An environment-driven, function-based approach to dynamic single-machine scheduling
by Arezoo Atighehchian; Mohammad Mehdi Sepehri
European J. of Industrial Engineering (EJIE), Vol. 7, No. 1, 2013

Abstract: In this paper, the dynamic single-machine scheduling problem with a sequence-dependent setup time and with minimising total weighted tardiness of jobs as the objective is investigated. Due to the dynamic nature of the problem, a function-based approach is developed that can capture dynamic characteristics associated with the environment. In order to find a function which maps the environment's states to an action at each decision point, a combination of simulated annealing and a multi-layer feed-forward neural network is employed in an algorithm named SANN. The efficiency of the proposed function-based approach is compared with the most commonly used dispatching rules and with an agent-based approach, which employs the Q-learning algorithm to develop a decision-making policy. Numerical results reveal that the proposed approach outperforms dispatching rules and the Q-learning algorithm. The mean value of the results is about 93% better than the mean of the best results obtained with dispatching rules. [Received 4 January 2010; Revised 28 September 2010, 22 February 2011, 6 June 2011, 28 June 2011; Accepted 3 July 2011].

Online publication date: Fri, 28-Feb-2014

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 European J. of Industrial Engineering (EJIE):
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