Book Reviews

Strategic Geography. By Hugh Faringdon. Routledge, London (1989), ISBN 0-415-00980-4, £40.00

Field Artillery and Firepower. By J. B. A. Bailey. The Military Press, Oxford (1989), ISBN 0-85066-810-7, £25.00 (hardback), ISBN 0-85066-811-5, £14.50 (paperback)

U.S.-Soviet Security Cooperation. Edited by Alexander L. George, Philip J. Farley and Alexander Dallin. Oxford University Press, New York (1988), ISBN 0-19-505397-4, £30.00 (hardback), $19.95 (paperback)

Non-production by Industry of Chemical-warfare Agents: Technical Verification under a Chemical Weapons Convention. Edited by S. J. Lundin. SIPRI/Oxford University Press, Oxford (1988), ISBN 0-19-829129-9, £19.50

Ireland's Terrorist Trauma: Interdisciplinary Perspectives. Edited by Alan O'Day and Yonah Alexander. Harvester Wheatsheaf, Hemel Hempstead (1989), ISBN 7450-049003, £27.50 ($48.65)

Pilots and Rebels: the Use of Aircraft in Unconventional Warfare 1918-1988. By P. A. Towle. Brassey's (UK), London (1989), ISBN 0-08-036712-7, £29.95 ($53.95)     

Rectilinear m-Center problem

In this article we consider the unweighted m-center problem with rectilinear distance. We preent an O(n^m–2 log n) algorithm for the m-center problem where m ≥ 4.     

Optimization in simulation: Current issues and the future outlook

Simulation is commonly used to find the best values of decision variables for problems which defy analytical solutions. This objective is similar to that of optimization problems and thus, mathematical programming techniques may be applied to simulation. However, the application of mathematical programming techniques, e.g., the gradient methods, to simulation is compounded by the random nature of simulation responses and by the complexity of the statistical issues involved. The literature relevant to optimization in simulation is scattered, and no comprehensive and up-to-date treatment of the subject is presently available. To that end, this article brings together numerous concepts related to t he problem of optimization in simulation. Specifically, it discusses the application of mathematical programming techniques to optimization in simulation, response surface methodology and designs, perturbation analysis, and frequency domain simulation experiments. The article provides a user with an overview of the available optimization techniues and identifies future research possibilities.     

Postoptimality analysis in integer programming by implicit enumeration: The mixed integer case

This paper develops a method for doing postoptimality analysis on the mixed integer programming problem. The proposed procedures form a natural adjunct to enumerative I.P. algorithms that are linear programming based, and they are designed, in effect, to capitalize on insights generated as the problem is initially solved to do subsequent analysis upon it. In particular, limited ranging analysis is possible on selected parameters, as is the efficient resolving of the problem following parameter changes.     

Multiple-attribute decision making with partial information: The comparative hypervolume criterion

A new approach is presented for analyzing multiple-attribute decision problems in which the set of actions is finite and the utility function is additive. The problem can be resolved if the decision makers (or group of decision makers) specifies a set of nonnegative weights for the various attributes or criteria, but we here assume that the decision maker(s) cannot provide a numerical value for each such weight. Ordinal information about these weights is therefore obtained from the decision maker(s), and this information is translated into a set of linear constraints which restrict the values of the weights. These constraints are then used to construct a polytope W of feasible weight vectors, and the subsets H_i (polytopes) of W over which each action a_i has the greatest utility are determined. With the Comparative Hypervolume Criterion we calculate for each action the ratio of the hypervolume of H_i to the hypervolume of W and suggest the choice of an action with the largest such ratio. Justification of this choice criterion is given, and a computational method for accurately approximating the hypervolume ratios is described. A simple example is provided to evaluate the efficiency of a computer code developed to implement the method.