Uncapacitated plant location-allocation problems with price sensitive stochastic demands

doi:10.1016/0305-0548(88)90011-1

Computers & Operations Research

Volume 15, Issue 2, 1988, Pages 189-198

https://doi.org/10.1016/0305-0548(88)90011-1 Get rights and content

Abstract

A quantitative model that focuses on maximizing the expected net profits has been developed for an uncapacitated transportation plant location-allocation problem in the presence of price sensitive stochastic demands. Based upon the results obtained from this research, it has been determined that a good heuristic that produces near optimal solutions within reasonable limits of percentage deviation is required for solving medium and large size problems. Reasonable computation time has been found to be another factor supportive of the heuristic algorithm.

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The first category is the stochastic facility location problems which deal with the cases when the uncertain parameters, like customers’ demands and operating costs of plants, are characterized by random variables. For instance, Logendran and Terrell [22] developed a stochastic uncapacitated transportation plant location-allocation model with the objective of maximizing the expected profits, and they proposed some heuristics to solve the problem. Louveaux and Peeters [23] discussed a dual-based procedure for a stochastic facility location problem with recourse.
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^†: Rasaratnam Logendran is Assistant Professor in the Department of Industrial Engineering at Southern Illinois University at Edwardsville. He received his Bachelor of Science Degree in mechanical engineering from the University of Sri Lanka, a Master of Engineering Degree in industrial engineering and management from the Asian Institute of Technology, Thailand and his Ph.D. in industrial engineering and management from Oklahoma State University. Dr Logendran's primary areas of interest are applications of operations research and simulation in industrial engineering.

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Uncapacitated plant location-allocation problems with price sensitive stochastic demands

Abstract

An analysis of private and public sector location models

Mgmt Sci.

Plant location-allocation problems with price sensitive stochastic demands

Facility location with price-sensitive demands: private, public, and quasi-public

Mgmt Sci.

Allocation under uncertainty when the demand has continuous D.F.

Mgmt Sci.

Integer Programming Theory, Applications, and Computations