SIMUS does a good job of providing different perspectives on the available alternatives—not limiting itself to one correct solution.

Chapter 7 compares LP with ELECTRE, PROMETHEE, AHP, and MAUT with respect to complexity, case size, and delimiting. The author concludes that LP is the true winner of these comparisons for “multifaceted or intricate scenarios” (p. 181). The main reasons for this, the author argues, are that LP can work with thresholds, is able to establish complex relationships among alternatives, and can tackle a problem of any size. Chapter 8, which is devoted to proving this assertion, is the chapter in which the rubber meets the road for practitioners in that it provides numerous examples—real-life, complex projects in which LP can help tremendously. The examples almost exclusively originate from the Americas and—as the book's subtitle suggests—have an environmental component; however, they are simply representations of the power of the method. The reader can easily extend the use of the methodology to cases in different contexts (e.g., human resources, marketing, or pricing).

Chapter 9, the last chapter, addresses strengths, weaknesses, opportunities, and threats (SWOT) and risk analysis, which probably belong in an appendix rather than the main text. These methods can only be used as support systems in a mathematically sound, deliberate decision-making process. In addition, SWOT analysis is never a mathematical method. The chapter provides a register of 66 projects that were solved by different decision-making models and lists the author and source of each. The interested reader is invited to peruse this well-researched list and see how other practitioners and researchers worldwide have approached the problems that they faced.

Enis E. Ocal

FedEx Services, Memphis, Tennessee 38125, [email protected]