Optimizing the Reliability and Efficiency for an Assembly Line That Considers Uncertain Task Time Attributes

An assembly line is an industrial arrangement of machines, equipment, and operators for continuous flow of workpieces in mass-production operations. In an assembly line balancing problem, tasks are allocated to workstations according to their processing times and precedence relationships amongst tasks. Nowadays, some research investigated the reliability of assembly production by taking account of task time uncertainties. Our research utilizes uncertainty theory to model task time uncertainties and introduces the belief reliability measure to the assembly line production for the first time. We proposed a multi-objective optimization model that aimed at maximizing the belief reliability and minimizing the cycle time. The problem is solved using a newly developed restart neighborhood search method. The numerical experiments are conducted to verify its efficiency. The methodology proposed in this paper is applicable to any industry (including the automotive industry) when the historical data on task processing times are very scarce.