Mitigating closed-loop supply chain risk through assessment of production cost, disruption cost, and reliability

Modern supply chains are affected by high disruption risks owing to their production complexity and globalization characteristics. In addressing supply chain disruption risks, we observed mutual conflict among reliability, disruption costs, and production costs. Therefore, the relationship among production costs, disruption costs, and reliability must be balanced to accommodate supply chain disruptions more effectively. This study establishes a four-tier closed-loop supply chain multi-objective optimization model based on a propagation structure. This study is the first to unify these three objectives for supply chain optimization. Additionally, the artificial bee colony (ABC) algorithm and gradient descent method are combined for the solution. To demonstrate the effectiveness of this method, the classical ABC algorithm is compared with a simulated annealing algorithm through a case study in the semiconductor industry. Our research findings indicate that considering the industrial applicability of the semiconductor industry, increasing supply chain reliability and disruption cost weights, and assigning overall supply chain orders from a holistic perspective can effectively address supply chain disruption risk.