An Improved Mayfly Method to Solve Distributed Flexible Job Shop Scheduling Problem under Dual Resource Constraints

Shoujing Zhang, Tiantian Hou, Qing Qu, Adam Glowacz, Samar M. Alqhtani, Muhammad Irfan, Grzegorz Królczyk and Zhixiong Li ()
Additional contact information
Shoujing Zhang: Department of Industrial Engineering, Xi’an Key Laboratory of Modern Intelligent Textile Equipment, Xi’an Polytechnic University, Xi’an 710600, China
Tiantian Hou: Department of Industrial Engineering, Xi’an Key Laboratory of Modern Intelligent Textile Equipment, Xi’an Polytechnic University, Xi’an 710600, China
Qing Qu: Department of Industrial Engineering, Xi’an Key Laboratory of Modern Intelligent Textile Equipment, Xi’an Polytechnic University, Xi’an 710600, China
Adam Glowacz: Department of Automatic, Control and Robotics, AGH University of Science and Technology, 30-059 Kraków, Poland
Samar M. Alqhtani: Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Muhammad Irfan: Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 61441, Saudi Arabia
Grzegorz Królczyk: Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland
Zhixiong Li: Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland

Sustainability, 2022, vol. 14, issue 19, 1-19

Abstract: Aiming at the distributed flexible job shop scheduling problem under dual resource constraints considering the influence of workpiece transportation time between factories and machines, a distributed flexible job shop scheduling problem (DFJSP) model with the optimization goal of minimizing completion time is established, and an improved mayfly algorithm (IMA) is proposed to solve it. Firstly, the mayfly position vector is discrete mapped to make it applicable to the scheduling problem. Secondly, three-layer coding rules of process, worker, and machine is adopted, in which the factory selection is reflected by machine number according to the characteristics of the model, and a hybrid initialization strategy is designed to improve the population quality and diversity. Thirdly, an active time window decoding strategy considering transportation time is designed for the worker–machine idle time window to improve the local optimization performance of the algorithm. In addition, the improved crossover and mutation operators is designed to expand the global search range of the algorithm. Finally, through simulation experiments, the results of various algorithms are compared to verify the effectiveness of the proposed algorithm for isomorphism and isomerism factories instances.

Keywords: dual resource constrained; distributed flexible job shop scheduling; transportation time; improved mayfly algorithm; discrete mapping (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/19/12120/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/19/12120/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:19:p:12120-:d:924739

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().