A computation procedure for the exact solution of location-allocation problems with rectangular distances

A method is presented to locate and allocate p new facilities in relation to n existing facilities. Each of the n existing facilities has a requirement flow which must be supplied by the new facilities. Rectangular distances are assumed to exist between all facilities. The algorithm proceeds in two stages. In the first stage a set of all possible optimal new facility locations is determined by a set reduction algorithm. The resultant problem is shown to be equivalent to finding the p-median of a weighted connected graph. In the second stage the optimal locations and allocations are obtained by using a technique for solving the p-median problem.     

On the treatment of force-level constraints in times-equential combat problems

The treatment of force-level constraints in time-sequential combat optimization problems is illustrated by further studying the fire-programming problem of Isbell and Marlow. By using the theory of state variable inequality constraints from modern optimal control theory, sharper results are obtained on necessary conditions of optimality for an optimal fire-distribution policy (in several cases justifying conjectures made in previous analysis). This leads to simplification of the determination of the domains of controllability for extremals leading to the various terminal states of combat. (Additionally, some new results for the determination of boundary conditions for the adjoint variables in optimal control problems with state variable inequality constraints have arisen from this work.) Some further extensions of previous analysis of the fire-programming problem are also given. These clarify some key points in the solution synthesis. Some important military principles for target selection and the valuation of combat resources are deduced from the solution. As a result of this work, more general time-sequential combat optimization problems can be handled, and a more systematic solution procedure is developed.     

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.     

Book reviews

Third World Military Expenditure: Determinants and Implications. By Robert McKinlay. Frances Pinter, London (1989)

The UK Defence Industrial Base: Development and Future Policy Options. By Trevor Taylor and Keith Hayward. Brassey's, London, for Royal United Services Institute (1989), ISBN 0-08-036713-5, £22.50

Mutiny. By Lawrence James. Buchan & Enright, London (1987), ISBN 0-907675-70-0, £12.95; Scapegoat! Famous Courts Martial. By John Harris. Severn House, London (1988), ISBN 0-7278-2103-2, £12.95; In Glass Houses. By Robert Boyes. Military Provost Staff Corps Association, Colchester (1986), ISBN 0-9513467-0-9, £6.50 (paperback)

The Nuclear Weapons World: Who, How and Where. Edited by Patrick Burke. Frances Pinter, London (1988), ISBN 086187-705-5, £50.00

Merchants of Treason—America's Secrets for Sale. By Thomas B. Allen and Norman Polmar. Robert Hale, London (1988), ISBN 0-7090-3543-8, £14.95 ($21.95); Intelligence and Intelligence Policy in a Democratic Society. Edited by Stephen J. Cimbala. Transnational Publishers, Dobbs Ferry, NY (1987), ISBN 0-941320-44-8, $37.50; Catching Spies—Principles and Practices of Counterespionage. By H. H. A. Cooper and Lawrence J. Redlinger. Paladin Press, Boulder, CO (1988), ISBN 0-87364-466-2, $24.95

The BattleshipRoyal Sovereignand Her Sister Ships. By Peter C. Smith. William Kimber, Wellingborough (1988), ISBN 0-7183-0704-6, £12.95; Air Power at Sea, 1945 to Today. By John Winton. Sidgwick & Jackson, London (1987), £9.95     

The inventory packing problem

We study the problem of finding the minimum number of identical storage areas required to hold n items for which demand is known and constant. The replenishments of the items within a single storage area may be time phased so as to minimize the maximum total storage capacity required at any time. This is the inventory-packing problem, which can be considered as a variant of the well-known bin-packing problem, where one constraint is nonlinear. We study the worst-case performance of six heuristics used for that earlier problem since the recognition version of the inventory-packing problem is shown to be NP complete. In addition, we describe several new heuristics developed specifically for the inventory-packing problem, and also study their worst-case performance. Any heuristic which only opens a bin when an item will not fit in any (respectively, the last) open bin needs, asymptotically, no more than 25/12 (resp., 9/4) times the optimal number of bins. Improved performance bounds are obtainable if the range from which item sizes are taken is known to be restricted. Extensive computational testing indicates that the solutions delivered by these heuristics are, for most problems, very close to optimal in value.     

A multi-item EOQ model with inventory cycle balancing

This article concerns a multi-item, infinite-horizon, lot-sizing problem, where the objective is to minimize a total cost function made up of reordering cost, holding cost, and a cost determined by peak inventory levels. By spreading inventory replenishments over the reordering cycle, the peak inventory level can be reduced. The model permits the derivation of simultaneously optimal solutions for the length of the cycle and the individual item replenishment times within the cycle. An alternative formulation, in which total storage capacity is modeled as a constraint, is also solved.     

Scheduling on a single machine with a single breakdown to minimize stochastically the number of tardy jobs

Jobs with known processing times and due dates have to be processed on a machine which is subject to a single breakdown. The moment of breakdown and the repair time are independent random variables. Two cases are distinguished with reference to the processing time preempted by the breakdown (no other preemptions are allowed): (i) resumption without time losses and (ii) restart from the beginning. Under certain compatible conditions, we find the policies which minimize stochastically the number of tardy jobs.