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 A hybrid multi-subpopulation genetic algorithm for textile batch dyeing scheduling and an empirical study
Tác giả hoặc Nhóm tác giả: Nhat-To Huynh, Chen-Fu Chien
Nơi đăng: Computers & Industrial Engineering; Số: https://doi.org/10.1016/j.cie.2018.01.005;Từ->đến trang: https://doi.org/10.1016/j.cie.2018.01.005;Năm: 2018
Lĩnh vực: Kỹ thuật; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
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.
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.
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