An efficient optimisation procedure for the Workforce Scheduling and Routing Problem: Lagrangian relaxation and iterated local search

Gu, H; Zhang, Y; Zinder, Y

An efficient optimisation procedure for the Workforce Scheduling and Routing Problem: Lagrangian relaxation and iterated local search

Gu, H

Zhang, Y Zinder, Y

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Computers and Operations Research, 2022, 144
Issue Date:: 2022-08-01

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Field	Value	Language
dc.contributor.author	Gu, H https://orcid.org/0000-0003-2035-2583
dc.contributor.author	Zhang, Y
dc.contributor.author	Zinder, Y https://orcid.org/0000-0003-2024-8129
dc.date.accessioned	2023-01-31T02:02:11Z
dc.date.available	2023-01-31T02:02:11Z
dc.date.issued	2022-08-01
dc.identifier.citation	Computers and Operations Research, 2022, 144
dc.identifier.issn	0305-0548
dc.identifier.issn	1873-765X
dc.identifier.uri	http://hdl.handle.net/10453/165642
dc.description.abstract	The paper is concerned with the Workforce Scheduling and Routing Problem where certain service providers complete tasks at different locations. The presented optimisation procedure is an amalgamation of the iterated local search and Lagrangian relaxation. This optimisation procedure has been tested on benchmark problems from the literature, and showed superior performance in comparison with a previously published implementation of the iterated local search.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Computers and Operations Research
dc.relation.isbasedon	10.1016/j.cor.2022.105829
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0103 Numerical and Computational Mathematics
dc.subject.classification	Operations Research
dc.title	An efficient optimisation procedure for the Workforce Scheduling and Routing Problem: Lagrangian relaxation and iterated local search
dc.type	Journal Article
utslib.citation.volume	144
utslib.for	0102 Applied Mathematics
utslib.for	0103 Numerical and Computational Mathematics
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access	*
dc.date.updated	2023-01-31T02:01:53Z
pubs.publication-status	Published
pubs.volume	144

Abstract:

The paper is concerned with the Workforce Scheduling and Routing Problem where certain service providers complete tasks at different locations. The presented optimisation procedure is an amalgamation of the iterated local search and Lagrangian relaxation. This optimisation procedure has been tested on benchmark problems from the literature, and showed superior performance in comparison with a previously published implementation of the iterated local search.

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http://hdl.handle.net/10453/165642