An application of interactive fuzzy optimization model for redesigning supply chain for resilience.

Autor: Kungwalsong K; Graduate School of Management and Innovation, King Mongkut's University of Technology Thonburi, Bangkok, Thailand., Mendoza A; Facultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, 45010 Zapopan, Jalisco Mexico., Kamath V; Operations and Decision Sciences, T A Pai Management Institute, Manipal Academy of Higher Education, Manipal, 576104 India., Pazhani S; Advanced Analytics and Optimization Services Group, SAS Institute, 100 SAS Campus Dr, Cary, NC 27513 USA., Marmolejo-Saucedo JA; Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, 03920 Ciudad de México, Mexico.
Jazyk: angličtina
Zdroj: Annals of operations research [Ann Oper Res] 2022; Vol. 315 (2), pp. 1803-1839. Date of Electronic Publication: 2022 Feb 15.
DOI: 10.1007/s10479-022-04542-5
Abstrakt: Supply chain disruptions compel professionals all over the world to consider alternate strategies for addressing these issues and remaining profitable in the future. In this study, we considered a four-stage global supply chain and designed the network with the objectives of maximizing profit and minimizing disruption risk. We quantified and modeled disruption risk as a function of the geographic diversification of facilities called supply density (evaluated based on the interstage distance between nodes) to mitigate the risk caused by disruptions. Furthermore, we developed a bi-criteria mixed-integer linear programming model for designing the supply chain in order to maximize profit and supply density. We propose an interactive fuzzy optimization algorithm that generates efficient frontiers by systematically taking decision-maker inputs and solves the bi-criteria model problem in the context of a realistic example. We also conducted disruption analysis using a discrete set of disruption scenarios to determine the advantages of the network design from the bi-criteria model over the traditional profit maximization model. Our study demonstrates that the network design from the bi-criteria model has a 2% higher expected profit and a 2.2% lower profit variance under disruption than the traditional profit maximization solution. We envisage that this model will help firms evaluate the trade-offs between mitigation benefits and mitigation costs.
(© The Author(s) 2022.)
Databáze: MEDLINE
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