Computer Science and Information Technology Vol. 1(3), pp. 208 - 224
DOI: 10.13189/csit.2013.010307
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Using Simulated Annealing and Ant-Colony Optimization Algorithms to Solve the Scheduling Problem

Nader Chmait , Khalil Challita ^*
Department of Computer Sciences, Faculty of Natural and Applied Sciences, Notre-Dame University, Louaize, Zouk Mos-beh, Lebanon

ABSTRACT

The scheduling problem is one of the most challenging problems faced in many different areas of everyday life. This problem can be formulated as a combinatorial optimization problem, and it has been solved with various methods using meta-heuristics and intelligent algorithms. We present in this paper a solution to the scheduling problem using two different heuristics namely Simulated Annealing and Ant Colony Optimization. A study comparing the performances of both solutions is described and the results are analyzed.

KEYWORDS
ACO, Ant Colony Optimization, SA, Simulated Annealing, Scheduling Problem, Exam Scheduling, Timetabling

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Nader Chmait , Khalil Challita , "Using Simulated Annealing and Ant-Colony Optimization Algorithms to Solve the Scheduling Problem," Computer Science and Information Technology, Vol. 1, No. 3, pp. 208 - 224, 2013. DOI: 10.13189/csit.2013.010307.

(b). APA Format:
Nader Chmait , Khalil Challita (2013). Using Simulated Annealing and Ant-Colony Optimization Algorithms to Solve the Scheduling Problem. Computer Science and Information Technology, 1(3), 208 - 224. DOI: 10.13189/csit.2013.010307.