Conserving workforce while temporarily rebalancing assembly lines under demand disruption
针对需求骤降（如疫情）导致库存积压的问题，提出混合整数线性规划模型，在保留员工的前提下通过按比例减工减薪重新平衡装配线，最小化劳动力成本并公平分配社会成本。
In stable circumstances, assembly lines have workers with different capabilities assigned to stations. They perform a set of specialised tasks multiple times daily. Under a situation of high demand disruption (e.g. the COVID-19 pandemic), the overproduction rate would lead inventory levels to soar. An approach to cope with these demand drops and ongoing workforce costs is to dismiss employees and rebalance the line. Nevertheless, this implies social and economic costs related to rehiring and training. Alternatively, agreements can be made to reduce workload with a proportional wage deduction. These decisions are particularly challenging in heterogeneous workforces. We propose a Mixed-Integer Linear Programming (MILP) model to address the Assembly Line Worker Assignment and Rebalancing Problem (ALWARP). Our model aims at preserving jobs while minimising labour costs. We consider scenarios with falling demands and impose regularity metrics on workload reductions. Computational tests on benchmark datasets show that our strategy can distribute social costs among workers, with only slightly higher cumulative labour hours, while avoiding inconveniences associated with future renovations. A real-world case study of a truck cabin assembly line is investigated: the model can easily incorporate many realistic features and decide which workers should have their workload reduced and at which rate.