Closed-loop supply chains (CLSC) though present for decades, have seen significant research in optimization only in the last five years. Traditional sustainable CLSCs have generally implemented a Carbon Cap Trading (CCT), Carbon Cap (CC), or Carbon Taxes methodology to set carbon emissions limits but fail to minimize these emissions explicitly. Moreover, the traditional CCT model discourages investment in greener technologies by favoring established logistics over eco-friendly alternatives. This research tackles the sustainable CLSC problem by proposing a mixed-integer linear programming (MILP) carbon-conscious textit{p}-hub location model having the objective of minimizing emissions subject to profit constraints. The model is then extended to incorporate multi-periodicity, transportation modes, and end-of-life periods with a bi-objective cost and emissions function. Additionally, the model accounts for long-term planning and optimization, considering changes in demand and returns over time by incorporating a time dimension. The model's robustness and solving capabilities were tested for the case of electric vehicle (EV) battery supply chains. Demand for EVs is projected to increase by 18% annually, and robust supply chain designs are crucial to meet this demand, making this sector an important test case for the model to solve.
Two baseline cases with minimum cost and minimum emissions objectives were tested, revealing a significant gap in emissions and underlining the need for an emissions objective. A sensitivity analysis was conducted on key parameters focusing on minimizing emissions; the analysis revealed that demand, return rates, and recycling costs greatly impact CLSC dynamics. The results showcase the model's capability of tackling real-world case scenarios, thus facilitating comprehensive decision-making goals in carbon-conscious CSLC design. / Master of Science / Closed-loop supply chain (CLSC) is a supply chain that recycles used products back to the manufacturer. CLSCs have been around for decades, but significant progress in optimizing them has only emerged over the last five years. Sustainable CLSC models often include limits on carbon emissions but usually don't directly minimize them. Traditional CLSC models tend to prioritize established logistics over greener technologies, discouraging investment in eco-friendly options. This study addresses this problem by introducing a mathematical model designed to minimize emissions while considering profit constraints. The model is expanded to factor in different time periods, transportation methods, and end-of-use phases with two goals in mind: cost and emissions. Additionally, it incorporates long-term planning, accounting for shifts in customer demand and product returns. The model's effectiveness was tested with electric vehicle (EV) battery supply chains, which serve as an important example given the predicted annual 18% growth in EV demand and the crucial need for efficient supply chain design.
Two baseline scenarios were examined: one aiming to minimize costs and the other to minimize emissions. The results showed a notable disparity in emissions between the two, underscoring the importance of an emissions-focused objective. Key parameters, such as demand, return rates, and recycling costs, demonstrated a significant impact on CLSC operations. The findings highlight the model's ability to handle real-world challenges, enabling informed decision-making for designing carbon-conscious CLSCs.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/119203 |
| Date | 22 May 2024 |
| Creators | Iyer, Arjun |
| Contributors | Industrial and Systems Engineering, Shewchuk, John P., Bansal, Manish, Buyuktahtakin Toy, Esra |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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