Machine scheduling with deteriorating jobs and DeJong's learning effect

Min Ji; Xiaoying Tang; Xin Zhang; Edwin Tai Chiu Cheng

doi:10.1016/j.cie.2015.10.015

Machine scheduling with deteriorating jobs and DeJong's learning effect

Min Ji, Xiaoying Tang, Xin Zhang, Edwin Tai Chiu Cheng

Research output: Journal article publication › Journal article › Academic research › peer-review

44 Citations (Scopus)

Abstract

We consider parallel-machine scheduling with deteriorating jobs and DeJong's learning effect. We focus on the problems to minimize the total completion time and the makespan. We show that the former is polynomially solvable, while the latter is NP-hard, for which we provide a fully polynomial-time approximation scheme.

Original language	English
Pages (from-to)	42-47
Number of pages	6
Journal	Computers and Industrial Engineering
Volume	91
DOIs	https://doi.org/10.1016/j.cie.2015.10.015
Publication status	Published - 1 Jan 2016

Keywords

DeJong's learning effect
FPTAS
Job deterioration
Scheduling

ASJC Scopus subject areas

General Computer Science
General Engineering

More information

10.1016/j.cie.2015.10.015

Cite this

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abstract = "We consider parallel-machine scheduling with deteriorating jobs and DeJong's learning effect. We focus on the problems to minimize the total completion time and the makespan. We show that the former is polynomially solvable, while the latter is NP-hard, for which we provide a fully polynomial-time approximation scheme.",

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AB - We consider parallel-machine scheduling with deteriorating jobs and DeJong's learning effect. We focus on the problems to minimize the total completion time and the makespan. We show that the former is polynomially solvable, while the latter is NP-hard, for which we provide a fully polynomial-time approximation scheme.

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KW - Job deterioration

KW - Scheduling

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JO - Computers and Industrial Engineering

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Machine scheduling with deteriorating jobs and DeJong's learning effect

Abstract

Keywords

ASJC Scopus subject areas

More information

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