Local search enhanced multi-objective PSO algorithm for scheduling textile production processes with environmental considerations

• Published in: Applied soft computing Vol. 61; pp. 447 - 467
• Main Authors: Zhang, Rui, Chang, Pei-Chann, Song, Shiji, Wu, Cheng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2017
• Subjects: Local search; Multi-objective particle swarm optimization; Pollutant emission minimization; Production scheduling; Multi-objective particle swarm optimization; Local search; Production scheduling; Pollutant emission minimization
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2017.08.013

[Display omitted] •We study a real-world-inspired scheduling problem for the textile dyeing process.•Minimization of pollutant emission is explicitly considered as an objective.•A multi-objective PSO algorithm with enhanced local search technique is proposed.•The proposed algorithm outperforms existing methods in terms of solution quality.•Robustness of the algorithm with respect to computation time budget is also tested. Textile dyeing often constitutes a bottleneck procedure in the production of clothing because the dyeing process is time-consuming and heavily constrained. Meanwhile, dyeing processes inevitably produce emissions of water pollutants especially when the involved equipment undergoes cleaning operations. Scheduling could be utilized as a system-level tool to reduce the amount of pollutant emission besides its normal role for improving the production performance (e.g., reducing delivery tardiness). To this end, we have formulated the textile dyeing process scheduling problem as a bi-objective optimization model, in which one objective is connected with tardiness cost while the other objective reflects the level of pollutant emission. Due to the NP-hard nature of the resulting problem, we have proposed a multi-objective particle swarm optimization algorithm enhanced by problem-specific local search techniques (MO-PSO-L) to seek high-quality non-dominated solutions. The proposed hybrid algorithm is characterized by a tailored solution-initialization method, a set of time-variant parameters and several unique mechanisms for dealing with multi-objective optimization (including density-oriented solution sorting and personal/global best solution handling). The local search technique based on ejection chains has been specifically designed for improving a number of promising solutions with a focus on the pollution-related objective. Superiority of the proposed solution approach has been verified by computational experiments on a large set of test instances together with fair comparisons with two state-of-the-art algorithms.