TY - CHAP
T1 - Multi-objective Workforce Allocation in Construction Projects
AU - Iskandar, Andrew
AU - Allmendinger, Richard
N1 - Funding Information:
The authors would like ASGC for supporting this study and providing real-world construction data available for the analysis.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Managing construction projects is a complex, resource-intense and risky task that involves the organization and management of people skilled in the design and completion of construction projects. Embarking on a construction project means to plan the allocation of resources and labour, while ensuring that the output (e.g. a new building) meets a certain quality, and is delivered in time and within budget without breaching contractual obligations. We formulate a simplified version of this task as a constrained multi-objective optimization problem, and then use a non-dominated sorting genetic algorithm to tackle the problem. In addition to providing a formal definition of the problem, further contributions of this work include the validation of the methodology using real data of construction projects varying in scale and resource-utilisation; the use of real data is scarce in the construction project management area. We also perform a scenario-based analysis to understand how the approach reacts to changing environmental parameters (such as availability of resources). Finally, we discuss practical implications. Our empirical analysis highlights that the proposed approach improves significantly in terms of project budget, quality, and duration targets, when compared with the industry standard.
AB - Managing construction projects is a complex, resource-intense and risky task that involves the organization and management of people skilled in the design and completion of construction projects. Embarking on a construction project means to plan the allocation of resources and labour, while ensuring that the output (e.g. a new building) meets a certain quality, and is delivered in time and within budget without breaching contractual obligations. We formulate a simplified version of this task as a constrained multi-objective optimization problem, and then use a non-dominated sorting genetic algorithm to tackle the problem. In addition to providing a formal definition of the problem, further contributions of this work include the validation of the methodology using real data of construction projects varying in scale and resource-utilisation; the use of real data is scarce in the construction project management area. We also perform a scenario-based analysis to understand how the approach reacts to changing environmental parameters (such as availability of resources). Finally, we discuss practical implications. Our empirical analysis highlights that the proposed approach improves significantly in terms of project budget, quality, and duration targets, when compared with the industry standard.
KW - Construction project management
KW - Evolutionary algorithms
KW - Multi-objective optimization
KW - Resource allocation
KW - Workforce allocation
UR - http://www.scopus.com/inward/record.url?scp=85107512859&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/c3ad352b-ad7d-3512-ac2e-5c779df97093/
U2 - 10.1007/978-3-030-72699-7_4
DO - 10.1007/978-3-030-72699-7_4
M3 - Chapter
AN - SCOPUS:85107512859
SN - 9783030726980
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 50
EP - 64
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Castillo, Pedro A.
A2 - Jiménez Laredo, Juan Luis
PB - Springer Nature
T2 - 24th International Conference on the Applications of Evolutionary Computation, EvoApplications 2021 held as Part of EvoStar 2021
Y2 - 7 April 2021 through 9 April 2021
ER -