Abstract
Disruption on a supply chain provokes lost that can be minimized through an alternative solution. This solution involves a strategy to manage the impact of the disruption and thus to recuperate the supply chain. Difficulty of this management is the diversity of factors such that becomes complex to provide or choice a solution among the possible ones. Depending on the objective(s) to optimize are the strategy to follow and the solution to choice. In this work the Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization NSGA-II is used as the strategy to generate and optimize (minimize) solutions (lost) in front of a disruption. The included objectives are cost, risk and the place of facilities supporting the supply chain recuperation. These objectives are combined to generate possible solutions and to choice one such that it provides a proposal to minimize the disruption impact on a delimited period of time. Advantage of NSGA-II utilization is the provision of a practical formal and computational tool to analyze different scenarios without simplifies the complexity of a standard real supply chain. The illustrative exercise presents recovery scenarios for a crude oil refinery supply chain.
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Serrano, V., Alvarado, M., Coello, C.A.C. (2007). Optimization to Manage Supply Chain Disruptions Using the NSGA-II. In: Castillo, O., Melin, P., Ross, O.M., Sepúlveda Cruz, R., Pedrycz, W., Kacprzyk, J. (eds) Theoretical Advances and Applications of Fuzzy Logic and Soft Computing. Advances in Soft Computing, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72434-6_47
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DOI: https://doi.org/10.1007/978-3-540-72434-6_47
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