Resource constrained project scheduling with uncertain activity durations

Chakrabortty, RK; Sarker, RA; Essam, DL

Resource constrained project scheduling with uncertain activity durations

Chakrabortty, RK

Sarker, RA Essam, DL

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Publication Type:: Journal Article
Citation:: Computers and Industrial Engineering, 2017, 112 pp. 537 - 550
Issue Date:: 2017-10-01

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Field	Value	Language
dc.contributor.author	Chakrabortty, RK https://orcid.org/0000-0002-7373-0149	en_US
dc.contributor.author	Sarker, RA	en_US
dc.contributor.author	Essam, DL	en_US
dc.date.issued	2017-10-01	en_US
dc.identifier.citation	Computers and Industrial Engineering, 2017, 112 pp. 537 - 550	en_US
dc.identifier.issn	0360-8352	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124424
dc.description.abstract	© 2017 Elsevier Ltd In this paper, we consider Resource Constrained Project Scheduling Problems (RCPSPs) with known deterministic renewable resource requirements but uncertain activity durations. In this case, the activity durations are represented by random variables with different probability distribution functions. To deal with this problem, we propose an approach based on the robust optimization concept, which produces reasonably good solutions under any likely input data scenario. Depending on different uncertainty characteristics, we have developed six different heuristics to incorporate the uncertain duration as a deterministic constraint in a robust optimization model. The resulting optimization model is then solved by using a Coin-Branch & Cut (CBC) solver. To judge the performance of the algorithm, we solved 30, 60, 90 and 120-activity benchmark problems from the project scheduling problem library (PSPLIB). Our proposed approach guarantees the feasibility of solutions and produces high-quality solutions, particularly for larger activity instances, compared to other existing approaches.	en_US
dc.relation.ispartof	Computers and Industrial Engineering	en_US
dc.relation.isbasedon	10.1016/j.cie.2016.12.040	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Resource constrained project scheduling with uncertain activity durations	en_US
dc.type	Journal Article
utslib.citation.volume	112	en_US
utslib.for	010303 Optimisation	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	112	en_US

Abstract:

© 2017 Elsevier Ltd In this paper, we consider Resource Constrained Project Scheduling Problems (RCPSPs) with known deterministic renewable resource requirements but uncertain activity durations. In this case, the activity durations are represented by random variables with different probability distribution functions. To deal with this problem, we propose an approach based on the robust optimization concept, which produces reasonably good solutions under any likely input data scenario. Depending on different uncertainty characteristics, we have developed six different heuristics to incorporate the uncertain duration as a deterministic constraint in a robust optimization model. The resulting optimization model is then solved by using a Coin-Branch & Cut (CBC) solver. To judge the performance of the algorithm, we solved 30, 60, 90 and 120-activity benchmark problems from the project scheduling problem library (PSPLIB). Our proposed approach guarantees the feasibility of solutions and produces high-quality solutions, particularly for larger activity instances, compared to other existing approaches.

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http://hdl.handle.net/10453/124424