A scatter search algorithm for scheduling optimisation of job shop problems Online publication date: Thu, 03-Dec-2009
by M. Saravanan, A. Noorul Haq
International Journal of Product Development (IJPD), Vol. 10, No. 1/2/3, 2010
Abstract: The Job Shop Scheduling (JSS) problem is one of the most difficult NP-hard combinatorial optimisation problems. it is very difficult to solve by conventional optimisation techniques. There has been increasing interest in applying metaheuristic methods to solve such hard optimisation problems. In this work, a novel metaheuristic approach called Scatter Search (SS) is applied for the JSS problem, an NP-hard sequencing problem. The approach is used to find a schedule to minimise the makespan (Cmax), that is, the time required to complete all jobs. SS contrasts with other evolutionary procedures by providing a wide exploration of the search space through intensification and diversification. In addition, it has a unifying principle for joining solutions. It exploits the adaptive memory principle to avoid generating or incorporating duplicate solutions at various stages of the problem. The main aim of this research is to explore the potential of SS for scheduling JSS problems. SS provides unifying principles for joining solutions based on generalised path constructions and by utilising strategic designs where other approaches resort to randomisation. In this paper, 11 benchmark problems are taken from the literature. The results available for the various existing metaheuristic methods for the selected benchmark problems are compared with results obtained by the SS method. The proposed framework achieves better results for all 11 problems.
Online publication date: Thu, 03-Dec-2009
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 Product Development (IJPD):
Login with your Inderscience username and 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 email@example.com