Value recovery options portfolio optimization for remanufacturing end of life product

Recovering valuable components from End-of-Life (EOL) product is regarded as a means to extend remaining useful life and reduce production cost of used components in the context of remanufacturing. There are three value recovery options for each component including new, reuse, and reconditioning, making the value recovery of EOL product a complex combinatorial optimization problem. To obtain the optimal value recovery options portfolio of used components and improve the economic benefits from remanufacturing, a multi-objective optimization method of value recovery is applied to the remanufacturing of EOL product. Firstly, an evaluation criterion in terms of quantified damage level and remaining life of used components is established, which aims to identify value recovery options for each used component. Then, the concept of Life Span Equilibrium (LSE) is proposed and a multi-objective optimization model is established, in which LSE, value recovery efficiency, and cost are taken as the objectives. An adaptive Epsilon-dominance based Strength Pareto Evolution Algorithm (AE-SPEA2) is employed to obtain an optimal value recovery portfolio, and its results are compared with an Elitist based Non-dominated Sorting Genetic Algorithm (NSGA-II) and a Pareto Envelope based Selection Algorithm (PESA-II). Finally, a used lathe (model C6132) is taken as an example to verify the practicality and effectiveness of the proposed method, the results of which indicate that the proposed method is effective in optimizing the value recovery of EOL product. (C) 2018 Elsevier Ltd. All rights reserved.