Book Reviews

Men, Ideas and Tanks: British Military Thought and Armoured Forces, 1903–1939. By J. P. Harris, Manchester University Press, (1995) ISBN 0 7190 3762 (hardback) £40.00 or ISBN 0 7190 4814 (paperback) £14.99

Fighting for Ireland. By M. L. R. Smith. London and New York: Routledge, (1995) ISBN 0–415–09161–6.

The Fundamentals of British Maritime Doctrine (BR1806) HMSO London (1995) ISBN 0–11–772470‐X £9.50

Regional Conflicts: The Challenges to US‐Russian Co‐Operation Edited by James E. Goodby SIPRI: Oxford University Press 1995 ISBN 019‐S29–171X, £30.00

SIPRI Yearbook 1995 ‐ Armaments, Disarmament and International Security Oxford: Oxford University Press 1995. ISBN 019–829–1930, £60.00.

Drug Trafficking in the Americas Edited by Bruce M. Bagley & William O. Walker III Transaction Publishers, New Brunswick, (USA), 1994 ISBN 1–56000–752–4.

Raglan: From the Peninsula to the Crimea By John Sweetman, Arms & Armour 1993. ISBN 1–85409–059–3. £19.00.     

Pseudo-monotonic interval programming

A pseudo-monotonic interval program is a problem of maximizing f(x) subject to x ε X = {x ε Rⁿ | a < Ax < b, a, b ε R^m} where f is a pseudomonotonic function on X, the set defined by the linear interval constraints. In this paper, an algorithm to solve the above program is proposed. The algorithm is based on solving a finite number of linear interval programs whose solutions techniques are well known. These optimal solutions then yield an optimal solution of the proposed pseudo-monotonic interval program.     

Some approximations in multi-item,multi-echelon inventory systems for recoverable items

The optimization problem as formulated in the METRIC model takes the form of minimizing the expected number of total system backorders in a two-echelon inventory system subject to a budget constraint. The system contains recoverable items – items subject to repair when they fail. To solve this problem, one needs to find the optimal Lagrangian multiplier associated with the given budget constraint. For any large-scale inventory system, this task is computationally not trivial. Fox and Landi proposed one method that was a significant improvement over the original METRIC algorithm. In this report we first develop a method for estimating the value of the optimal Lagrangian multiplier used in the Fox-Landi algorithm, present alternative ways for determining stock levels, and compare these proposed approaches with the Fox-Landi algorithm, using two hypothetical inventory systems – one having 3 bases and 75 items, the other 5 bases and 125 items. The comparison shows that the computational time can be reduced by nearly 50 percent. Another factor that contributes to the higher requirement for computational time in obtaining the solution to two-echelon inventory systems is that it has to allocate stock optimally to the depot as well as to bases for a given total-system stock level. This essentially requires the evaluation of every possible combination of depot and base stock levels – a time-consuming process for many practical inventory problems with a sizable system stock level. This report also suggests a simple approximation method for estimating the optimal depot stock level. When this method was applied to the same two hypotetical inventory systems indicated above, it was found that the estimate of optimal depot stock is quite close to the optimal value in all cases. Furthermore, the increase in expected system backorders using the estimated depot stock levels rather than the optimal levels is generally small.     

Renewal processes of phase type

This paper discusses a class of analytically and numerically tractable renewal processes, which generalize the Poisson process. When used to describe interarrival or service times in queues, these renewal processes lead to computationally explicit solutions which involve only real arithmetic. Previous modifications of the Poisson process, based on the Erlang or the hyperexponential distributions, appear as particular cases.     

Maximizing the sum of certain quasiconcave functions using generalized benders decomposition

In this paper we consider the problem of maximizing the sum of certain quasi-concave functions over a convex set. The functions considered belong to the classes of functions which are known as nonlinear fractional and binonlinear functions. Each individual function is quasi-concave but the sum is not. We show that this nonconvex programming problem can be solved using Generalized Benders Decomposition as developed by Geoffrion.