Robust mixed-model assembly line balancing and sequencing problem considering preventive maintenance scenarios with interval processing times

Considering preventive maintenance scenarios (PMS) in assembly line balancing problems can significantly influence machine reliability and production continuity. However, no research considers PMS in robust mixed-model assembly line balancing and sequencing problems (RMALBSP) with interval processing times. This paper designs a robust mathematical model to solve RMALBSP considering PMS (RMALBSP_PMS) with two types of objectives: makespan and task alteration. Then, it is linearized by duality to be directly solved by Cplex. We also develop a multi-objective cooperative differential evolution algorithm (MOCDE) to solve large-scale instances. In this algorithm, we design a coevolutionary mechanism to partition the RMALBSP and optimize each part separately. The priority-based encoding and decoding methods are proposed to represent solutions in each population. Evolutionary operators for populations and the archive are designed to explore the solution space. Four neighbor search operators are developed to replace repetitive solutions. The first experimental result illustrates the model can obtain the Pareto solutions of small-scale instances, and the MOCDE is more suitable for solving large-scale instances. The remaining two experiments demonstrate that the designed coevolutionary mechanism and improvements are effective, and the MOCDE algorithm outperforms eight state-of-the-art algorithms.