Abstract

We propose easy-to-implement heuristics for a problem referred to in the literature as the facility location problem with immobile servers, stochastic demand, and congestion, or the service system design problem. The problem is typically posed as one of allocating capacity to a set of M/M/1 queues to which customers with stochastic demand are assigned with the objective of minimizing a cost function composed of a fixed capacity-acquisition cost, a variable customer-assignment cost, and an expected-waiting-time cost. The expected-waiting-time cost results in a non-linear term in the objective function of the standard binary programming formulation of the problem. Thus, the solution approaches proposed in the literature are either sophisticated linearization or relaxation schemes, or metaheuristics. In this study we demonstrate that an ensemble of straight-forward, greedy heuristics can find high-quality solutions in less than one second of CPU time. In addition to filling the gap in the literature using the heuristic, a noniterating linear model was also developed and the existing iterating linear model was tested using various stopping criteria while also examining the affects of additional constraints on the problem similar to issues being discussed in current literature. In many cases, our heuristic approach finds solutions of the same or better quality than those found with expensive, state-of-the art mathematical programming software, in particular a commercial non-linear MIP solver given a five-minute time limit.

Degree Date

Fall 2022

Document Type

Thesis

Degree Name

Ph.D.

Department

Operations Research and Engineering Management

Advisor

Eli Olinick

Second Advisor

Sila Cetinkaya

Third Advisor

Richard barr

Fourth Advisor

Gheorghe Spiride

Fifth Advisor

Michael Hahsler

Subject Area

Computer Engineering

Format

.pdf

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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