Note: A geometrical method of solving certain games

One of the diagrammatic methods for solving two-person 2 × n matrix games can be extended to solve m × n games where each column of the matrix is a concave function of the row number. This gives a simple proof of a theorem of Benjamin and Goldman that such games have solutions involving no more than two consecutive strategies for the row player, and no more than two strategies for the column player. Two extensions are discussed. © 1994 John Wiley & Sons, Inc.     

An analytical evaluation of optimal solution value estimation procedures

The estimation of optimal solution values for large-scale optimization problems is studied. Optimal solution value estimators provide information about the deviation between the optimal solution and the heuristic solution. Some estimation techniques combine heuristic solutions with randomly generated solutions. In particular, we examine a class of jacknife-based estimators which incorporate any heuristic solution value with the two best randomly generated solution values. The primary contribution of this article is that we provide a framework to analytically evaluate a class of optimal solution value estimators. We present closed-form results on the relationship of heuristic performance, sample size, and the estimation errors for the case where the feasible solutions are uniformly distributed. In addition, we show how to compute the estimation errors for distributions other than uniform given a specific sample size. We use a triangular and an exponential distribution as examples of other distributions. A second major contribution of this article is that, to a large extent, our analytical results confirm previous computational results. In particular, the best estimator depends on how good the heuristic is, but seems to be independent of the underlying distribution of solution values. Furthermore, there is essentially an inverse relationship between the heuristic performance and the performance of any estimator. © 1994 John Wiley & Sons, Inc.     

Optimizing the allocation of components to kits in small-lot,multiechelon assembly systems

The kitting problem in multiechelon assembly systems is to allocate on-hand stock and anticipated future deliveries to kits so that cost is minimized. This article structures the kitting problem and describes several preprocessing methods that are effective in refining the formulation. The model is resolved using an optimizing approach based on Lagrangian relaxation, which yields a separable problem that decomposes into a subproblem for each job. The resulting subproblems are resolved using a specialized dynamic programming algorithm, and computational efficiency is enhanced by dominance properties devised for that purpose. The Lagrangian problem is resolved effectively using subgradient optimization and a specialized branching method incorporated in the branch-and-bound procedure. Computational experience demonstrates that the specialized approach outperforms the general-purpose optimizer OSL. The new solution approach facilitates time-managed flow control, prescribing kitting decisions that promote cost-effective performance to schedule. © 1994 John Wiley & Sons. Inc.     

Localization of optimal strategies in certain games

Assume the payoffs of a matrix game are concave in the index of the maximizing player. That player is shown to have an optimal strategy which uses at most two consecutive pure strategies, identifiable through approximate solution of a related continuous game. Generalizations are given, and the results are applied to a motivating hidden-target model due to Shapley. © 1994 John Wiley & Sons, Inc.     

Experience with prototype multicriteria decision support systems for pelagic fish quota determination

This article describes an attempt to use certain multiple-criteria decision-making aids to support the formulation of catch quota policies in pelagic fisheries management. A set of attributes was identified, as necessary to represent the decision-making goals. Potentially useful methods appeared to be STEM, interactive sequential goal programming (ISGP), and interactive multiple goal programming (IMGP). Prototype microcomputer-based systems were developed to implement each of the three approaches. Although experience with the implementation is still relatively limited, it has involved actual planners and decision makers from both the fishing industry and from the relevant state department. The evidence is that IMGP is particularly useful in this context, and that it does seem to facilitate the reaching of consensus between different interest groups.     

Partitioning and balancing for the assembly of nozzle guide vanes in gas turbine engines

The gas turbine engine is used to power many different types of commercial and military aircraft. During the scheduled maintenance of these engines, many of the turbine components are replaced. Of particular importance to us is the replacement of nozzle guide vanes in the nozzle assembly section of the engine. Individual vanes are selected from inventory to make up sets, and each set must meet certain characteristics in order to be feasible. The vanes in each set must then be sequenced in order to meet additional criteria. In this article, we give heuristics for the above partitioning and sequencing problems. Empirical analyses, using actual data from a branch of the armed services and a major engine manufacturer, are used to evaluate the proposed heuristics. The results of these analyses indicate that the heuristics are effective.     

Multiaction maintenance under markovian deterioration and incomplete state information

Much research been devoted to modeling the replacement problem under incomplete state information. Almost no work has been done on the maintenance problem under incomplete information with multiple maintenance actions that may not return the system to as good as new. We model this problem and derive structural results concerning the optimal maintenance policy. For the case where the effect of maintenance actions is state dependent, we give conditions under which the optimal policy is finitely computable. Where maintenance is state independent we show a specific structure, consisting of monotonic waiting times and constant maintenance actions, to be optimal.     

The frequency assignment problem: A solution via nonlinear programming

This paper gives a mathematical programming model for the problem of assigning frequencies to nodes in a communications network. The objective is to select a frequency assignment which minimizes both cochannel and adjacent-channel interference. In addition, a design engineer has the option to designate key links in which the avoidance of jamming due to self interference is given a higher priority. The model has a nonconvex quadratic objective function, generalized upper-bounding constraints, and binary decision variables. We developed a special heuristic algorithm and software for this model and tested it on five test problems which were modifications of a real-world problem. Even though most of the test problems had over 600 binary variables, we were able to obtain a near optimum in less than 12 seconds of CPU time on a CDC Cyber-875.