Author Identifier

Seyed Ashkan Hosseini Shekarabi: https://orcid.org/0000-0001-5020-6734

Date of Award

2025

Document Type

Thesis - ECU Access Only

Publisher

Edith Cowan University

Degree Name

Doctor of Philosophy

School

School of Business and Law

First Supervisor

Reza Kiani Mavi

Second Supervisor

Flavio Romero Macau

Abstract

This thesis investigates the development of a robust, multi-objective framework to enhance the resilience and sustainability of supply chains. While the model was tested using data from the fast-moving consumer goods sector in Western Australia, it was developed as a general tool applicable across various industries. It addresses the complexities and challenges of supply chain management in the face of disruptions and uncertainties, employing innovative methodologies and analytical techniques across three distinct yet interrelated studies.

The research begins by analysing the current state of supply chain resilience and identifying key themes, methodologies, and gaps within existing literature. It emphasises optimisation, technology adoption, and strategic resilience planning in mitigating supply chain vulnerabilities. The study delineates three main areas requiring further investigation through comprehensive literature review techniques: optimisation strategies, technological enhancements, and post-disruption recovery mechanisms.

Subsequently, the thesis explores decision-making under uncertainty within the context of the meat supply chain, introducing a risk-aware, two-stage stochastic programming model. This model aims to balance conflicting objectives such as cost, environmental impact, and social benefits, demonstrating the application of advanced analytical methods to improve decision-making efficacy and supply chain robustness.

The final part of the thesis extends the discussion to perishable supply chains, presenting novel approaches such as Axis-Shift Robustness and full-fledged p-robust optimisation to manage and mitigate the risks associated with demand uncertainties and perishability. This includes strategically allocating financial resources, adopting resilience strategies, and optimal pricing strategies supported by computational analyses and machine learning techniques.

This research generates both theoretical and practical contributions. Theoretically, it broadens supply chain literature by introducing new resilience strategies, such as Retailer Product Horizontal Transfer Allowance, and advanced uncertainty approaches like axis-shift robustness. It also proposes a full-fledged p-robust optimisation method, which addresses feasibility and optimality under diverse scenarios. These implications enrich existing models by highlighting uncertainties and integrating scenario-based stochastic programming and data-driven optimisation.

Practically, the study designs a decision support system that guides managers and decision makers in applying effective resilience strategies, ensuring supply chain networks (SCNs) can withstand disruptions. This system helps leaders make tactical, operational, and strategic decisions related to resource allocation, sales representative selection, and inventory control. It provides actionable insights for policymakers and practitioners, fostering adaptability, reliability, and long-term sustainability within evolving market environments.

DOI

10.25958/7jnb-6g96

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