Abstract
Supply Chain (SC) performance appraisal extends beyond economic evaluation to encompass environmental and social impacts, as well as resilience to supply and demand disruptions. Thus, SC network design decisions should simultaneously optimize all these performance metrics as a primary objective. However, existing studies optimized these objectives independently or in a limited scope, failing to comprehensively address the interconnected nature of these performance metrics. Therefore, this study proposes a mixed integer linear programming (MILP) model with four objectives to address a multi-tier resilient and sustainable semiconductor SC network design problem under trade credit, supply, and demand uncertainties. The model aims to determine the optimal number and location of various facilities, the amount to order from suppliers, economic production quantities, quantity allocation between SC entities, and ideal supplier selections. The proposed supply chain network contributes to the global effort to reduce carbon emissions and enhance the circular economy by considering carbon trading, recycling, and the proper disposal of end-of life products. Uncertainties in input parameters are addressed using a fuzzy programming approach, while lexicographic optimization and the ɛ-constraint method are used to solve the proposed model. This paper presents a numerical example to illustrate the applicability of the proposed model and provide managerial insights. The numerical analysis results show that provided an appropriate confidence level for the uncertain parameters, the sets of Pareto optimal solutions can be generated. And it also shows that the total SC cost increases with decreasing carbon emissions level and increasing job opportunities created. This research advances sustainable and resilient supply chain management practices while offering practical guidance for decision-makers in real-world contexts.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.