Identifying and ranking a most preferred subset of alternatives in the presence of multiple criteria

In this article an interactive method is developed to identify and rank a most preferred subset, T, of alternatives assuming that the decision maker has an implicit quasiconcave nondecreasing utility function. The method requires the decision maker to compare pairs of selected alternatives. Based on the responses of the decision maker, convex cones are constructed to eliminate alternatives that are proved to be inferior to alternatives in set T. The method aims at keeping the number of pairwise comparisons small. Computational experience with the method indicates that the required number of pairwise comparisons to form set T is usually small. However, the number of pairwise comparisons needed to confirm that this set is best may be large.     

On the number of games played in a best of (2n – 1) series

Let p(⩾0.5) denote the probability that team A beats B in a single game. The series continues until either A or B wins n games. Assuming that these games are independent replications, we study some features of the distribution of X_n, the number of games played in the series. It is shown that X_n is unimodal, has an IFRA distribution, and is stochastically decreasing in p. Close approximations to its mode, mean, and variance are given. Finally, it is shown that the maximum-likelihood estimator of p based on X_n is unique.     

Estimation and prediction in a semi-markov model for evasive vehicle movement along a fixed path

In Turner and Holmes [8] a model for evasive vehicle movement along a fixed track is developed within the mathematical framework of a two-state semi-Markov process. They derive a number of analytical properties of the model. In this article we address problems concerning the estimation of parameters in the model and the construction of data-based prediction equations.     

Mixture selection for maximal stochastic performance

Methodology for the optimal selection of a mixture of components for the attainment of a required performance level is developed. This accommodates both cost and stochastic variation and is applied to gasoline blending. © 1994 John Wiley & Sons, Inc.     

Sole sourcing versus dual sourcing under stochastic demands and lead times

The use of a single vendor for each inventoried item is usually assumed in most of the inventory models. However, there are situations where the use of more than one vendor should be considered, especially when lead times are stochastic. This research presents a theoretical investigation of the effect of cost structures on the relative performance of sole-sourcing versus dual-sourcing inventory control policies. We show that except for cases where the ordering cost is high, the lead-time variability is low, or the customer service level is low, dual sourcing performs better than sole sourcing under the normally distributed demand and shifted-exponential lead times. Moreover, the computational results indicate the dual sourcing provides a better service level than sole sourcing at the optimal solutions, and that dual sourcing results in larger order quantities than sole sourcing, which suggests that attractive quantity discounts may not be in jeopardy when dual sourcing is employed. Finally, because it is generally known that multiple sourcing can enhance the competition among suppliers, material managers should consider splitting purchase orders when two equally qualified suppliers are available. © 1994 John Wiley & Sons, Inc.     

Properties of a three-dimensional estimator

A large-scale simulation was conducted to investigate the statistical properties and the suitability of an estimator that estimates the coordinates of a three-dimensional source, according to the angles at which several observers view the source. The estimator uses a heuristical averaging method of estimates obtained from the solution of six two-dimensional problems that utilize line-to-point transformations. The estimator was found to be approximately unbiased with low standard deviations for normal error distributions. An effective and easy-to-use forecasting formula was developed to forecast the parameters of the distributions of the estimates. © 1994 John Wiley & Sons, Inc.     

Note: Pairwise rearrangements in reliability structures

Boland, Proschan, and Tong [2] used the notion of criticality of nodes in a coherent system to study the optimal component arrangement of reliability structures. They also provided a sufficient minimal cut (path) based criterion for verifying the criticality ordering of two nodes. We develop a necessary and sufficient condition for two nodes to be comparable and provide specific examples illustrating our result's applicability. As a corollary, certain optimal arrangement properties of well-known systems are derived. © 1994 John Wiley & Sons, Inc.     

The reliability of a series system of repairable subsystems: A bayesian approach

Consider the problem of estimating the reliability of a series system of (possibly) repairable subsystems when test data and historical information are available at the component, subsystem, and system levels. Such a problem is well suited to a Bayesian approach. Martz, Waller, and Fickas [Technometrics, 30 , 143–154 (1988)] presented a Bayesian procedure that accommodates pass/fail (binomial) data at any level. However, other types of test data are often available, including (a) lifetimes of nonrepayable components, and (b) repair histories for repairable subsystems. In this article we describe a new Bayesian procedure that accommodates pass/fail, life, and repair data at any level. We assume a Weibull model for the life data, a censored Weibull model for the pass/fail data, and a power-law process model for the repair data. Consequently, the test data at each level can be represented by a two-parameter likelihood function of a certain form, and historical information can be expressed using a conjugate family of prior distributions. We discuss computational issues, and use the procedure to analyze the reliability of a vehicle system. © 1994 John Wiley & Sons, Inc.     

A stochastic optimization model for planning capacity expansion in a service industry under uncertain demand

A stochastic optimization model for capacity expansion for a service industry that incorporates uncertainty in future demand is developed. Based on a weighted set of possible demand scenarios, the model generates a recommended schedule of capacity expressions, and calculates the resulting sales under each scenario. The capacity schedule specifies the size, location, and timing of these expansions that will maximize the company's expected profit. The model includes a budget constraint on available resources. By using Lagrangian relaxation and exploiting the special nested knapsack structure in the sub-problems, an algorithm was developed for its solution. Based on the initial computational results, this algorithm appears to be more efficient than linear programming for this special problem. © 1994 John Wiley & Sons, Inc.