期刊
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
卷 111, 期 2, 页码 802-811出版社
ELSEVIER
DOI: 10.1016/j.ijpe.2007.03.013
关键词
scheduling; single machine; learning effect; time-dependent
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. (C) 2007 Elsevier B.V. All rights reserved.
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