Date of Award
Doctor of Philosophy
Industrial and Entrepreneurial Engineering and Engineering Management
James Burns, Ph.D.
Azim Houshyar, Ph.D.
Timothy J. Greene, Ph.D.
Nasrin Mohabbati, Ph.D.
Multi-objective optimization, resilience, stochastic programming, supply chain network design, sustainability
Supply chain resilience (SCRES) and sustainable supply chain management (SSCM) have garnered significant attention in the field of Supply Chain Management (SCM) science. This heightened interest is largely attributable to the increasing frequency and severity of uncertainty and disruptions in global supply chains. Resilience is a beneficial approach to managing uncertainties and preparing for and mitigating the adverse consequences of Supply Chain (SC) disruptions. Sustainability is a key concept in SCM, driven by governmental regulations and heightened stakeholder and customer concerns for environmental and societal well-being. Studies on SCRES have traditionally focused on preparing for and reacting to disruptions and neglected sustainability factors, while SSCM research has incorporated environmental and social considerations into supply chain decision-making processes without addressing SC disruptions and uncertainty. However, due to the dynamic and complex business environment, a need has emerged to bridge the gap between these two areas of study and consider them simultaneously in SC decision-making processes.
This dissertation contributes to the literature of supply chain management by developing a multi-objective, two-stage stochastic optimization modeling approach that considers SCRES and SSCM concurrently. Following SC modeling conventions, the model addresses economic, iii environmental, social, and resilience objectives while accounting for potential disruptions that are related to demand and supplier capacity fluctuations. It integrates resilience strategies, namely backup resources, along with sustainability measures including operations and transportation emissions, energy consumption, and job creation. The augmented e-constraint (AUGMECON) method is employed to derive optimal solutions for this multi-objective problem. Numerical examples are presented to validate the presented model and highlight its practical capabilities that will help decision-makers to make more insightful decisions. In addition, the modeling approach applied in this study is compared with other modeling approaches to indicate the advantages of the presented model. Results from the numerical experiment indicate that the developed model is an effective tool supply chain decision-makers can use to incorporate SCRES and SSCM objectives into their strategic and tactical decisions.
Majdfaghihi, Mohammad H., "A Multi-Objective Stochastic Programming Model for Resilient and Sustainable Supply Chains under Uncertainty" (2023). Dissertations. 4036.