Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/104564
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Title: Resource-dependent scheduling with deteriorating jobs and learning effects on unrelated parallel machine
Authors: Lu, YY
Jin, J
Ji, P 
Wang, JB
Issue Date: Oct-2016
Source: Neural computing and applications, Oct. 2016, v. 27, no. 7, p. 1993-2000
Abstract: The focus of this paper is to analyze unrelated parallel-machine resource allocation scheduling problem with learning effect and deteriorating jobs. The goal is to find the optimal sequence of jobs and the optimal resource allocation separately for minimizing the cost function including the total load, the total completion time, the total absolute deviation of completion time and the total resource cost. We show that the problem is polynomial time solvable if the number of machines is a given constant.
Keywords: Deteriorating jobs
Learning effect
Parallel machine
Resource allocation
Scheduling
Publisher: Springer UK
Journal: Neural computing and applications 
ISSN: 0941-0643
EISSN: 1433-3058
DOI: 10.1007/s00521-015-1993-x
Rights: © The Natural Computing Applications Forum 2015
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s00521-015-1993-x.
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