An exact method for disassembly line balancing problem with limited distributional information
Résumé
As an important part in product recycling, disassembly line balancing problem (DLBP) has attracted a large amount of attention. Stochastic DLBP is now a hot and challenging research topic, due to its wide applications. This work investigates a DLBP with uncertain task times, where the distributional information is limited, i.e. only the mean values and standard deviations are given, due to the lack of data. From a two-stage perspective, a disassembly process is determined and the disassembly tasks are assigned to workstations in the first stage, and the penalty cost (i.e. the recourse cost) for exceeding the cycle time is minimised in the second stage. The objective is to minimise the expected system cost. For the problem, a two-stage distributionally robust formulation is devised, to minimise the worst-case expected system cost out of all possible probability distributions. Different from literature that focuses on approximation methods to tackle the limited distributional information, an exact method, i.e. the cutting-plane algorithm, is developed. Numerical results show that compared with the state-of-the-art solution methods, our developed cutting-plane algorithm can provide more reliable solutions in term of the robustness, especially in some extreme cases.