Integrating resiliency in intelligent decision support systems for real-time management of disruptions in large-scale supply chains
department or school: Applied Science, Faculty of
department or school: Mechanical Engineering, Department of
data provider: DSpace
degree campus: UBCO
creator: Mohammadi, Mahsa
issued: 2025-04-28T18:04:35Z
issued: 2025
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: In an era marked by rapid technological advancements, volatile market conditions, and global disruptions, effective Supply Chain (SC) management requires innovative approaches to address uncertainty, complexity, and dynamic changes. This dissertation presents the development and application of a set of novel Intelligent Decision Support Systems (IDSSs) for solving large-scale, stochastic, and dynamic SC networks under disruptions. The proposed IDSSs are validated using different case studies in diverse domains, including e-commerce, manufacturing, healthcare, and sustainability. Specifically, the research introduces four novel resilient IDSSs that integrate advanced optimization, machine learning, reinforcement learning, and logistics simulations to tackle some of the real-world challenges in the event of major disruptions in SCs. The first proposed resilient IDSS optimizes last-mile delivery in urban areas using mobile depots and crowd-shipping, achieving significant reductions in delivery time, cost, and environmental impact. The second IDSS enhances resilience in global SCs by developing a multi-stage stochastic dynamic programming to deal with disruptions and optimize logistics operations under uncertainty. The third IDSS focuses on equitable and efficient vaccine distribution, addressing demand uncertainties with a data-driven decision-making approach that significantly reduces shortages. Lastly, the fourth IDSS designs a robust and sustainable mask distribution and recycling network, balancing cost efficiency, environmental impact, and customer services. This research integrates exact methods (e.g., Parallelized Stochastic Dual Dynamic integer Programming (PSDDiP)), reinforcement learning, and hybrid optimization approaches, to enhance real-time resilient decision-making for large-scale SCs, while ensuring scalability, and computational efficiency. This is despite that most earlier IDSSs specifically reported for âresilienceâ SC design, lack such integration, …
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/90850
note: Integrating Resiliency in IntelligentDecision Support Systems forReal-Time Management of Disruptionsin Large-Scale Supply ChainsbyMahsa MohammadiB.Sc., Zanjan University, 2016M.Sc., Azad University, 2019A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinTHE COLLEGE OF GRADUATE STUDIES(Mechanical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)April 2025© Mahsa Mohammadi, 2025The following individuals certify that they have read, and recommend to the College of GraduateStudies for acceptance, a dissertation entitled:Integrating Resiliency in Intelligent Decision Support Systems for Real-TimeManagement of Disruptions in Large-Scale Supply Chainssubmitted byMahsa Mohammadi in partial fulfilment of the requirements of the degree of Doctor of PhilosophyDr. Lukas Bichler, School of EngineeringSupervisorDr. Abbas Milani, School of EngineeringCo-SupervisorDr. Jahangir Hossain, School of EngineeringSupervisory Committee MemberDr. Mahmudur Fatmi, School of EngineeringSupervisory Committee MemberDr. Harish Krishnan, University of British Columbia- VancouverUniversity ExaminerDr. Guoqing Zhang, Faculty of Engineering, University of WindsorExternal ExamineriiAbstractIn an era marked by rapid technological advancements, volatile market conditions, and globaldisruptions, effective Supply Chain (SC) management requires innovative approaches to addressuncertainty, complexity, and dynamic changes. This dissertation presents the development andapplication of a set of novel Intelligent Decision Support Systems (IDSSs) for solving large-scale,stochastic, and dynamic SC networks under disruptions. The proposed IDSSs are validated usingdifferent case studies in diverse domains, including e-commerce, manufacturing, healthcare, andsustainability. Specifically, the research introduces four novel resilient IDSSs that integrate ad-vanced optimization, machine learning, reinforcement learning, and logistics simulations to tacklesom…
has type: Thesis/Dissertation
date issued: 2025-05
is shown at: 10.14288/1.0448631
language: eng
degree discipline: Mechanical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Bichler, Lukas
contributor: Milani, Abbas S.
type: Text
identifier URI: http://hdl.handle.net/2429/90850