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International Journal of Crowd Science 2023, 7(1): 1-9 https://doi.org/10.26599/IJCS.2022.9100024
Open Access | Issue | Published: 31 March 2023

Production Scheduling of Regional Industrial Clusters Based on Customization Oriented Smart Garment Ecosystem

Show Author's Information Zhishuo Liu1( ),  Simeng Lin1,  Han Li1
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Keywords:
garment customization, Smart Garment Ecosystem (SG-ECO), collaborative production scheduling, improved genetic algorithm (IGA)
Cite this article:
Liu Z, Lin S, Li H. Production Scheduling of Regional Industrial Clusters Based on Customization Oriented Smart Garment Ecosystem. International Journal of Crowd Science, 2023, 7(1): 1-9. https://doi.org/10.26599/IJCS.2022.9100024
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Abstract Full text About this article

This paper presents the Smart Garment Ecosystem (SG-ECO) and enriches the garment customization production scheduling model theory. On the basis of SG-ECO, the author designs a regional collaborative production alliance (RCPA) based on the idea of collaborative production management and then establishes a flexible production scheduling model (FPSM) that is oriented to the RCPA model under multiple constraints and aims to maximize weighted cost savings. The RCPA model and research on FPSM can enrich the theoretical system of production scheduling research to a certain extent and provide new ideas for the latter’s research on customized production scheduling. Although the calculation example proves that the genetic algorithm based on double-layer integer coding (DIC-GA) can effectively solve the FPSM problem, the feasible solution space of the algorithm increases when the order size increases, and the number of iterations and search time required gradually increase. An improved genetic algorithm with double-layer integer coding for processes and workshops is designed, which can not only ensure that each chromosome is a legal individual but also reduce the complexity of the algorithm. The crossover operation and compilation operation are designed based on the coding method to ensure that the genetic operations in the algorithm can produce feasible solutions.


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About this article

Production Scheduling of Regional Industrial Clusters Based on Customization Oriented Smart Garment Ecosystem

Show Author's information Zhishuo Liu1( ), Simeng Lin1, Han Li1
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China

Abstract

This paper presents the Smart Garment Ecosystem (SG-ECO) and enriches the garment customization production scheduling model theory. On the basis of SG-ECO, the author designs a regional collaborative production alliance (RCPA) based on the idea of collaborative production management and then establishes a flexible production scheduling model (FPSM) that is oriented to the RCPA model under multiple constraints and aims to maximize weighted cost savings. The RCPA model and research on FPSM can enrich the theoretical system of production scheduling research to a certain extent and provide new ideas for the latter’s research on customized production scheduling. Although the calculation example proves that the genetic algorithm based on double-layer integer coding (DIC-GA) can effectively solve the FPSM problem, the feasible solution space of the algorithm increases when the order size increases, and the number of iterations and search time required gradually increase. An improved genetic algorithm with double-layer integer coding for processes and workshops is designed, which can not only ensure that each chromosome is a legal individual but also reduce the complexity of the algorithm. The crossover operation and compilation operation are designed based on the coding method to ensure that the genetic operations in the algorithm can produce feasible solutions.

Keywords: garment customization, Smart Garment Ecosystem (SG-ECO), collaborative production scheduling, improved genetic algorithm (IGA)

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Received: 14 March 2022
Revised: 24 June 2022
Accepted: 27 June 2022
Published: 31 March 2023
Issue date: March 2023

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