Single-machine past-sequence-dependent setup times scheduling with resource allocation and learning effect
RAIRO - Operations Research - Recherche Opérationnelle, Special issue - Advanced Optimization Approaches and Modern OR-Applications, Tome 50 (2016) no. 4-5, pp. 733-748.

This paper addresses single-machine scheduling problem with resource allocation and learning effect in the background of past-sequence-dependent (p-s-d) setup times. In the proposed model of this paper, the actual job processing times are dependent on learning effect and the amount of resource allocated, and the setup times are proportional to the length of the already processed jobs. The resource function used here is a general convex one. The optimal job sequence and the optimal amount of resource allocated to each job are determined jointly for the objective function yielded by a combination of the total completion time, total absolute differences in completion times, and the total resource consumption. Besides, we also discuss some extension and special cases of this problem. It is shown that all the problems under study are polynomially solvable while the complexity results are different.

DOI : 10.1051/ro/2016007
Classification : 90B35
Mots-clés : Scheduling, p-s-d setup times, resource allocation, learning effect
Zhu, Zhanguo 1, 2 ; Chu, Feng 2 ; Yu, Yugang 3 ; Sun, Linyan 4

1 College of Economics and Management, Nanjing Agricultural University, Nanjing 210095, P.R. China
2 Laboratoire d’informatique, biologie intégrative et systèmes complexes (IBISC), EA 4526, Université d’Evry Val d’Essonne, 91020 Evry cedex, France.
3 School of Management, University of Science and Technology of China, Hefei, P.R. China
4 School of Management, Xi’an Jiaotong University, Xi’an, 710049 Shaanxi Province, P.R. China
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     title = {Single-machine past-sequence-dependent setup times scheduling with resource allocation and learning effect},
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Zhu, Zhanguo; Chu, Feng; Yu, Yugang; Sun, Linyan. Single-machine past-sequence-dependent setup times scheduling with resource allocation and learning effect. RAIRO - Operations Research - Recherche Opérationnelle, Special issue - Advanced Optimization Approaches and Modern OR-Applications, Tome 50 (2016) no. 4-5, pp. 733-748. doi : 10.1051/ro/2016007. http://archive.numdam.org/articles/10.1051/ro/2016007/

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