A Reconfigurable Cellular Remanufacturing Architecture: a multi-objective design approach

Remanufacturing is a practice that postpones the product ‘end-of-life’ by returning the properties or features of a new product to a used product. Such a process represents an efficient circular economy strategy to extend product life, reducing its environmental footprint. However, remanufacturing systems must overcome distinct challenges related to information uncertainties about quantity and conditions of used products. Current strategies to address these issues include smart approaches towards smart remanufacturing systems. However, remanufacturing architectures must manage the negative effects on operations derived from the used product’s deterioration and return rate variability, as well as remanufactured products demand fluctuations. This study contributes to address this issue by a Reconfigurable Cellular Remanufacturing Architecture that is integrated in a business strategy towards smart sustainable remanufacturing. The design process is based on a multi-objective optimization model that minimizes the grouping cost, the workload balancing cost, the investment cost, the makespan cost and the reconfigurable cost. A customized version of the well-known multi-objective evolutionary non-dominated sorting genetic algorithm II (NSGA2), a mono-objective genetic algorithm (GA-mo) and a GAMS model were implemented to obtain the potential architecture’ configurations for an explanatory case study based on real-world industrial and random data, respectively. Two procedures to identify the best architecture were also considered. A sensitivity analysis is presented to illustrate the robustness of the proposed model. Managerial insights illustrate about solution methods, and best architecture selection. Practical implications also are provided to offer useful options for practitioners.