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Bi-objective hybrid flow shop scheduling with common due date

Zhi Li, Ray Y. Zhong, Ali Vatankhah Barenji (), J. J. Liu, C. X. Yu and George Q. Huang
Additional contact information
Zhi Li: School of Electromechanical Engineering, Guangdong University of Technology
Ray Y. Zhong: The University of Hong Kong
Ali Vatankhah Barenji: School of Electromechanical Engineering, Guangdong University of Technology
J. J. Liu: School of Electromechanical Engineering, Guangdong University of Technology
C. X. Yu: China University of Petroleum-Beijing
George Q. Huang: The University of Hong Kong

Operational Research, 2021, vol. 21, issue 2, No 15, 1153-1178

Abstract: Abstract In this paper, the problem of hybrid flow shop scheduling with common due dates (HFSCDD) is studied, and the objectives are to minimize the total waiting time and the total earliness/tardiness issues that arise. This study was motivated by a real-life shop floor, with the predefined goal of meeting the requirements of the final product in many manufacturing industries. Where the final product is assembled from multiple components and, the assembly is only initiated when all components of the product are complete in number. These interrelated components have common due dates. In this study, we developed a mathematical model of HFSCDD which made up of “n” jobs that were processed in “m” machines, located on “I” stages by taking into consideration the common due dates. This problem is classified as being NP-hard, and so an efficient modified genetic algorithm is developed to solve it. The proposed modify GA is developed based on the NSGA II method for large sized problems. The results of the proposed algorithm have been compared with PSO and GA algorithms and showed that the proposed algorithm achieved better performance than existing solutions, since the waiting time and the earliness/tardiness are significantly reduced. This is facilitated by the simultaneous production of components for the same product.

Keywords: Hybrid flow shop scheduling; Multi-objective; Genetic algorithm; Common due date (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s12351-019-00470-8

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