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Title: Resource levelling using genetic algorithms for a power plant boiler construction project

Authors: Christos Kyriklidis; Georgios Dounias

Addresses: Management and Decision Engineering Laboratory (MDE-Lab), Department of Financial and Management Engineering, University of the Aegean, 41 Kountouriotou Str., 82100 Chios, Greece ' Management and Decision Engineering Laboratory (MDE-Lab), Department of Financial and Management Engineering, University of the Aegean, 41 Kountouriotou Str., 82100 Chios, Greece

Abstract: This paper deals with the use of intelligent techniques for resource levelling optimisation. An intelligent approach previously introduced but enriched and improved, is applied in a real world project corresponding to the construction of a power plant boiler. Resource levelling optimisation is related to the optimal handling of available resources of a project. Conventional optimisation approaches like exhaustive or greedy search are unable to provide high quality solutions in a short amount of time. Thus, intelligent approaches properly modified, are used, which are capable of quickly reaching highly accurate near-optimal solutions. In this work, a genetic algorithm is applied in the resource levelling data of the boiler construction project, using a new approach that introduces a special time interval for the activities calculation of the early and late start. Accordingly, resource profiles are further evaluated with the use of a special multiple evaluation function approach called sequential function evaluation (SFE).

Keywords: resource levelling; project management; genetic algorithms; power plant boilers; boiler construction; construction projects; construction industry; optimisation; sequential function evaluation; SFE.

DOI: 10.1504/IJDSS.2016.081755

International Journal of Decision Support Systems, 2016 Vol.2 No.1/2/3, pp.151 - 167

Available online: 23 Jan 2017 *

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