Journal of the Operational Research Society

Cook, Wade D.; Kress, Moshe

doi: 10.1057/jors.1990.178pmid: N/A

AbstractThe problem of ranking players in a tournament has been studied by a number of authors. The methods developed for ranking players fall under two general headings—direct methods and rating methods. The present paper extends the tournament ranking problem in two directions. First, the usual definition of a tournament is broadened to include ties or draws. Thus, our model determines the best weak ranking of the players. Second, the ranking method presented takes account of player strength in that wins over strong players are valued higher than wins over weak players. To account for player strength, we evaluate both direct or first-order wins of players over opponents (i defeats j) and indirect or higher-order wins (i defeats k, who defeats j). A model which derives a composite score for each player, combining both direct and indirect wins, is used to obtain an overall ranking of the competitors.

Martel, Jean-Marc; Aouni, Belaïd

doi: 10.1057/jors.1990.179pmid: N/A

AbstractMany algorithms have been developed for multiple-criteria decision-making problems. Goal programming (GP) is one of these algorithms. This model is a special extension of linear programming. Usually, it is not easy for the decision-maker to choose his aspiration levels a priori. Moreover, the incommensurability of the measurement units of the various objectives creates an aggregation problem. However, in the standard GP formulation, the decision-maker is not required to arbitrate among conflicting objectives. To deal with these difficulties, we explicitly introduce the structure of the decision-maker's preferences into the GP model in order to evaluate the impact of deviations from the decision-maker's aspirations levels. Easily and naturally, the idea of a generalized criterion, as introduced in the Promethee outranking method, will be used to build this structure of preferences.

Saliby, Eduardo

doi: 10.1057/jors.1990.180pmid: N/A

AbstractAlthough simple random sampling is the standard sampling procedure in Monte Carlo simulation, such practice is questioned in this paper. In any Monte Carlo application, sampled distributions are assumed to be known. Using simple random sampling, sample histograms or, equivalently, sample moments will vary at random, thus producing an imprecise description of the known input distribution, and consequently increasing the variance of simulation estimates. This problem can be avoided with descriptive sampling, here proposed as a more appropriate approach in Monte Carlo simulation than simple random sampling. Descriptive sampling is based on a deterministic and purposive selection of the sample values—in order to conform as closely as possible to the sampled distribution—and the random permutation of these values. As such, it represents a fundamental conceptual change in Monte Carlo sampling, departing from the ‘principle’ that sample values must be randomly generated in order to describe random behaviour. The basis of this new idea, examples of its use and empirical results are presented.

Urli, Bruno; Nadeau, Raymond

doi: 10.1057/jors.1990.181pmid: N/A

AbstractNumerous multiobjective linear programming (MOLP) methods have been proposed in the last two decades, but almost all for contexts where the parameters of problems are deterministic. However, in many real situations, parameters of a stochastic nature arise. In this paper, we suppose that the decision-maker is confronted with a situation of partial uncertainty where he possesses incomplete information about the stochastic parameters of the problem, this information allowing him to specify only the limits of variation of these parameters and eventually their central values. For such situations, we propose a multiobjective stochastic linear programming methodology; it implies the transformation of the stochastic objective functions and constraints in order to obtain an equivalent deterministic MOLP problem and the solving of this last problem by an interactive approach derived from the STEM method. Our methodology is illustrated by a didactical example.

Wang, K.-H.

doi: 10.1057/jors.1990.182pmid: N/A

AbstractThis paper studies the machine-repair problem with two different types of a single service station breakdown under steady-state conditions. In type 1, the service station can break down only when there is at least one failed machine in the system, while in type 2, the station can break down even if no failed machines are in the system. A profit model is developed in order to determine the optimal value of the number of machines to be assigned to the service station for both types. Numerical results are provided in which several system characteristics are evaluated under optimal operating conditions.

Wei, Jerry C.; Olson, David L.; White, Edna M.

doi: 10.1057/jors.1990.183pmid: N/A

AbstractA challenging problem in process control is the selection of input levels which will produce desirable output quality. This problem is complicated by the unsure relationships of cause and effect and by the trade-offs between meeting conflicting output specifications. This paper proposes a new approach, which incorporates prediction-interval constraints into a goal-programming model. A process-control problem, originally solved by the desirability-function approach, is solved using this new model. Comparisons between the two approaches are discussed.

Goyal, S. K.; Gupta, Omprakash K.

doi: 10.1057/jors.1990.184pmid: N/A

AbstractIn this paper we consider the problem of determining the optimal purchase quantity when the supplier offers a discount on large purchases. We propose a simple approach which requires fewer EOQ calculations for determining the lot size.

Wajiga, G.; Adekola, O. A.

doi: 10.1057/jors.1990.185pmid: N/A

AbstractEvery life faces a certain age-specific mortality risk. Changing this mortality risk for a life aged A by a small quantity R, say, would change the life expectancy by R x K, where K is some constant. Kamerud has proposed a model for K which is independent of R. This note offers an improvement to Kamerud's model which depends on both A and R.