2026-09-20T13:49:25Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1560282024-08-13T03:02:22Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Magnanti, Thomas
Freund, Daniel
Ceballos Mondragón, Regina
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Sloan School of Management
2024-08-12T14:16:23Z
2024-08-12T14:16:23Z
2024-05
2024-06-25T18:10:19.325Z
https://hdl.handle.net/1721.1/156028
Following NextEra Energy Resources’ accelerated growth and disruptions in the solar panel supply chain, their solar panel allocation process is becoming more complex. This process results in a schedule that determines when to deliver close to 150 million solar panels to more than fifty project sites under development and construction, while balancing requirements from multiple stakeholders. Due to project and contract interdependencies, modifying the equipment delivery schedule leads to costs that have consequential impacts. This thesis presents and implements a novel mixed integer programming model to determine the optimal schedule for delivering solar panels to project sites. The model abstracts impactful and quantifiable costs and minimizes them to propose a realistic solution. It produces a schedule in significantly less time than the current manual approach by finding a feasible solution in less than 15 minutes. The thesis introduces three scenarios of supply chain disruptions that mimic real-world events, demonstrating the model’s flexibility and helping NextEra Energy Resources adapt to future supply chain disruptions.
S.M.
M.B.A.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
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https://rightsstatements.org/page/InC-EDU/1.0/
Improving Supply Chain Resiliency through Solar Panel Delivery Optimization
Thesis
application/pdf
Master
Master of Science in Electrical Engineering and Computer Science
Master of Business Administration
Publication
unknown
unknown
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<Abstract>Following NextEra Energy Resources’ accelerated growth and disruptions in the solar panel supply chain, their solar panel allocation process is becoming more complex. This process results in a schedule that determines when to deliver close to 150 million solar panels to more than fifty project sites under development and construction, while balancing requirements from multiple stakeholders. Due to project and contract interdependencies, modifying the equipment delivery schedule leads to costs that have consequential impacts. This thesis presents and implements a novel mixed integer programming model to determine the optimal schedule for delivering solar panels to project sites. The model abstracts impactful and quantifiable costs and minimizes them to propose a realistic solution. It produces a schedule in significantly less time than the current manual approach by finding a feasible solution in less than 15 minutes. The thesis introduces three scenarios of supply chain disruptions that mimic real-world events, demonstrating the model’s flexibility and helping NextEra Energy Resources adapt to future supply chain disruptions.</Abstract>
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