A scheduling problem with job values given as a power function of their completion times
AbstractThis paper deals with a problem of scheduling jobs on the identical parallel machines, where job values are given as a power function of the job completion times. Minimization of the total loss of job values is considered as a criterion. We establish the computational complexity of the problem - strong NP-hardness of its general version and NP-hardness of its single machine case. Moreover, we solve some special cases of the problem in polynomial time. Finally, we construct and experimentally test branch and bound algorithm (along with some elimination properties improving its efficiency) and several heuristic algorithms for the general case of the problem.
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Bibliographic InfoArticle provided by Elsevier in its journal European Journal of Operational Research.
Volume (Year): 193 (2009)
Issue (Month): 3 (March)
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Web page: http://www.elsevier.com/locate/eor
Computational complexity Job value Branch and bound Heuristic Experimental analysis;
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- Voutsinas, Theodore G. & Pappis, Costas P., 2002. "Scheduling jobs with values exponentially deteriorating over time," International Journal of Production Economics, Elsevier, vol. 79(3), pages 163-169, October.
- Wlodzimierz Szwarc & Marc E. Posner & John J. Liu, 1988. "The Single Machine Problem with a Quadratic Cost Function of Completion Times," Management Science, INFORMS, vol. 34(12), pages 1480-1488, December.
- Bachman, Aleksander & Janiak, Adam, 2000. "Minimizing maximum lateness under linear deterioration," European Journal of Operational Research, Elsevier, vol. 126(3), pages 557-566, November.
- W. Townsend, 1978. "The Single Machine Problem with Quadratic Penalty Function of Completion Times: A Branch-and-Bound Solution," Management Science, INFORMS, vol. 24(5), pages 530-534, January.
- Janiak, Adam & Krysiak, Tomasz, 2012. "Scheduling jobs with values dependent on their completion times," International Journal of Production Economics, Elsevier, vol. 135(1), pages 231-241.
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