Modern Digital Simulation Methodology II
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#1998 Operational Research Society Ltd. All rights reserved. 0160-5682/98 $12.00 http://www.stockton-press.co.uk/jor
Book Selection Edited by JM Wilson EJ Dudewicz (Ed.): Modern Digital Simulation Methodology II P Kouvelis and G Yu: Robust Discrete Optimization and its Applications D Cieslik: Steiner Minimal Trees WG Wojtkowski, W Wojtkowski, S Wrycza and J Zupancic (Eds.): System Development Methods for the Next Century
Modern Digital Simulation Methodology II EJ Dudewicz (Ed) American Sciences Press, Syracuse, New York, 1997. 231 pp. $135.00. ISBN 0 935950 42 7 The title of this book suggests that it will be of interest to many in OR, but I suspect they will be disappointed. The book consists of four long papers of a technical nature and would only interest a few specialists. The ®rst paper, by Dudewicz and Karian, provides many pages of tables of the extended generalised lambda distribution. The second paper, again by Dudewicz and a co-author, provides numerous pictures of the bivariate generalised lambda distribution. The third paper, by Wright and Bates, investigates Monte Carlo methods on mass enumeration studies. The ®nal paper, by Sun and MuÈller-Schwarze, provides a case study on beaver dispersal patterns and includes a listing of a bootstrap/ jacknife program. The book is a special issue of the American Journal of Mathematical and Management Studies, so the intention of the volume may always have been different from that conveyed by the title. Loughborough University
JM Wilson
Robust Discrete Optimization and its Applications P Kouvelis and G Yu Kluwer Academic Publishers, London, 1997. xvi 356 pp. £119.00. ISBN 0 7923 4291 7 This book collates research carried out by the two authors and colleagues over the past few years. Its aim is to introduce the concept of Robust Discrete Optimization (RDO) to a readership spanning from the graduate student to the OR/MS practitioner.
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Unlike deterministic optimisation models which rely on the assumption that the most likely scenario will certainly occur, RDO acknowledges that real-life problems involve considerable uncertainty. However, rather than assigning probabilities to different scenarios with a view to generating a decision which is optimal in the long run (as in stochastic optimisation approaches), RDO considers a set of potentially realisable scenarios. Use of min±max criteria is then made to select a decision that has the best worst-case performance over the scenario set. As the authors point out, the approach is particularly suitable for decisions of a non-repetitive nature where the decision-makers are riskaverse and/or where decisions are evaluated against the realised scenario. The material in the book is organised as follows. Chapter 1 introduces the robustness approach to decision-making under uncertainty, mainly by comparing it against its traditional counterparts; that is, deterministic and stochastic optimisation. The merit of the approach is illustrated clearly using a simple example from production scheduling. After intr
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