Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1307
Title: Single-machine scheduling with a time-dependent learning effect
Authors: Wang, Ji-Bo
Ng, Chi-to Daniel
Cheng, T. C. Edwin
Liu, L. L.
Subjects: Scheduling
Single machine
Learning effect
Time-dependent
Issue Date: Feb-2008
Publisher: Elsevier
Source: International journal of production economics, Feb. 2008, v. 111, no. 2, p. 802-811.
Abstract: In this paper we consider the single-machine scheduling problem with a time-dependent learning effect. The time-dependent learning effect of a job is assumed to be a function of the total normal processing time of the jobs scheduled in front of the job. We show by examples that the optimal schedule for the classical version of the problem is not optimal in the presence of a time-dependent learning effect for the following three objective functions: the weighted sum of completion times, the maximum lateness and the number of tardy jobs. But for some special cases, we prove that the weighted shortest processing time (WSPT) rule, the earliest due date (EDD) rule and Moore's Algorithm can construct an optimal schedule for the problem to minimize these objective functions, respectively. We use these three rules as heuristics for the general cases and analyze their worst-case error bounds. We also provide computational results to evaluate the performance of the heuristics.
Rights: International Journal of Production Economics © 2007 Elsevier B.V. The journal web site is located at http://www.sciencedirect.com.
Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/1307
DOI: 10.1016/j.ijpe.2007.03.013
ISSN: 0925-5273
Appears in Collections:LMS Journal/Magazine Articles

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