Restoring Transparency to Computational Solutions. A.M. Geoffrion. Decision Modelling and Information Systems. Nikitas-Spiros Koutsoukis, Gautam Mitra (eds.). Kluwer Academic Publishers. 2003.
Computational methods in support of decision-making have grown greatly in power during recent decades owing in large measure to Moore's Law. Nothing like this law operates in the realm of mathematical models, which has led to an increasingly lopsided "mind share" in favor of computation at the expense of mathematics among decision support system developers. The increased emphasis on computational methods is a mixed blessing, for these seldom excel at revealing why the solutions they yield are what they are. Yet in many situations, decision makers and policy makers need to understand the why behind these solutions in order to convince themselves and others of the need for action or to deepen their own understanding of the system under study. This paper advocates, with two detailed illustrations, conceptually simple models, arguments, and spreadsheets as adjuncts to complex computational models to help explain important aggregate properties of detailed computational solutions. This can improve the transparency, and hence the value, of such solutions.
E-Business and Management Science: Mutual Impacts (Part 1 of 2). A.M. Geoffrion, R. Krishnan. Management Science. 49(10). 2003.
This begins a two-part commentary on management science and e-business, the theme of this two-part special issue. After explaining the topical clusters that give organization to both parts, we pose two key questions concerning the impact of the emerging digital economy on management science research: What fundamentally new research questions arise, and what kind of research enables progress on them. We sketch the papers appearing in this part from the perspective of both these questions, and offer summary comments on the first question based on the papers in both parts. The principal conclusion is that the digital economy is giving birth to new research questions in three main ways (not all independent): by enabling and popularizing several types of technology-mediated interactions, by spawning large-scale digital data sources, and by creating recurring operational decisions that need to be automated.
E-Business and Management Science: Mutual Impacts (Part 2 of 2). A.M. Geoffrion, R. Krishnan. Management Science. 49(11). 2003.
This concludes a two-part commentary on management science and e-business, the theme of this two-part special issue. After reviewing the topical clusters that give organization to both parts, we sketch the papers appearing in this second part from the perspective of two key questions concerning the impact of the emerging digital economy on management science research: What fundamentally new research questions arise, and what kind of research enables progress on them. We then offer summary comments on the second question based on the papers in both parts. The principal conclusions are that, in meeting the challenges posed by the digital economy, management science researchers are (a) making greater use of parts of economics and computer science/information technology, and (b) exploiting the improving productivity advantages of empirical and methodological work in comparison with theoretical work.
Progress in Operations Management. A.M. Geoffrion. Production and Operations Management. 11(2): 92-100. Spring 2002.
Robert Hayes (this issue) has written provocatively about the implications of the digital economy for Operations Management (O.M.). Here I examine and illustrate a simple 4-stage framework for thinking about these implications: advances in digital technology (Stage 1) lead to business developments (Stage 2), which impact the views of O.M.-relevant thought leaders (Stage 3), which influence the conduct of O.M. activities (Stage 4). This framework provides a perspective for viewing the evolution of O.M. and indicates how educators, researchers, and practitioners can steal the march on mainstream thinking.
Prospects for Operations Research in the E-Business Era. A.M. Geoffrion, R. Krishnan. Interfaces. 31(2): 6-36. March-April 2001.
The digital economy is creating abundant opportunities for operations research (OR) applications. Several factions of the profession are beginning to respond aggressively, leading to notable successes in such areas as financial services, electronic markets, network infrastructure, packaged OR-software tools, supply-chain management, and travel-related services. Because OR is well matched to the needs of the digital economy in certain ways and because certain enabling conditions are coming to pass, prospects are good for OR to team with related analytic technologies and join information technology as a vital engine of further development for the digital economy. OR professionals should prepare for a future in which most businesses will be e-businesses.
Introduction: Operations Research in the E-Business Era. A.M. Geoffrion, R. Krishnan. Interfaces. 31(2): 1-5. March-April 2001.
