Hybrid multi-objective evolutionary meta-heuristics for a parallel machine scheduling problem with setup times and preferences

Fabric dyeing is a vital production process of textile products. In the dyeing process, orders are scheduled to machines, and the sequence of jobs on a machine is crucial since the machines require washing out that depends on the type of consecutive orders. This washing out process is a significant cost factor of the dyeing process because of the chemicals used, which causes sequence dependent setup times. Also, the order of the products to be processed is important in terms of quality that depends on color and shade of the jobs. In this paper, two metaheuristics, namely a non-dominated sorting genetic algorithm (NSGA-II) and multi-objective evolutionary algorithm based on decomposition (MOEA/D) are presented to solve the multi-objective scheduling problem for the dyeing process with sequence dependent setup times, in which the objectives are makespan, tardiness, total number of setups, color preference, and shade difference. Two different gene designs and corresponding feasible schedule generation methods - one heuristic based and one integer programming based, which are integrated within the metaheuristic framework, are proposed. Finally, proposed algorithms are compared based on single and multiple objective analysis. From the tests conducted, we observed that the use of hybrid-optimal approaches integrated within the meta-heuristic frameworks provide higher quality solutions but suffer from longer computation times.

Automated summary

This paper presents two metaheuristics for solving the multi-objective scheduling problem in the dyeing process. Through comparative analysis, it is found that hybrid-optimal approaches provide higher quality solutions but suffer from longer computation times.