Degree Type

Thesis

Date of Award

2015

Degree Name

Master of Science

Department

Industrial and Manufacturing Systems Engineering

First Advisor

Sarah M. Ryan

Abstract

Environmental, social and economic concerns motivate the operation of closed-

loop supply chain networks (CLSCN) in many industries. We propose a novel profit

maximization model for CLSCN design as a mixed-integer linear program in which there is flexibility in covering the proportions of demand satisfied and returns collected based on the firm's policies. Our major contribution is to develop a novel hybrid robust-stochastic programming (HRSP) approach to simultaneously model two different types of uncertainties by including stochastic scenarios for transportation costs and polyhedral uncertainty sets for demands and returns. Transportation cost scenarios are generated using a Latin Hypercube Sampling method and scenario reduction is applied to consolidate them. An accelerated stochastic Benders decomposition algorithm is proposed for solving this model. To speed up the convergence of this algorithm, valid inequalities are introduced to improve the quality of lower bound, and also a Pareto-optimal cut generation scheme is used to strengthen the Benders optimality cuts.

Numerical studies are performed to verify our mathematical formulation and also demonstrate the benefits of the HRSP approach. The performance improvements achieved by the valid inequalities and Pareto-optimal cuts are demonstrated in randomly generated instances.

DOI

https://doi.org/10.31274/etd-180810-4106

Copyright Owner

Esmaeil Keyvanshokooh

Language

en

File Format

application/pdf

File Size

76 pages

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