%0 Journal Article
%T A heuristic solution method for disassemble-to-order problems with binomial disassembly yields
%A Inderfurth, Karl
%A Langella, Ian M.
%A Transchel, Sandra
%A Vogelgesang, Stephanie
%D 2017
%J International Journal of Production Economics
%@ 09255273
%V 185
%P 266–274
%! Inderfurth, Langella et al. 2017 – A heuristic solution method
%R 10.1016/j.ijpe.2017.01.006
%X 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.