Single-machine scheduling with a time-dependent learning effect

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...

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Bibliographic Details
Published in:International journal of production economics 2008-02, Vol.111 (2), p.802-811
Main Authors: Wang, J.-B., Ng, C.T., Cheng, T.C.E., Liu, L.L.
Format: Article
Language:English
Subjects:
Heuristic
Job shops
Learning
Learning effect
Optimization algorithms
Production scheduling
Scheduling
Single machine
Studies
Time-dependent
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Summary: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.
ISSN:0925-5273
1873-7579
DOI:10.1016/j.ijpe.2007.03.013