The bounded interval generalized assignment model

The bounded interval generalized assignment model is a “many-for-one” assignment model. Each task must be assigned to exactly one agent; however, each agent can be assigned multiple tasks as long as the agent resource consumed by performing the assigned tasks falls within a specified interval. The bounded interval generalized assignment model is formulated, and an algorithm for its solution is developed. Algorithms for the bounded interval versions of the semiassignment model and sources-to-uses transportation model are also discussed.     

On a single-server queue with state-dependent service

This paper discusses a class of queueing models in which the service time of a customer al a single server facility is dependent on the queue size at the onset of its service. The Laplace transform for the wait in queue distribution is derived and the utilization of the server is given when the arrival is a homogeneous Poisson process.     

Adaptive disposal models

This paper reconsiders the classical model for selling an asset in which offers come in daily and a decision must then be made as to whether or not to sell. For each day the item remains unsold a continuation (or maintenance cost) c is incurred. The successive offers are assumed to be independent and identically distributed random variables having an unknown distribution F. The model is considered both in the case where once an offer is rejected it may not be recalled at a later time and in the case where such recall of previous offers is allowed.     

Redundant spares allocation to reduce reliability costs

The problem considered here is the optimal selection of the inventory of spares for a system built from two kinds of modules, the larger of which can be connected so it performs the role of the smaller one. The optimal inventory is the least costly one which achieves a specified probability that the spares will not be exhausted over the design lifetime. For some costs and failure rates it is most economical to use the larger module for both roles, due to the resulting increase in flexibility in the deployment of a single type of spare module. Both analytical and simulation methods have been used to study this problem.     

Engineering costs and customer costs in designing product support

Product support encompasses activities undertaken by durable goods producers to ensure their customers the continued use of the product. Examples of product support elements include after the sale activities such as providing repair services and warranty programs, as well as all the activities undertaken at the design and production stage to improve the reliability of products before they reach the market. The implications of incorporating customer costs while designing product support packages are the concern of this study. We study how the parameters of support package impact the costs incurred by customers and provide insights about selecting appropriate levels of product support. We show that the engineering orientation of maximizing the product's availability ignores market characteristics, and results in a mismatch between the corporation's support package and the customer's needs. The research is intended to be a step in understanding the interaction between design engineering parameters and customer's costs. © 1993 John Wiley & Sons, Inc.     

Minimal forecast horizons in equipment replacement models with multiple technologies and general switching costs

This article considers the problem of equipment replacement in which the replacement decision at a particular time must take into account (i) the state of the existing machine in use, (ii) the available replacement alternatives at the time, (iii) the future advances in the relevant technologies with regard to the equipment under consideration, and (iv) costs of switching between different technologies. A methodology that attains minimal forecast horizons for the problem is developed. A numerical example illustrates the methodology.     

A dynamic general linear model for inference from accelerated life tests

We present a new approach for inference from accelerated life tests. Our approach is based on a dynamic general linear model setup which arises naturally from the accelerated life-testing problem and uses linear Bayesian methods for inference. The advantage of the procedure is that it does not require large numbers of items to be tested and that it can deal with both censored and uncensored data. We illustrate the use of our approach with some actual accelerated life-test data. © 1992 John Wiley & Sons, Inc.