Open Access

ARTICLE

Optimization Model in Manufacturing Scheduling for the Garment Industry

Chia-Nan Wang¹, Yu-Chen Wei², Po-Yuk So^3,*, Viet Tinh Nguyen⁴, Phan Nguyen Ky Phuc⁵

1 Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan
2 Department of Money and Banking, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan
3 Ph.D. Program in Finance and Banking, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan
4 Faculty of Commerce, Van Lang University, Ho Chi Minh City, Vietnam
5 Faculty of Industrial Engineering and Management, International University, Ho Chi Minh City, 70000, Vietnam

* Corresponding Author: Po-Yuk So. Email:

(This article belongs to the Special Issue: Big Data for Supply Chain Management in the Service and Manufacturing Sectors)

Computers, Materials & Continua 2022, 71(3), 5875-5889. https://doi.org/10.32604/cmc.2022.023880

Received 24 September 2021; Accepted 06 December 2021; Issue published 14 January 2022

Abstract

The garment industry in Vietnam is one of the country's strongest industries in the world. However, the production process still encounters problems regarding scheduling that does not equate to an optimal process. The paper introduces a production scheduling solution that resolves the potential delays and lateness that hinders the production process using integer programming and order allocation with a make-to-order manufacturing viewpoint. A number of constraints were considered in the model and is applied to a real case study of a factory in order to view how the tardiness and lateness would be affected which resulted in optimizing the scheduling time better. Specifically, the constraints considered were order assignments, production time, and tardiness with an objective function which is to minimize the total cost of delay. The results of the study precisely the overall cost of delay of the orders given to the plant and successfully propose a suitable production schedule that utilizes the most of the plant given. The study has shown promising results that would assist plant and production managers in determining an algorithm that they can apply for their production process.

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Keywords

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