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Single-machine scheduling with general learning functions

โœ Scribed by Ji-Bo Wang

Publisher
Elsevier Science
Year
2008
Tongue
English
Weight
262 KB
Volume
56
Category
Article
ISSN
0898-1221

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โœฆ Synopsis

Learning effect Sum of completion times square Weighted sum of completion times Maximum lateness Number of tardy jobs a b s t r a c t

In this study we consider the single-machine scheduling problem with a sum-ofprocessing-times-based learning effect. The sum-of-processing-times-based learning effect of a job is assumed to be a function of the sum of the normal processing times of the already processed jobs. We prove that the shortest processing time (SPT) rule is optimal for the sum of completion times square minimization problem. We also show by examples that the optimal schedule for the classical version of the problem is not optimal in the presence of a sum-of-processing-times-based 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.

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