A parallel hybrid ant colony optimisation approach for job-shop scheduling problem
by Haipeng Zhang, Mitsuo Gen
International Journal of Manufacturing Technology and Management (IJMTM), Vol. 16, No. 1/2, 2009

Abstract: In this paper, we combine ACO with some randomised dispatching heuristics and propose a special transition rule for finding the best schedule to the JSP problems. Moreover, a special critical path-based local search is also combined to improve the best solutions by reducing the idle time. In order to gain higher efficiency of the proposed algorithm and avoid the early convergence in local optimal solution, we enhance the hybrid ACO by building a parallel hybrid Ant Colony Optimisation (ph-ACO) algorithm. Some numerical examples are used to demonstrate the performance of the ph-ACO and we can find that the proposed ph-ACO algorithm with Longest Remaining processing Time (LRT) and Longest Remaining processing time Excluding the operation under consideration (LRE) can both improve the efficiency of the algorithm obviously. Furthermore, we also decide the appropriate parameter setting of β is around 2. Finally, after comparing with hybrid Genetic Algorithm (GA) by solving same benchmark problems, the experimental results show the proposed ph-ACO outperforms traditional ACO and hybrid GA.

Online publication date: Sun, 30-Nov-2008

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Manufacturing Technology and Management (IJMTM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com