Abstract
This study designed a cross-stage reverse logistics course for defective products so that damaged products generated in downstream partners can be directly returned to upstream partners throughout the stages of a supply chain for rework and maintenance. To solve this reverse supply chain design problem, an optimal cross-stage reverse logistics mathematical model was developed. In addition, we developed a genetic algorithm (GA) and three particle swarm optimization (PSO) algorithms: the inertia weight method (PSOA_IWM), V Max method (PSOA_VMM), and constriction factor method (PSOA_CFM), which we employed to find solutions to support this mathematical model. Finally, a real case and five simulative cases with different scopes were used to compare the execution times, convergence times, and objective function values of the four algorithms used to validate the model proposed in this study. Regarding system execution time, the GA consumed more time than the other three PSOs did. Regarding objective function value, the GA, PSOA_IWM, and PSOA_CFM could obtain a lower convergence value than PSOA_VMM could. Finally, PSOA_IWM demonstrated a faster convergence speed than PSOA_VMM, PSOA_CFM, and the GA did.
Highlights
Intense competition within the global market has prompted enterprise competition to change from a competition among companies to that among supply chains
Dong et al [55] compared the improved PSO, a combinatorial particle swarm optimization (CPSO), with genetic algorithm (GA), and the results showed that the improved PSO was more effective in solving nonlinear problems
(1) We presented a mathematical model for partner selection and production-distribution planning in multistage supply chain networks with crossstage reverse logistics
Summary
Intense competition within the global market has prompted enterprise competition to change from a competition among companies to that among supply chains. Reverse logistics is more complex than forward logistics, and this study aimed to develop a mathematical foundation for modeling a cross-stage reverse logistics plan that enables defective products with differing degrees of damage to be returned to upstream partners in the stages of a supply chain for maintenance, replacement, or restructuring. This crossstage reverse logistics model can help save time, lessen unnecessary deliveries, and, more importantly, meet the conditions of reverse logistics operation more efficiently. Time, quality, and green appraisal score are measurable criteria with different units; in this study the T-transfer approach was Optimal mathematical model
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