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 language | English |
|---|---|
| Title of host publication | FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings |
| Pages | 622-626 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - Sep 27 2011 |
| Event | 2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei, Taiwan, Province of China Duration: Jun 27 2011 → Jun 30 2011 |
Publication series
| Name | IEEE International Conference on Fuzzy Systems |
|---|---|
| ISSN (Print) | 1098-7584 |
Other
| Other | 2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 |
|---|---|
| Country | Taiwan, Province of China |
| City | Taipei |
| Period | 6/27/11 → 6/30/11 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Software
- Theoretical Computer Science
- Artificial Intelligence
- Applied Mathematics
Cite this
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 proceeding › Conference contribution
}
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.
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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 -