Open Access

ARTICLE

Flow-Shop Scheduling with Transportation Capacity and Time Consideration

by Chia-Nan Wang¹, Glen Andrew Porter², Ching-Chien Huang^3,*, Viet Tinh Nguyen⁴, Syed Tam Husain⁴

1 Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan
2 College of Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan
3 Department of Mechanical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan
4 Faculty of Commerce, Van Lang University, Ho Chi Minh City, 70000, Vietnam

* Corresponding Author: Ching-Chien Huang. Email:

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

Computers, Materials & Continua 2022, 70(2), 3031-3048. https://doi.org/10.32604/cmc.2022.020222

Received 15 May 2021; Accepted 29 June 2021; Issue published 27 September 2021

Abstract

Planning and scheduling is one of the most important activity in supply chain operation management. Over the years, there have been multiple researches regarding planning and scheduling which are applied to improve a variety of supply chains. This includes two commonly used methods which are mathematical programming models and heuristics algorithms. Flowshop manufacturing systems are seen normally in industrial environments but few have considered certain constraints such as transportation capacity and transportation time within their supply chain. A two-stage flowshop of a single processing machine and a batch processing machine are considered with their capacity and transportation time between two machines. The objectives of this research are to build a suitable mathematical model capable of minimizing the maximum completion time, to propose a heuristic optimization algorithm to solve the problem, and to develop an applicable program of the heuristics algorithm. A Mixed Integer Programming (MIP) model and a heuristics optimization algorithm was developed and tested using a randomly generated data set for feasibility. The overall results and performance of each approach was compared between the two methods that would assist the decision maker in choosing a suitable solution for their manufacturing line.

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Keywords

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