A multiple objective optimization in mixed-model assembly line sequencing on JIT systems

Mixed model assembly lines are a type of production line where a variety of product models similar in product characteristics are assembled. The effective utilization of these lines requires a schedule for assembling the different products to be determined. This paper considered two objectives variation in parts utilization at the different workstations of the assembly line (PU) and variation of the workload associated with each workstation (WL), these two objectives are typically inversely correlated with each other, and therefore, simultaneously optimization of both is challenging. Due to the NP-hardness of the problem, this paper introduces a Memetic Algorithm (MA) to solve a Just-In-Time (JIT) sequencing problem where these objectives are to be optimized simultaneously. Performance of the MA was compared against two other search heuristics weighted sum multi objective genetic algorithm (MOGAW) and Non-dominated sorting genetic algorithm II (NSGA-II) in small, medium and large problems. To compare presented algorithm with previous ones, an extensive computational study on 3 sets of benchmark problems has been conducted. Experimental results showed that our algorithm outperforms the previous algorithms, in respect of comparison metric.