Scenario-based stochastic linear programming model for multi-period disassembly lot-sizing problems under random lead time

Abstract

In the last few years, there has been a growing interest in the disassembly scheduling problem to fulfil the demands of individual disassembled parts over a given planning horizon. An analysis of the literature shows that the disassembly lead time is often considered deterministic. Indeed, in real-life systems, this parameter is rarely known and often has uncertain values that can be caused by the complexity of disassembly operation. In this study, a new scenario-based stochastic linear programming model is proposed to deal with a multi-period, single product type and two-level disassembly lot-sizing problem under lead time uncertainty. The demand for each component is known for each time period and the real disassembly lead time of end-of-life product is an independent random discrete variable with a known probability distribution. The proposed model is used to determine the optimal quantity for disassembled end-of-life products in order to minimize the setup cost for end-of-life product and the sum of average inventory holding and backlogging costs for each component, over the planning horizon and all scenarios.

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