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Optimizing multi-mode time-cost-quality trade-off of construction project using opposition multiple objective difference evolution
The time, cost and quality are crucial, conflicting aspects to construction project management. Tradeoff optimization among project duration (time), project cost, and the project quality within the project scope is necessary to enhance overall construction project benefit. A novel optimization algorithm, Opposition-based Multiple Objective Differential Evolution (OMODE), is presented to solve the time-cost-quality tradeoff (TCQT) problem. This novel algorithm employs an opposition-based learning technique for population initialization and for generation jumping. Opposition numbers are used to improve the exploration and convergence performance of the optimization process. A numerical high-way construction project case study is analyzed to illustrate the use of the algorithm and demonstrate its capabilities in generating non-dominated solutions. Comparisons with non-dominated sorting genetic algorithm (NSGA-II), multiple objective particle swarm optimization (MOPSO), multiple objective differential evolution (MODE) and previous results verify the efficiency and effectiveness of the proposed algorithm. This study is expected to provide an alternative solving methodology for the TCQT problem and help project manager plan construction methods with optimal time-cost-quality tradeoff.
Optimizing multi-mode time-cost-quality trade-off of construction project using opposition multiple objective difference evolution
The time, cost and quality are crucial, conflicting aspects to construction project management. Tradeoff optimization among project duration (time), project cost, and the project quality within the project scope is necessary to enhance overall construction project benefit. A novel optimization algorithm, Opposition-based Multiple Objective Differential Evolution (OMODE), is presented to solve the time-cost-quality tradeoff (TCQT) problem. This novel algorithm employs an opposition-based learning technique for population initialization and for generation jumping. Opposition numbers are used to improve the exploration and convergence performance of the optimization process. A numerical high-way construction project case study is analyzed to illustrate the use of the algorithm and demonstrate its capabilities in generating non-dominated solutions. Comparisons with non-dominated sorting genetic algorithm (NSGA-II), multiple objective particle swarm optimization (MOPSO), multiple objective differential evolution (MODE) and previous results verify the efficiency and effectiveness of the proposed algorithm. This study is expected to provide an alternative solving methodology for the TCQT problem and help project manager plan construction methods with optimal time-cost-quality tradeoff.
Optimizing multi-mode time-cost-quality trade-off of construction project using opposition multiple objective difference evolution
Luong, Duc-Long (author) / Tran, Duc-Hoc (author) / Nguyen, Phong Thanh (author)
International Journal of Construction Management ; 21 ; 271-283
2021-03-04
13 pages
Article (Journal)
Electronic Resource
English
| British Library Online Contents | 2015