Robust optimization for plastic closed-loop supply chains: advancing data-driven models with kernel weight adjustment and fairness metrics
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Zhao, Yuchen
issued: 2024-12-05T20:40:38Z
issued: 2024
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Recycling is essential for reducing the environmental impact of plastic waste and is a key part of sustainable supply chains. However, optimizing these networks is challenging due to uncertainties in input parameters like demand and return rates, requiring solutions to enhance efficiency and resilience in Closed-Loop Supply Chains (CLSC). This research focuses on designing robust CLSC networks to improve resource efficiency, reduce costs, and maximize recycling rates, addressing the need for more resilient and sustainable systems. This research develops a novel approach using Data-Driven Robust Optimization (DDRO), which incorporates historical data into the optimization process to effectively deal with uncertainties such as fluctuating demand and variable return rates of recycled materials. A key innovation in this research is the introduction of a novel concept in DDRO approaches, focusing on the construction of balanced uncertainty sets. A ”balanced uncertainty set” is defined as one where the boundaries maintain nearly equal distances in all directions to the uncovered data samples, ensuring a more equitable representation of uncertainty. To achieve balanced uncertainty sets across varying conservatism levels, a single-Kernel learning-based approach is developed, wherein the weights of the basis kernel matrices are adjusted based on the data’ dispersion in each direction. Additionally, this thesis introduces a Robust Fairness (RF) index to evaluate and compare the balance of different uncertainty sets. A data-driven algorithm is further developed to efficiently compute the RF index. Numerical results demonstrate that the proposed DDRO approach in this thesis generates more balanced uncertainty sets compared to competing methods. In addition, the proposed data-driven algorithm can effectively compute the RF index and compare uncertainty sets in terms of balance, without introducing computational complexities.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/89786
note: ROBUST OPTIMIZATION FOR PLASTIC CLOSED-LOOP SUPPLY CHAINS: ADVANCING DATA-DRIVEN MODELS WITH KERNEL WEIGHT ADJUSTMENT AND FAIRNESS METRICS by Yuchen Zhao B.A.Sc., University of British Columbia, 2022 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Mechanical Engineering) Master of Applied Science (M.A.Sc.) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) November 2024 © Yuchen Zhao, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Robust Optimization for Plastic Closed-Loop Supply Chains: Advancing Data-Driven Models With Kernel Weight Adjustment And Fairness Metrics submitted by Yuchen Zhao in partial fulfillment of the requirements of the degree of Master of Applied Science. Dr. Tosarkani, School of Engineering Supervisor Dr. Chen, School of Engineering Supervisory Committee Member Dr. Ardestani-Jaafari, School of Engineering Supervisory Committee Member Dr. Zarifi, School of Engineering University Examiner iii Abstract Recycling is essential for reducing the environmental impact of plastic waste and is a key part of sustainable supply chains. However, optimizing these networks is challenging due to uncertainties in input parameters like demand and return rates, requiring solutions to enhance efficiency and resilience in Closed-Loop Supply Chains (CLSC). This research focuses on designing robust CLSC networks to improve resource efficiency, reduce costs, and maximize recycling rates, addressing the need for more resilient and sustainable systems. This research develops a novel approach using Data-Driven Robust Optimization (DDRO), which incorporates historical data into the optimization process to effectively deal with uncertainties such as fluctuating demand and variable return rates of recycled materials. A key innovation in this research is the introduction…
has type: Thesis/Dissertation
date issued: 2025-02
is shown at: 10.14288/1.0447413
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: Tosarkani, Babak Mohamadpour
type: Text
identifier URI: http://hdl.handle.net/2429/89786