Approximation Algorithms for Multitasking Scheduling Problems

In this work, we incorporate human factors and real-life operations into newly proposed multitasking scheduling problems with periodic shift activities. It is motivated by personnel resource scheduling with periodic work shifts under the requirement of providing continuous service to customers. We m...

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Main Authors:	Feifeng Zheng, Zhaojie Wang, Ming Liu, Chengbin Chu
Format:	Article
Language:	English
Published:	IEEE 2020-01-01
Series:	IEEE Access
Subjects:	Multitasking scheduling periodic shift approximation algorithm
Online Access:	https://ieeexplore.ieee.org/document/9134734/

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spelling	doaj-c07c4861d30e4ef9bff71635ee7b8da12021-03-30T02:06:45ZengIEEEIEEE Access2169-35362020-01-01812753012753410.1109/ACCESS.2020.30077559134734Approximation Algorithms for Multitasking Scheduling ProblemsFeifeng Zheng0https://orcid.org/0000-0002-1603-4163Zhaojie Wang1https://orcid.org/0000-0002-5295-1794Ming Liu2https://orcid.org/0000-0003-3190-5008Chengbin Chu3Glorious Sun School of Business and Management, Donghua University, Shanghai, ChinaGlorious Sun School of Business and Management, Donghua University, Shanghai, ChinaSchool of Economics and Management, Tongji University, Shanghai, ChinaSchool of Economics and Management, Fuzhou University, Fuzhou, ChinaIn this work, we incorporate human factors and real-life operations into newly proposed multitasking scheduling problems with periodic shift activities. It is motivated by personnel resource scheduling with periodic work shifts under the requirement of providing continuous service to customers. We model the problem as two identical parallel machine scheduling with complementary non-available time periods, and consider two models with the objectives of the makespan, i.e. the maximum completion time and respectively the total completion time. We then prove that the Greedy algorithm and SPT rule are of asymptotic and parametric approximation ratios for the two models, respectively.https://ieeexplore.ieee.org/document/9134734/Multitaskingschedulingperiodic shiftapproximation algorithm
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Feifeng Zheng Zhaojie Wang Ming Liu Chengbin Chu
spellingShingle	Feifeng Zheng Zhaojie Wang Ming Liu Chengbin Chu Approximation Algorithms for Multitasking Scheduling Problems IEEE Access Multitasking scheduling periodic shift approximation algorithm
author_facet	Feifeng Zheng Zhaojie Wang Ming Liu Chengbin Chu
author_sort	Feifeng Zheng
title	Approximation Algorithms for Multitasking Scheduling Problems
title_short	Approximation Algorithms for Multitasking Scheduling Problems
title_full	Approximation Algorithms for Multitasking Scheduling Problems
title_fullStr	Approximation Algorithms for Multitasking Scheduling Problems
title_full_unstemmed	Approximation Algorithms for Multitasking Scheduling Problems
title_sort	approximation algorithms for multitasking scheduling problems
publisher	IEEE
series	IEEE Access
issn	2169-3536
publishDate	2020-01-01
description	In this work, we incorporate human factors and real-life operations into newly proposed multitasking scheduling problems with periodic shift activities. It is motivated by personnel resource scheduling with periodic work shifts under the requirement of providing continuous service to customers. We model the problem as two identical parallel machine scheduling with complementary non-available time periods, and consider two models with the objectives of the makespan, i.e. the maximum completion time and respectively the total completion time. We then prove that the Greedy algorithm and SPT rule are of asymptotic and parametric approximation ratios for the two models, respectively.
topic	Multitasking scheduling periodic shift approximation algorithm
url	https://ieeexplore.ieee.org/document/9134734/
work_keys_str_mv	AT feifengzheng approximationalgorithmsformultitaskingschedulingproblems AT zhaojiewang approximationalgorithmsformultitaskingschedulingproblems AT mingliu approximationalgorithmsformultitaskingschedulingproblems AT chengbinchu approximationalgorithmsformultitaskingschedulingproblems
_version_	1724185777376591872

Approximation Algorithms for Multitasking Scheduling Problems

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