Skip to Main Content
Notice of Violation of IEEE Publication Principles
"Job Shop Scheduling Problem with an Novel Particle Swarm Optimization based on Tabu Search"
by Dejia Shi, Li Wang, Jing He
in the Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence, Nov. 2009
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.
This paper contains significant portions of original text from the papers cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper titles) and without permission.
Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following articles:
"A Hybrid Particle Swarm Optimization for Job Shop Scheduling Problem"
by D.Y. Sha and Cheng-Yu Hsu
in Computers and Industrial Engineering, Vol 51, No 4, Elsevier, December 2006, pp. 791-808
Scheduling for the flexible job-shop is very important in both fields of production management and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. The combining of several optimization criteria induces additional complexity and new problems. Particle swarm optimization is an evolutionary computation technique mimicking the behavior of flying birds and their means of information exchange. It combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. We modified the particle position based on preference list-based representation, particle movement based on swap operator, and particle velocity based on the tabu list conc- ept in our algorithm. Giffler and Thompson's heuristic is used to decode a particle position into a schedule. Furthermore, we applied tabu search to improve the solution quality. The computational results show that the modified PSO performs better than the original design, and that the hybrid PSO is better than other traditional metaheuristics.