An efficient hybrid particle swarm optimization for the Job Shop scheduling problem

Xue Feng Zhang, Miyuki Koshimura, Hiroshi Fujita, Ryuzo Hasegawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

This paper proposes a hybrid particle swarm optimization algorithm for solving Job Shop Scheduling Problems (JSSP) to minimize the maximum makespan. A new hybrid heuristic, based on Particle Swarm Optimization (PSO), Tabu Search (TS) and Simulated Annealing (SA), is presented. PSO combines local search (by self-experience) with global search (by neighboring experience), achieving a high search efficiency. TS uses a memory function to avoid being trapped at a local minimum, and has emerged as an effective algorithmic approach for the JSSP. This method can also be referred to as calculation of the horizontal direction. SA employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule (also known as calculation of vertical direction). By reasonably combining these three different search algorithms, we develop a robust, fast and simply implemented hybrid optimization algorithm HPTS (Hybrid of Particle swarm optimization, Tabu search and Simulated annealing). This hybrid algorithm is applied to the standard benchmark sets and compared with other approaches. The experimental results show that the proposed algorithm could obtain the high-quality solutions within relatively short computation time. For 6 of 43 instances, new upper bounds among the unsolved problems are found in a short time in HPTS.

Original languageEnglish
Title of host publicationFUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings
Pages622-626
Number of pages5
DOIs
Publication statusPublished - Sep 27 2011
Event2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei, Taiwan, Province of China
Duration: Jun 27 2011Jun 30 2011

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Other

Other2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011
CountryTaiwan, Province of China
CityTaipei
Period6/27/116/30/11

Fingerprint

Job Shop Scheduling Problem
Hybrid Optimization
Tabu Search
Simulated Annealing
Particle swarm optimization (PSO)
Particle Swarm Optimization
Tabu search
Hybrid Algorithm
Simulated annealing
Memory Function
Global Search
Particle Swarm Optimization Algorithm
Local Minima
Local Search
Search Algorithm
Cooling
Optimization Algorithm
Schedule
Horizontal
Vertical

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

Cite this

Zhang, X. F., Koshimura, M., Fujita, H., & Hasegawa, R. (2011). An efficient hybrid particle swarm optimization for the Job Shop scheduling problem. In FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings (pp. 622-626). [6007385] (IEEE International Conference on Fuzzy Systems). https://doi.org/10.1109/FUZZY.2011.6007385

An efficient hybrid particle swarm optimization for the Job Shop scheduling problem. / Zhang, Xue Feng; Koshimura, Miyuki; Fujita, Hiroshi; Hasegawa, Ryuzo.

FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings. 2011. p. 622-626 6007385 (IEEE International Conference on Fuzzy Systems).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhang, XF, Koshimura, M, Fujita, H & Hasegawa, R 2011, An efficient hybrid particle swarm optimization for the Job Shop scheduling problem. in FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings., 6007385, IEEE International Conference on Fuzzy Systems, pp. 622-626, 2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011, Taipei, Taiwan, Province of China, 6/27/11. https://doi.org/10.1109/FUZZY.2011.6007385
Zhang XF, Koshimura M, Fujita H, Hasegawa R. An efficient hybrid particle swarm optimization for the Job Shop scheduling problem. In FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings. 2011. p. 622-626. 6007385. (IEEE International Conference on Fuzzy Systems). https://doi.org/10.1109/FUZZY.2011.6007385
Zhang, Xue Feng ; Koshimura, Miyuki ; Fujita, Hiroshi ; Hasegawa, Ryuzo. / An efficient hybrid particle swarm optimization for the Job Shop scheduling problem. FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings. 2011. pp. 622-626 (IEEE International Conference on Fuzzy Systems).
@inproceedings{98888489e597451296b3dfd18a0f5117,
title = "An efficient hybrid particle swarm optimization for the Job Shop scheduling problem",
abstract = "This paper proposes a hybrid particle swarm optimization algorithm for solving Job Shop Scheduling Problems (JSSP) to minimize the maximum makespan. A new hybrid heuristic, based on Particle Swarm Optimization (PSO), Tabu Search (TS) and Simulated Annealing (SA), is presented. PSO combines local search (by self-experience) with global search (by neighboring experience), achieving a high search efficiency. TS uses a memory function to avoid being trapped at a local minimum, and has emerged as an effective algorithmic approach for the JSSP. This method can also be referred to as calculation of the horizontal direction. SA employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule (also known as calculation of vertical direction). By reasonably combining these three different search algorithms, we develop a robust, fast and simply implemented hybrid optimization algorithm HPTS (Hybrid of Particle swarm optimization, Tabu search and Simulated annealing). This hybrid algorithm is applied to the standard benchmark sets and compared with other approaches. The experimental results show that the proposed algorithm could obtain the high-quality solutions within relatively short computation time. For 6 of 43 instances, new upper bounds among the unsolved problems are found in a short time in HPTS.",
author = "Zhang, {Xue Feng} and Miyuki Koshimura and Hiroshi Fujita and Ryuzo Hasegawa",
year = "2011",
month = "9",
day = "27",
doi = "10.1109/FUZZY.2011.6007385",
language = "English",
isbn = "9781424473175",
series = "IEEE International Conference on Fuzzy Systems",
pages = "622--626",
booktitle = "FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings",

