We consider a partial disassembly line balancing problem with hazardous tasks whose successful completions are uncertain. When any hazardous task fails, it causes damages of the tasks on the workstation that it is performed on and all remaining tasks to be performed in the succeeding workstations. We attribute probabilities for the successful completion and failure of the hazardous tasks and aim to maximise the total expected net revenue. We formulate the problem as a two-stage stochastic mixed-integer programme where the assignment of the tasks to the workstations is decided in the first-stage, before the resolution of the uncertainty. We give the formulation for one, two and three hazardous tasks, and then extend to the arbitrary number of hazardous tasks. Our numerical results reveal that proposed stochastic programming models return satisfactory performance and can solve instances with up to 73 tasks very quickly. We observe that the number of tasks, number of hazardous tasks and success probabilities are the most significant parameters that affect the performance. We quantify the value of capturing uncertainty using the expected objective values attained by the solution of the stochastic model and that of the expected value model, and obtain very satisfactory results.

我们考虑一个部分拆卸线平衡问题与成功完成不确定的危险任务。当任何危险任务失败时，它会导致在其上执行的工作站上的任务以及在后续工作站中执行的所有剩余任务的损坏。我们将危险任务成功完成和失败的概率归于属性，并旨在最大化预期的总净收入。我们将问题表述为一个两阶段的随机混合整数程序，其中任务分配给工作站是在第一阶段决定的，在不确定性的解决之前。我们给出了一个、两个和三个危险任务的公式，然后扩展到任意数量的危险任务。我们的数值结果表明，所提出的随机规划模型返回了令人满意的性能，并且可以非常快速地解决多达 73 个任务的实例。我们观察到任务数量、危险任务数量和成功概率是影响性能的最重要参数。我们利用随机模型的解和期望值模型的解所获得的期望目标值来量化捕获不确定性的价值，并获得了非常令人满意的结果。