Open Access

ARTICLE

Scheduling Flexible Flow Shop in Labeling Companies to Minimize the Makespan

by Chia-Nan Wang¹, Hsien-Pin Hsu², Hsin-Pin Fu^3,*, Nguyen Ky Phuc Phan⁴, Van Thanh Nguyen⁵

1 Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan
2 Department of Supply Chain Management, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan
3 Department of Marketing and Distribution Management, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan
4 Faculty of Industrial Engineering and Management, International University, Ho Chi Minh City, 70000, Vietnam
5 Faculty of Commerce, Van Lang University, Ho Chi Minh City, 723000, Vietnam

* Corresponding Author: Hsin-Pin Fu. Email:

(This article belongs to the Special Issue: Impact of Industry 4.0 on Supply Chain Management and Optimization)

Computer Systems Science and Engineering 2022, 40(1), 17-36. https://doi.org/10.32604/csse.2022.016992

Received 17 January 2021; Accepted 14 March 2021; Issue published 26 August 2021

Abstract

In the competitive global marketplace, production scheduling plays a vital role in planning in manufacturing. Scheduling deals directly with the time to output products quickly and with a low production cost. This research examines case study of a Radio-Frequency Identification labeling department at Avery Dennison. The main objective of the company is to have a method that allows for the sequencing and scheduling of a set of jobs so it can be completed on or before the customer’s due date to minimize the number of late orders. This study analyzes the flexible flow shop scheduling problem with a sequence dependent setup by modifying the processing time and setup time to minimize the makespan on multiple machines. Based on the defined mathematical model, this study includes an alternative approach and application of heuristic algorithm with the input being big data. Both optimization programs are used in this study and compared to determine which method can better solve the company’s problems. The proposed algorithm is able to improve machine utilization with large-scale problems.

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