A fuzzy chance-constrained programming model for mathematical modeling-based metaheuristic algorithms in the design of green loop supply chain networks for power plants
Abstract
This study introduces a multi-cycle supply chain design model, encompassing crucial executive decisions confronting supply chain management firms. These decisions encompass facility location, the flow of raw material procurement, and investments in diversifying activities within the power plant's supply chain design. A fuzzy chance-constrained programming approach is employed to deal with the uncertainties associated demand and cost, and a service level indicator is incorporated into the performance metric. The model's validation is conducted on a larger scale, employing two metaheuristic algorithms, MOPSO and NSGAII. The results revealed that the MOPSO algorithm exhibited faster computational efficiency than NSGAII and demonstrated superior performance in the first and second objective functions. However, analytical parameters such as NPF, MSI, and SM favored the NSGAII algorithm over MOPSO. This study presents a comprehensive multi-cycle supply chain design model addressing key management decisions, dealing with demand and cost uncertainty, and evaluating performance using a service level indicator. The study's findings underscore the efficiency of the MOPSO algorithm in computational speed but highlight NSGAII's advantages in terms of certain analytical parameters. These insights contribute to enhancing supply chain management strategies in diverse scenarios.
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