}

TY - GEN

T1 - An efficient hybrid particle swarm optimization for the Job Shop scheduling problem

AU - Zhang, Xue Feng

AU - Koshimura, Miyuki

AU - Fujita, Hiroshi

AU - Hasegawa, Ryuzo

PY - 2011/9/27

Y1 - 2011/9/27

N2 - This paper proposes a hybrid particle swarm optimization algorithm for solving Job Shop Scheduling Problems (JSSP) to minimize the maximum makespan. A new hybrid heuristic, based on Particle Swarm Optimization (PSO), Tabu Search (TS) and Simulated Annealing (SA), is presented. PSO combines local search (by self-experience) with global search (by neighboring experience), achieving a high search efficiency. TS uses a memory function to avoid being trapped at a local minimum, and has emerged as an effective algorithmic approach for the JSSP. This method can also be referred to as calculation of the horizontal direction. SA employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule (also known as calculation of vertical direction). By reasonably combining these three different search algorithms, we develop a robust, fast and simply implemented hybrid optimization algorithm HPTS (Hybrid of Particle swarm optimization, Tabu search and Simulated annealing). This hybrid algorithm is applied to the standard benchmark sets and compared with other approaches. The experimental results show that the proposed algorithm could obtain the high-quality solutions within relatively short computation time. For 6 of 43 instances, new upper bounds among the unsolved problems are found in a short time in HPTS.

AB - This paper proposes a hybrid particle swarm optimization algorithm for solving Job Shop Scheduling Problems (JSSP) to minimize the maximum makespan. A new hybrid heuristic, based on Particle Swarm Optimization (PSO), Tabu Search (TS) and Simulated Annealing (SA), is presented. PSO combines local search (by self-experience) with global search (by neighboring experience), achieving a high search efficiency. TS uses a memory function to avoid being trapped at a local minimum, and has emerged as an effective algorithmic approach for the JSSP. This method can also be referred to as calculation of the horizontal direction. SA employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule (also known as calculation of vertical direction). By reasonably combining these three different search algorithms, we develop a robust, fast and simply implemented hybrid optimization algorithm HPTS (Hybrid of Particle swarm optimization, Tabu search and Simulated annealing). This hybrid algorithm is applied to the standard benchmark sets and compared with other approaches. The experimental results show that the proposed algorithm could obtain the high-quality solutions within relatively short computation time. For 6 of 43 instances, new upper bounds among the unsolved problems are found in a short time in HPTS.

UR - http://www.scopus.com/inward/record.url?scp=80053069998&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80053069998&partnerID=8YFLogxK

U2 - 10.1109/FUZZY.2011.6007385

DO - 10.1109/FUZZY.2011.6007385

M3 - Conference contribution

AN - SCOPUS:80053069998

SN - 9781424473175

T3 - IEEE International Conference on Fuzzy Systems

SP - 622

EP - 626

BT - FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings

ER -