This is the guest editors' introduction to a special issue on OR's accomplishments and potential in the digital economy.
A New Horizon for OR/MS. A.M. Geoffrion. INFORMS Journal on Computing. Fall 1998.
The Web creates important OR/MS opportunities, and the Bhargava-Krishnan feature article does an outstanding job of sketching some of them and of explaining the technicalities of how the Web can be used in the service of OR/MS. This commentary discusses briefly two points made in the article, and then calls attention to a new domain for OR/MS applications. This domain arises from a new and potentially large Web clientele for OR/MS that seems to have escaped notice to date: ordinary people. Satisfying this new clientele requires exactly the technologies explained by Bhargava and Krishnan. Doing so would pump new vigor into the field, directly benefit many people who use the Web, and help solve the long-standing public visibility problems of the field.
Structured Modeling. A.M. Geoffrion. Encyclopedia of Operations Research & Management Science. S.I. Gass, C.S. Harris (eds.). Kluwer Academic Publishers. 1996 (revised 06/99 for the Millennium Edition, 2001).
Structured modeling was developed as a comprehensive response to perceived shortcomings of modeling systems available in the 1980s. It is a systematic way of thinking about models and their implementations, based on the idea that every model can be viewed as a collection of distinct elements, each of which has a definition that is either primitive or based on the definition of other elements in the model. Elements are categorized into five types (so-called primitive entity, compound entity, attribute, function, and test), grouped by similarity into any number of classes called genera, and organized hierarchically as a rooted tree of modules so as to reflect the model's high-level structure. It is natural to diagram the definitional dependencies among elements as arcs in a directed acyclic graph. Moreover, this dependency graph can be computationally active because every function and test element has an associated mathematical expression for computing its value.
Using a model for any specific purpose involves subjective intentions. Structured modeling makes a sharp distinction between the resulting user-defined "problems" or "tasks" associated with a model, and the relatively objective model per se. A typical problem or task has to do with ad hoc query, drawing inferences, evaluating model behavior with specified inputs, determining a constrained solution, or optimization, and requires applying a computerized model manipulation tool ("solver"). For certain recurring kinds of problems and tasks, these tools are highly developed and readily available for incorporation into a structured modeling software system.
The theoretical foundation of structured modeling is formalized in Geoffrion (1989), which presents a rigorous semantic framework that deliberately avoids committing to a representational formalism. The framework is "semantic" because it casts every model as a system of definitions styled to capture semantic content. Ordinary mathematics, in contrast, typically leaves more of the meaning implicit. Twenty-eight definitions and eight propositions establish the notion of model structure at three levels of detail (so-called elemental, generic, and modular structure), the essential distinction between model class and model instance, certain related concepts and constructs, and basic theoretical properties. This framework has points in common with certain ideas found in the computer science literature on knowledge representation, programming language design, and semantic data modeling, but is designed specifically for modeling as practiced in OR/MS and related fields.
Twenty Years of Strategic Distribution System Design: An Evolutionary Perspective. A.M. Geoffrion, R.F. Powers. Interfaces. 25(5): 105-127. September-October 1995.
Using optimization to design distribution systems became technically feasible a little more than two decades ago, and developments have occurred at a rapid rate ever since. These developments can be understood in terms of six evolutionary processes. Four are core: evolution of algorithms, data developments tools, model features and software capabilities, and how companies actually use software for designing distribution systems. The other two are environmental: evolution of logistics as a corporate function and of computer and communications technology.
Distribution System Design. A.M. Geoffrion, J. Morris, S. Webster. Facility Location: A Survey of Applications. Z. Drezner (ed.). Springer-Verlag, New York. 1995.
This paper contains a detailed case study in the form of a series of fictional but realistic memos between a VP Logistics and his Director of Management Science over redesign of the company's network of distribution centers. This is followed by an exposition of some diagnostic tools and some optimization tools, and by an annotated bibliography.