Inderfurth, Karl, Ian M. Langella, Sandra Transchel and Stephanie Vogelgesang

A heuristic solution method for disassemble-to-order problems with binomial disassembly yields

International Journal of Production Economics, 185: 274-266, (2017).

Copy reference link   DOI: 10.1016/j.ijpe.2017.01.006

Abstract: In disassemble-to-order problems, where a specific amount of several components must be obtained from the disassembly of several types of returned products, random disassembly yields create a formidable challenge for appropriate planning. In this context, it is typically assumed that yields from disassembly are either stochastically proportional or follow a binomial process. In the case of yield process misspecification, it has been shown (see Inderfurth et al. (2015)) that assuming binomial yields usually results in a lower penalty than assuming stochastically proportional yields. While there have been heuristics developed for the disassemble-to-order problem with stochastically proportional yields, a suitable, powerful heuristic for binomial yields is needed in order to facilitate solving problems with complex real-world product structures. We present a heuristic approach that is based on a decomposition procedure for the underlying non-linear stochastic optimization problem and that can be applied to problems of arbitrary size. A comprehensive numerical performance study using both randomly generated instances as well as a full factorial experimental design and, additionally, the data of a practical case example reveals that this heuristic delivers close-to-optimal results.

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