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Top1. Introduction
Climate change is likely to increase the vulnerability of global supply chains that cause from growing global populations, which raise demand for resources. Manufacturers need to strive for greater efficiency in their use of material and energy. Therefore, manufacturers explore new ways of doing business, for example, by expanding into “remanufacturing” of end-of-life products (Fernando and Evans, 2015). A remanufacturing system is a system that remanufactures or reproduces returned or used products to be like-new products. Remanufacturing systems are viewed as green processes that could reduce global warming, increase environmental sustainability, and increase economic growth. They consist of many processes, such as disassembly, cleaning, inspection, reconditioning, reassembly, and testing. According to Shabanpour and Colledani (2018), remanufacturing is one of the major pillars of technology for the circular economy. It can improve companies’ profitability due to environmentally-friendly remanufacturing processing for used products.
Remanufacturing systems also present some drawbacks such as the uncontrollable quality and quantity of returned products. This inherent uncertainty needs to be considered in production planning. To improve the efficiency of such systems, achieve the demand of customers, and increase the systems’ profit, the operating parameters need to be optimized. Considering the complexity of such systems and their operations under uncertainty, meta-heuristic optimization methods are applied here rather than mathematical methods, to seek the best operational settings of the system. However, the meta-heuristics methods do not always guarantee good enough results. Hence, hybrid optimization methods, which are composite methods that combine more than one optimization algorithm, are used to avoid local optimal results and find better solutions. Therefore, the main contribution of this study is to introduce a hybrid simulation-based optimization approach in a remanufacturing system, which contains a great deal of inherent uncertainties as well as recommend significant operating parameters. The approach combines two meta-heuristics algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), to compare a hybrid algorithm with typical standalone heuristic algorithms. This hybrid algorithm should enhance the searching efficiency and avoid local optima, to identify better outcomes. This study demonstrates that the proposed hybrid PSO with GA significantly outperforms the standalone algorithms, achieving satisfactory results in terms of computation efficiency and solution quality.
The remainder of this paper is organized as follows. The literature review, which consists of a review of remanufacturing systems, uncertainties in remanufacturing, and system optimization, is provided in Section 2. Section 3 explains a hybrid simulation-based optimization approach, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and hybrid optimization algorithms. Section 4 illustrates a case study including the model parameter assumptions, decision variables, simulation model, and parameter settings of each optimization algorithm. Section 5 presents the results and discussion of this study. Finally, Section 6 provides the conclusions and recommendations for further study.