A heuristic solution method for disassemble-to-order problems with binomial disassembly yields
Karl Inderfurth,
Ian M. Langella,
Sandra Transchel and
Stephanie Vogelgesang
International Journal of Production Economics, 2017, vol. 185, issue C, 266-274
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.
Keywords: Remanufacturing; Disassemble-to-order problem; Binomial yields; Heuristics (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:185:y:2017:i:c:p:266-274
DOI: 10.1016/j.ijpe.2017.01.006
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