Book Reviews

Strategic Air Defense. Edited by Stephen J. Cimbala. Scholarly Resources, Wilmington, DE (1989), ISBN 0–8420–2285–6, $40.00

NATO's Defence of the North. Brassey's Atlantic Commentaries No. 1. Edited by Eric Grove. Brassey's, London (1989), ISBN 0–08–037339–9, £7.50

Maritime Strategy and the Balance of Power: Britain and America in the Twentieth Century. Edited by John B. Hattendorf and Robert S. Jordan. Macmillan, London (1989), ISBN 0–333–43789–6, £45.00

Superpowers at Sea: an Assessment of the Naval Arms Race. By Richard Fieldhouse and Shunji Taoka. SIPRI, Oxford (1989), ISBN 0–19–829135–3

Security at Sea: Naval Arms Control. Edited by Richard Fieldhouse. Oxford University Press, Oxford (1990), ISBN 0–19–829130–2, £25.00

Strategy in the Southern Oceans: a South American View. By Virginia Gamba‐Stonehouse. Pinter, London (1989), ISBN 0–86187–017–4, £30.00

The Defence Industrial Base and the West. Edited by D. G. Haglund. Routledge, London (1989), ISBN 0–415–00923–5, £30.00

Defense and Détente: US and West German Perspectives on Defense Policy. Edited by Joseph I. Coffey and Klaus von Schubert. Westview Press, Boulder, CO, ISBN 0–8133–7722–6, $36.50     

A note on the “value” of bounds on EVPI in stochastic programming

The existing literature concentrates on determining sharp upper bounds for EVPI in stochastic programming problems. This seems to be a problem without an application. Lower bounds, which we view as having an important application, are only the incidental subject of study and in the few instances that are available are obtained at an extremely high cost. In order to suggest a rethinking of the course of this research, we analyze the need for bounds on EVPI in the context of its significance in decision problems.     

Parameter estimation for the EOQ lot-size model: Minimax and expected value choices

This article provides formulas for estimating the parameters to be used in the basic EOQ lot-size model. The analysis assumes that the true values of these parameters are unknown over known ranges and perhaps nonstationary over time. Two measures of estimator “goodness” are derived from EOQ sensitivity analysis. Formulas are given for computing the minimax choice and the minimum expected value choice for the parameter estimates using both measures of estimator “goodness”. A numerical example is included.     

Optimal ordering policies when anticipating parameter changes in EOQ systems

The classical Economic Order Quantity Model requires the parameters of the model to be constant. Some EOQ models allow a single parameter to change with time. We consider EOQ systems in which one or more of the cost or demand parameters will change at some time in the future. The system we examine has two distinct advantages over previous models. One obvious advantage is that a change in any of the costs is likely to affect the demand rate and we allow for this. The second advantage is that often, the times that prices will rise are fairly well known by announcement or previous experience. We present the optimal ordering policy for these inventory systems with anticipated changes and a simple method for computing the optimal policy. For cases where the changes are in the distant future we present a myopic policy that yields costs which are near-optimal. In cases where the changes will occur in the relatively near future the optimal policy is significantly better than the myopic policy.     

Interval estimation of a global optimum for large combinatorial problems

Consider an “intractable” optimization problem for which no efficient solution technique exists. Given a systematic procedure for generating independent heuristic solutions, we seek to obtain interval estimates for the globally optimal solution using statistical inference. In previous work, accurate point estimates have been derived. Determining interval estimates, however, is a considerably more difficult task. In this paper, we develop straightforward procedures which compute confidence intervals efficiently in order to evaluate heuristic solutions and assess deviations from optimality. The strategy presented is applicable to a host of combinatorial optimization problems. The assumptions of our model, along with computational experience, are discussed.     

Robustness of preallocated preferential defense with assumed attack size and perfect attacking and defending weapons

The problem is to protect a set of t targets by n perfect interceptors against an attack by m perfect weapons. If the defender solves for an optimal preallocated preferential defense and associated game value assuming m₁ attackers, and the attacker knows the assumption of the defender and utilizes m₂ attackers, he may be able to achieve significantly more damage than had the defender assumed that there would be m₂ attackers. The article treats the robustness of preallocated preferential defense to assumptions about the size of the attack and presents results of an alternative approach.