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Remanufacturing, one of the most advanced product recovery options, processes used or end-of-life products with disassembly, reprocessing, and reassembly operations in such a way that their qualities are as good as new. This paper focuses on production planning in remanufacturing systems over a given planning horizon with discrete time periods. The main decisions are: (a) the number of used or end-of-life products to be disassembled and disposed; (b) the number of parts or components to be reprocessed, disposed, and newly purchased; and (c) the number of products to be reassembled. The objective is to maximize the total profit, i.e. the difference between the sales revenue and the relevant costs. In particular, set-up costs and times are explicitly considered since set-ups are significant in remanufacturing systems. As an extension of the existing literatures on production planning in remanufacturing systems, a generic mixed integer programming model is suggested that incorporates the detailed processes of the remanufacturing process. Then, owing to the complexity of the problem, two types of heuristics based on the linear programming technique are developed. To show the performances of the heuristics, computational experiments were done on some test instances, and the results are reported.

Disassembly levelling is to determine disassembly structures that specify components to be obtained from end-of-use/life products, and disassembly lot-sizing is to determine the timing and quantity of disassembling end-of-use/life products to satisfy the demands of their components. As an extension of the previous studies that consider them separately, this study integrates the two problems, especially in the form of multi-period model. Particularly, this study considers a generalized integrated problem in which disassembly levels may be different for the products of the same type. To describe the problem mathematically, we develop an integer programming model that minimizes the sum of setup, operation and inventory holding costs. Then, due to the problem complexity, a heuristic algorithm is proposed that consists of two phases: (a) constructing an initial solution using a priority-based greedy heuristic and (b) improving it by removing unnecessary disassembly operations after characterizing the properties of the problem. To show the performance of the heuristic algorithm, computational experiments were performed on various test instances and the results are reported.

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