Research on permutation flow shop scheduling problems with general position-dependent learning effects

Lin-Hui Sun (), Kai Cui, Ju-Hong Chen, Jun Wang and Xian-Chen He

Annals of Operations Research, 2013, vol. 211, issue 1, 473-480

Abstract: Machine learning exists in many realistic scheduling situations. This study focuses on permutation flow shop scheduling problems, where the actual processing time of a job is defined by a general non-increasing function of its scheduled position, i.e., general position-dependent learning effects. The objective functions are to minimize the total completion time, the makespan, the total weighted completion time, and the total weighted discounted completion time, respectively. To solve these problems, we present approximation algorithms based on the optimal permutations for the corresponding single machine scheduling problems and analyze their worst-case error bound. Copyright Springer Science+Business Media New York 2013

Keywords: Scheduling; Flow shop; Learning effect (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10479-013-1481-6

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