Disassembly Leveling and Lot-sizing for Multiple Product Types with Uncertain Component Demands

Abstract

This study considers an integrated problem of determining the depth of disassembling products, i.e. disassembly leveling, and the quantities of disassembling the products and their subassemblies according to its disassembly level to satisfy the component demands, i.e. disassembly lot-sizing. As an extension of the previous study, two stochastic versions of the problem for multiple product types with uncertain demands are considered, i.e. a basic problem and an extension with parts commonality. Stochastic integer programming models are developed that minimize the expected sum of setup and operation costs. Then, for the basic problem, an optimal sample average approximation algorithm is proposed that incorporates a polynomial optimal algorithm to solve the sample average approximating problems and it is illustrated with a numerical example. Also, for the extended problem, sample average approximation-based heuristics is proposed due to the complexity of the sample average approximating problems. To test the performance of the heuristic, computational experiments were done on various test instances and the results are reported.