Designing a multi-objective integrated model for forward/reverse logistics of spare parts under uncertainty and solved with Meta-heuristic algorithms (case study: Diesel Generator)

In recent years, the focus on economic, competitive, marketing, and environmental issues have drawn the attention of many researchers to reverse logistics. For this reason, they try to integrate it with forward logistics. In this study, a mixed integer Linear programing model is proposed to optimize the forward/reverse logistics, in which all the parameters are considered to be fuzzy. Then, this fuzzy model is converted into a deterministic model using the efficient method of Jimenez. In this research, spare parts ofa product (multi-component) is considered which has been studied rarely.Because of the focus on the defense systems, the level of responsiveness is of great importance. Therefore, the objective functions are set to combine minimization of supply chain costs (i.e. costs of renting different facilities as well as transportation costs between them) and increasing the responsiveness (i.e. minimization of the lead time of the spare parts and the product). Echelons of the supply chain of this study include supply, production, distribution, collection, assembly, refurbish, recycling, and disposal. The model is developed for spare parts of AC box in diesel generator and is solved usin g solver CPLEX. Then, four algorithms including Non-Dominated Sorting Genetic Algorithm, Multi-Objective Simulated Annealing, Bee Hybrid Algorithm, and hybrid algorithm of particle swarm optimization are presented and as a matter of verification, their results are compared to the exact results of solver CPLEX

Designing a multi-objective integrated model for forward/reverse logistics of spare parts under uncertainty and solved with Meta-heuristic algorithms (case study: Diesel Generator) Keywords:

Forward/Reverse supply chain , Multi-component products , Fuzzy multi-objective programming , Closed-loop supply chain , NSGA II , MOSA , Bee Algorithm , PSO