Heuristics for solving disassemble-to-order problems with stochastic yields

Within the realm of reverse logistics, remanufacturing has become renowned as a popular option in many reverse logistics settings. In remanufacturing, firms take back products at the end of their use, disassemble them to obtain components, and reassemble these components into a “good as new” remanufactured product. As a process, remanufacturing requires parts as an input, parts which are gained mostly from recovered products. As the quality of the returned products are not known in advance, likewise the amount of good quality parts recovered from the returned product is subject to uncertainty. In this paper, we develop two heuristics of different sophistication which take into consideration that the yields of disassembly are stochastic. The methodology is further illustrated with a numerical example, and performance of the heuristics is examined through a performance study. The performance study indicates excellent performance for the more sophisticated heuristic, but also reveals under which conditions the more simple heuristic can be recommended for application.