Two robust meta-heuristics for scheduling multiple job classes on a single machine with multiple criteria

Authors:
R. Soltani;F. Jolai;M. Zandieh
Affiliations:
Department of Industrial and Mechanical Engineering, Azad University, Qazvin, Iran;Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran;Faculty of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 7
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Abstract

We consider a single machine scheduling problem consisted of two groups of jobs with two different criteria that are minimizing total weighted completion time for the first group and minimizing maximum lateness for the second one. This problem which minimizes a mix of these criteria is in the NP-hard class of problems. Hence, inevitably we make use of meta-heuristic methods to tackle large scale problems. In this paper two meta-heuristics such as genetic algorithm and hybrid kangaroo simulated annealing are taken into consideration. Taguchi method is employed to tune the parameters of these algorithms and analyze the parameters of the studying problem simultaneously.