Closed-loop supply chain network design for the paper industry: Amulti-objective stochastic robust approach

Abstract

Closed-loop supply chain design is used to provide an optimal platform for efficient and effective supply chain management. It is an essential and strategic operation management problem in supply chain management and, usually, includes multiple and conflicting objectives. A new mixed integer non-linear programming model for a multi-objective closed-loop supply chain network design problem in the paper industry is developed under uncertainty. The objective functions can minimize the total cost, maximize the total volume flexibility, and minimize the total number of vehicles hired in order to fulfill the paper industry's policies towards a cleaner and green environment. In addition, a novel hybrid solution is presented based on stochastic programming, robust optimization, and fuzzy goal programming. A numerical example utilizing the real data from the paper industry in East Azerbaijan of Iran is designed, and the model performance is assessed. Furthermore, a recently developed Dragonfly Algorithm (DA) has been employed to solve the given problem on large scales and compared with Genetic Algorithm (GA). The results indicated that the DA achieved better performance, as compared with the GA.