Hybrid Simulation-Based Optimization for Production Planning of a Dedicated Remanufacturing System

Abstract

This paper presents a comparative simulation study of a dedicated remanufacturing system. The production line of a dedicated remanufacturing system producing multiple products under uncertain environment is improved through the simulation-based optimization approach. Appropriate inventory capacity of buffers, a proper switching rule, and a suitable run size of each product should be optimally set to yield the highest system's profit. Then, hybrid simulation-based optimization algorithms with two hybrid optimization forms using a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as complementary to each other in relation to their standard algorithms are proposed and compared. A case study is used to demonstrate and compare the performances among the algorithms to show the advantages of the proposed algorithms. This approach can assist in decision making for the planning and management of dedicated remanufacturing systems that are required to operate with various decision variables under uncertainties.