A multi-objective artificial bee colony algorithm for parallel batch-processing machine scheduling in fabric dyeing processes

Fabric dyeing is a critical production process in the clothing industry. Characterized by high energy consumption and water pollutant emission, dyeing processes need careful scheduling in order to reduce the relevant direct and indirect costs. In this paper, we describe the dyeing process scheduling problem as a bi-objective parallel batch-processing machine scheduling model, in which the first objective function reflects the tardiness cost and the second objective function concerns the utilization rate of dyeing vats. To obtain satisfactory schedules within reasonable time, we propose an efficient multi-objective artificial bee colony (MO-ABC) algorithm to solve the scheduling problem. The proposed algorithm features a specialized encoding scheme, a problem-specific initialization technique and several unique functions to deal with multi-objective optimization. After preliminary tuning of parameters, we use a set of 90 instances with up to 300 jobs to test the MO-ABC algorithm. Extensive experiments show that the MO-ABC outperforms a generic multi-objective scheduling algorithm in terms of both solution quality and computational time robustness.