TY - JOUR
T1 - Finance-based scheduling multi-objective optimization
T2 - Benchmarking of evolutionary algorithms
AU - El-Abbasy, Mohammed S.
AU - Elazouni, Ashraf
AU - Zayed, Tarek
N1 - Funding Information:
The authors gratefully acknowledge the support provided by Sultan Qaboos University for this research project under award No. RF/ENG/CAED/19/01 .
Publisher Copyright:
© 2020 Elsevier B.V.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/12
Y1 - 2020/12
N2 - Project scheduling and financing should be adequately integrated during the planning phase to avoid probable cost overruns and delays. Many studies addressed the achievement of integration between project financing and scheduling using multi-objective optimization in particular. However, up to the knowledge of the authors, there is no research conducted to evaluate and assess the performance of the multi-objective optimization techniques employed in this domain. Thus, the current study developed a finance-based scheduling multi-objective optimization model for multiple projects using the elitist non-dominated sorting genetic algorithm (NSGA-II). Further, the obtained results were compared with the results obtained by solving the same problem in another study from the literature using the multi-objective optimization technique of strength Pareto evolutionary algorithm (SPEA). Benchmarking was conducted based on the quality of the obtained solutions and performance. The results indicated that the NSGA-II outperformed SPEA in most aspects with achieved improvements range from 1.7% to 98.2%.
AB - Project scheduling and financing should be adequately integrated during the planning phase to avoid probable cost overruns and delays. Many studies addressed the achievement of integration between project financing and scheduling using multi-objective optimization in particular. However, up to the knowledge of the authors, there is no research conducted to evaluate and assess the performance of the multi-objective optimization techniques employed in this domain. Thus, the current study developed a finance-based scheduling multi-objective optimization model for multiple projects using the elitist non-dominated sorting genetic algorithm (NSGA-II). Further, the obtained results were compared with the results obtained by solving the same problem in another study from the literature using the multi-objective optimization technique of strength Pareto evolutionary algorithm (SPEA). Benchmarking was conducted based on the quality of the obtained solutions and performance. The results indicated that the NSGA-II outperformed SPEA in most aspects with achieved improvements range from 1.7% to 98.2%.
KW - Evolutionary algorithms
KW - Finance-based scheduling
KW - Multi-objective optimization
KW - Multiple projects
UR - http://www.scopus.com/inward/record.url?scp=85090862980&partnerID=8YFLogxK
U2 - 10.1016/j.autcon.2020.103392
DO - 10.1016/j.autcon.2020.103392
M3 - Journal article
AN - SCOPUS:85090862980
SN - 0926-5805
VL - 120
JO - Automation in Construction
JF - Automation in Construction
M1 - 103392
ER -