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Full text release has been delayed at the author's request until September 01, 2028

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Strategic Modeling for Sustainable Assembly Supply Chain Network Design

Younessinaki, Roohollah
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1690825395226176

Abstract Details

, Doctor of Philosophy (PhD), Ohio University, Mechanical and Systems Engineering (Engineering and Technology).
This research presents a novel multi-objective mathematical model for the design of a three-echelon sustainable supply chain network comprising suppliers, assemblers, and customers. The research aims to optimize three sustainability functions, namely economic, environmental, and social aspects. The proposed integrated optimization model addresses four key decision areas: (1) locating assembly plants and determining their manufacturing capacity and line configurations, (2) selecting transportation modes for the delivery of parts from suppliers to assemblers and the final product to customers, (3) supplier selection, and (4) choosing the source of energy from a range of conventional and renewable options. This research investigates the interactions between sustainability objectives by analyzing the results obtained through a Pareto frontier approach. The study aims to enable decision-makers to select their preferred option from a range of scenarios. To showcase the practical application of the proposed optimization model, a case study involving a US truck manufacturer is conducted. The findings of the study reveal the trade-offs that exist among the sustainability criteria, providing decision-makers with a variety of alternatives to align their business strategies accordingly. The proposed problem is a multi-objective mixed-integer non-linear programming model that incorporates chance constraints to account for energy usage uncertainties in the assembly plant. The integration of robots within assembly plants introduces variability in energy consumption. Factors such as specific robot tasks, variations in product mix or production volumes, and the condition of robot components can all influence energy usage. In order to effectively address these uncertainties, it is essential to formulate appropriate constraints as chance constraints. By incorporating chance constraints, the model can consider the probabilistic nature of energy usage and ensure that the prescribed probability of meeting energy requirements is satisfied. This approach allows decision-makers to account for uncertainties associated with robot-related factors and make more robust and reliable decisions regarding energy management in the assembly plant. To address the intricate nature of solving the proposed mixed-integer non-linear programming problem, the research utilized the epsilon constraint method as the solution approach. This method effectively handles multiple conflicting objectives by transforming them into a single-objective problem with additional constraints. To solve the transformed problem and obtain optimal solutions, the Baron solver in AMPL was employed. Three test problems are considered in the analysis. However, due to the computational challenges faced by exact solvers in solving large-scale instances, the NSGA II algorithm is utilized to tackle these instances effectively. The results obtained from this research provide decision-makers with valuable insights for making informed decisions that balance economic, environmental, and social sustainability objectives. Furthermore, sensitivity analysis reveals that the availability of sustainable energy and part suppliers significantly impacts the selection of transportation modes to maintain an acceptable carbon footprint. It is recommended to incorporate resiliency in energy supply to mitigate the influence of uncertain weather conditions on renewable energy sources. By considering these factors, decision-makers can enhance the sustainability and resilience of their supply chain networks.
Tao Yuan (Advisor)
Tao Yuan (Committee Chair)
Diana Schwerha (Committee Member)
William Young (Committee Member)
Ashley Metcalf (Committee Member)
Gary Weckman (Committee Co-Chair)
Energy; Environmental Management; Industrial Engineering; Operations Research; Sustainability
Sustainable Assembly Supply Chain, Multi-Objective Optimization, Energy Efficiency, Ergonomic Risks, Strategic Decision Making

Recommended Citations

Citations

  • Younessinaki, R. (n.d.). Strategic Modeling for Sustainable Assembly Supply Chain Network Design [Doctoral dissertation, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1690825395226176

    APA Style (7th edition)

  • Younessinaki, Roohollah. Strategic Modeling for Sustainable Assembly Supply Chain Network Design. Ohio University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1690825395226176.

    MLA Style (8th edition)

  • Younessinaki, Roohollah. "Strategic Modeling for Sustainable Assembly Supply Chain Network Design." Doctoral dissertation, Ohio University. Accessed NOVEMBER 23, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1690825395226176

    Chicago Manual of Style (17th edition)

ohiou1690825395226176
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This open access ETD is published by Ohio University and OhioLINK.