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Augmented Time–Cost Trade-Off Optimization Using Particle Swarm Optimization
https://orcid.org/0000-0002-2484-4553
https://orcid.org/0000-0003-2431-033X
This study proposes an optimization model based on particle swarm optimization (PSO) to achieve the objectives of minimizing project duration, project cost, and resource fluctuation while maximizing project quality and schedule flexibility. The objectives of this study are as follows: (1) to determine whether the interrelationships between these five objectives affect the outcome of the optimization; (2) to determine whether the presence or absence of resource constraints affects the solution; and (3) to verify that the solution of the PSO-based optimization model coincides with the exact mathematical solution, but faster. An example project was presented to illustrate the applicability of the proposed model. The conclusions of this study are as follows: (1) multiple objectives do interact with each other and should be evaluated simultaneously rather than one at a time to reach realistic solutions, (2) resource constraints do affect the solutions obtained, and (3) the proposed model successfully reaches an optimal solution that is extremely close to the exact mathematical solution much faster than the mathematical model.
Augmented Time–Cost Trade-Off Optimization Using Particle Swarm Optimization
https://orcid.org/0000-0002-2484-4553
https://orcid.org/0000-0003-2431-033X
This study proposes an optimization model based on particle swarm optimization (PSO) to achieve the objectives of minimizing project duration, project cost, and resource fluctuation while maximizing project quality and schedule flexibility. The objectives of this study are as follows: (1) to determine whether the interrelationships between these five objectives affect the outcome of the optimization; (2) to determine whether the presence or absence of resource constraints affects the solution; and (3) to verify that the solution of the PSO-based optimization model coincides with the exact mathematical solution, but faster. An example project was presented to illustrate the applicability of the proposed model. The conclusions of this study are as follows: (1) multiple objectives do interact with each other and should be evaluated simultaneously rather than one at a time to reach realistic solutions, (2) resource constraints do affect the solutions obtained, and (3) the proposed model successfully reaches an optimal solution that is extremely close to the exact mathematical solution much faster than the mathematical model.
Augmented Time–Cost Trade-Off Optimization Using Particle Swarm Optimization
https://orcid.org/0000-0002-2484-4553
https://orcid.org/0000-0003-2431-033X
This study proposes an optimization model based on particle swarm optimization (PSO) to achieve the objectives of minimizing project duration, project cost, and resource fluctuation while maximizing project quality and schedule flexibility. The objectives of this study are as follows: (1) to determine whether the interrelationships between these five objectives affect the outcome of the optimization; (2) to determine whether the presence or absence of resource constraints affects the solution; and (3) to verify that the solution of the PSO-based optimization model coincides with the exact mathematical solution, but faster. An example project was presented to illustrate the applicability of the proposed model. The conclusions of this study are as follows: (1) multiple objectives do interact with each other and should be evaluated simultaneously rather than one at a time to reach realistic solutions, (2) resource constraints do affect the solutions obtained, and (3) the proposed model successfully reaches an optimal solution that is extremely close to the exact mathematical solution much faster than the mathematical model.
Augmented Time–Cost Trade-Off Optimization Using Particle Swarm Optimization
J. Constr. Eng. Manage.
Turkoglu, Harun (author) / Arditi, David (author) / Polat, Gul (author)
2024-05-01
Article (Journal)
Electronic Resource
English
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