A Multi-Objective Genetic Algorithm for mixed-model sequencing on JIT assembly lines

This paper presents a Multi-Objective Genetic Algorithm (MOGA) approach to a Just-In-Time (JIT) sequencing problem where variation of production rates and number of setups are to be optimized simultaneously. These two objectives are typically inversely correlated with each other, and therefore, simultaneous optimization of both is challenging. Moreover, this type of problem is NP-hard, hence attainment of IP/LP solutions, or solutions via Total Enumeration (TE) is computationally prohibitive. The MOGA approach searches for locally Pareto-optimal or locally non-dominated frontier where simultaneous minimization of the production rates variation and the number of setups is desired. Performance of the proposed MOGA was compared against a TE scheme in small problems and also against three other search heuristics in small, medium and large problems. Experimental results show that the MOGA performs very well when compared against TE in a considerably shorter time. It also outperforms the comparator algorithms in terms of quality of solutions at the same level of diversity in reasonable amount of CPU time.