A forward network simplex algorithm for solving multiperiod network flow problems

An optimization model which is frequently used to assist decision makers in the areas of resource scheduling, planning, and distribution is the minimum cost multiperiod network flow problem. This model describes network structure decision-making problems over time. Such problems arise in the areas of production/distribution systems, economic planning, communication systems, material handling systems, traffic systems, railway systems, building evacuation systems, energy systems, as well as in many others. Although existing network solution techniques are efficient, there are still limitations to the size of problems that can be solved. To date, only a few researchers have taken the multiperiod structure into consideration in devising efficient solution methods. Standard network codes are usually used because of their availability and perceived efficiency. In this paper we discuss the development, implementation, and computational testing of a new technique, the forward network simplex method, for solving linear, minimum cost, multiperiod network flow problems. The forward network simplex method is a forward algorithm which exploits the natural decomposition of multiperiod network problems by limiting its pivoting activity. A forward algorithm is an approach to solving dynamic problems by solving successively longer finite subproblems, terminating when a stopping rule can be invoked or a decision horizon found. Such procedures are available for a large number of special structure models. Here we describe the specialization of the forward simplex method of Aronson, Morton, and Thompson to solving multiperiod network network flow problems. Computational results indicate that both the solution time and pivot count are linear in the number of periods. For standard network optimization codes, which do not exploit the multiperiod structure, the pivot count is linear in the number of periods; however, the solution time is quadratic.     

Book Reviews

National Security Concepts of States: New Zealand. By Kennedy Graham. Taylor & Francis, London (1989), ISBN 0-8448-1614-0, £27.00

Power and Prestige in the British Army. By R.G. L. von Zugbach. Gower, Aldershot (1988), ISBN 0-566-05561-9, £22.50

The American Civil War and the Origins of Modern Warfare—Ideas, Organization, and Field Command. By Edward Hagerman. Indiana University Press, Bloomington, IN (1988), ISBN 0-253-30546-2, $37.50 (£23.29)

British Seapower and Procurement between the Wars: a Reappraisal of Rearmament. By G. A. H. Gordon. Macmillan, London (1988), ISBN 0-333-42332-1. £29.50

Armies in Low-intensity Conflict: a Comparative Analysis. Edited by David A. Charters and Maurice Tugwell. Brassey's Defence Publishers, London (1989), ISBN 0-08-036253-2, £25.00 ($45.00); Deadly Paradigms: the Failure of U.S. Counterinsurgency Policy. By Michael Shafer, Leicester University Press, Leicester (1988), ISBN 0-7185-1311-8, £28.00

British Defence Policy Striking the Right Balance. By J. Baylis. Macmillan, London (1989), ISBN 0-333-49133-5, £29.50 or £9.99     

An application of linear programming to contingency planning: A tactical airlift system analysis

A linear programming application for the selection of aircraft for a tactical airlift fleet is described.     

The fixed charge problem

The fixed charge problem is a nonlinear programming problem of practical interest in business and industry. Yet, until now no computationally feasible exact method of solution for large problems had been developed. In this paper an exact algorithm is presented which is computationally feasible for large problems. The algorithm is based upon a branch and bound approach, with the additional feature that the amount of computer storage required remains constant throughout (for a problem of any given size). Also presented are three suboptimal heuristic algorithms which are of interest because, although they do not guarantee that the true optimal solution will be found, they usually yield very good solutions and are extremely rapid techniques. Computational results are described for several of the heuristic methods and for the branch and bound algorithm.     

On computing the expected discounted return in a markov chain

The discounted return associated with a finite state Markov chain X₁, X₂… is given by g(X₁)+ αg(X₂) + α²g(X₃) + …, where g(x) represents the immediate return from state x. Knowing the transition matrix of the chain, it is desired to compute the expected discounted return (present worth) given the initial state. This type of problem arises in inventory theory, dynamic programming, and elsewhere. Usually the solution is approximated by solving the system of linear equations characterizing the expected return. These equations can be solved by a variety of well-known methods. This paper describes yet another method, which is a slight modification of the classical iterative scheme. The method gives sequences of upper and lower bounds which converge mono-tonely to the solution. Hence, the method is relatively free of error control problems. Computational experiments were conducted which suggest that for problems with a large number of states, the method is quite efficient. The amount of computation required to obtain the solution increases much slower with an increase in the number of states, N, than with the conventional methods. In fact, computational time is more nearly proportional to N², than to N³.     

A note on the optimal scheduling of two parallel processors

The problem of minimizing mean flow time of two parallel processors is discussed. Prior results are briefly reviewed. A dynamic programming algorithm is presented which minimizes mean flow time for a set of n preordered jobs on two nonequivalent parallel processors. The algorithm is illustrated with an example problem. The computational experience is presented which illustrates the efficiency of the algorithm.