Kungwalsong Kanokporn, Mendoza Abraham, Kamath Vasanth, Pazhani Subramanian, Marmolejo-Saucedo Jose Antonio
Graduate School of Management and Innovation, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.
Facultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, 45010 Zapopan, Jalisco Mexico.
Ann Oper Res. 2022;315(2):1803-1839. doi: 10.1007/s10479-022-04542-5. Epub 2022 Feb 15.
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.
供应链中断迫使全球专业人士考虑替代策略来解决这些问题并在未来保持盈利。在本研究中，我们考虑了一个四阶段的全球供应链，并以利润最大化和中断风险最小化为目标设计了该网络。我们将中断风险量化并建模为设施地理多样化（称为供应密度，基于节点间阶段距离评估）的函数，以减轻中断造成的风险。此外，我们开发了一个双目标混合整数线性规划模型来设计供应链，以实现利润最大化和供应密度最大化。我们提出了一种交互式模糊优化算法，该算法通过系统地获取决策者输入来生成有效前沿，并在一个实际示例的背景下解决双目标模型问题。我们还使用一组离散的中断场景进行了中断分析，以确定双目标模型的网络设计相对于传统利润最大化模型的优势。我们的研究表明，与传统利润最大化解决方案相比，双目标模型的网络设计在中断情况下预期利润高2%，利润方差低2.2%。我们设想该模型将帮助企业评估缓解效益和缓解成本之间的权衡。
