Conserving workforce while temporarily rebalancing assembly lines under demand disruption

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.