A hybrid multi-subpopulation genetic algorithm for textile batch dyeing scheduling and an empirical study. (November 2018) - Huynh, Nhat-To; Chien, Chen-Fu
Graphical abstract: A hybrid multi-subpopulation GA (MSGA-H) is developed for textile batch dyeing scheduling efficiently to minimize the makesapn effectively for smart production. Highlights: A hybrid multi-subpopulation GA is developed for textile batch dyeing scheduling. An empirical study was conducted in a world leading functional textile company. The proposed MSGA-H can improve solution quality effectively. The results have validated the proposed approach in real settings. Abstract: Multi-functional textile has been increasingly employed for various usages in sports, outdoor, city, casual and industrial materials. Due to shortening product life cycles of consumer era, textile batch dyeing scheduling problem that can be modeled as the parallel batch processing machines with arbitrary job size, incompatible job family, different due date, and sequence-dependent setup time has increasingly complicated product mix, while smart production is needed. To migrate for Industry 4.0, this study aims to develop a multi-subpopulation genetic algorithm with heuristics embedded (MSGA-H) to minimize the makespan to improve the textile batch dyeing scheduling that is the bottleneck. In addition, an approach that combines the state-of-art methods of batch processing scheduling is developed for reference solutions. To estimate the validity of the proposed MSGA-H, an empirical study was conducted in a world leading vertically integrated textile manufacturer in Taiwan with different scenarios based on real settings. The results have shown practical viability of the proposed MSGA-H. This study concludes with a discussion of contributions and future research directions for smart production in emerging countries.