Task-failure-driven rebalancing of disassembly lines

Many reverse-logistics systems that collect and reprocess end-of-life products require a disassembly stage. The variability of incoming products and the extent of damage, which is more likely to occur during disassembly than assembly, create significant uncertainty in disassembly tasks, namely the possibility of failed tasks. Such failures may lead to some successor tasks being infeasible, which changes the work content of downstream stations. To improve the profitability of such a disassembly line, a mixed-integer-programming-based, predictive-reactive approach is proposed. In the first step, a predictive balance is created and then, in the second step, given a task failure, the tasks of the disassembled product with that task failure are re-selected and re-assigned to the stations (i.e. the line is rebalanced). In the second step, the objective function models both the profit obtained from the disassembled product and the possible increase in any station's workload beyond the predictive cycle time. Since this rebalancing approach affects the work content of stations, a discrete-event simulation study is also carried out to analyse and compare the performance of disassembly lines for optimally found line balances (predictive and reactive). The results show that, with the proposed approach, 24-32% of the monetary throughput lost due to not taking corrective action can be recovered.