Quality identifies the overall level of performance of the desired building facility or civil infrastructure. Quality can include safety and sustainability requirements, and planning the desired quality level is paramount in construction projects. Nevertheless, two other significant project management Key Performance Indicators (KPIs) must be considered in construction project management: time and cost. Project Managers always perform a trade-off between these three KPIs, but it is known that the relationship between these three indicators can be difficult to understand. Therefore, a multi-objective Genetic Algorithm (GA) has been proposed to develop a comprehensive approach to optimize project performance in construction. The proposed multi-objective GA can be used as a decision support system for the detailed design stage of a construction project to detect better and alternative detailed design and construction solutions. A GA is an Artificial Intelligence application (AI) that develops an evolutionary learning optimization process that discards worse solutions and re-introduces better solutions with an iterative process. Therefore, the most suitable solution can be found by performing a trade-off between the three indicators. The research aims to demonstrate the availability of AI applications to understand and perform the Time–Cost–Quality trade-off for construction projects. The developed procedure has been tested on a simple pilot study of a building renovation project, and the best-found optimized results have been detected with Solver® and discussed. Future research work will be aimed at improving the procedure’s efficiency as to be implemented in larger projects.

A genetic algorithm-based approach for the time, cost, and quality trade-off problem for construction projects / Bragadin, Marco Alvise; Kähkönen, Kalle; Pozzi, Luca. - In: TEMA. - ISSN 2421-4574. - ELETTRONICO. - 9:2(2023), pp. 121-134. [10.30682/tema090012]

A genetic algorithm-based approach for the time, cost, and quality trade-off problem for construction projects

Bragadin, Marco Alvise
;
2023

Abstract

Quality identifies the overall level of performance of the desired building facility or civil infrastructure. Quality can include safety and sustainability requirements, and planning the desired quality level is paramount in construction projects. Nevertheless, two other significant project management Key Performance Indicators (KPIs) must be considered in construction project management: time and cost. Project Managers always perform a trade-off between these three KPIs, but it is known that the relationship between these three indicators can be difficult to understand. Therefore, a multi-objective Genetic Algorithm (GA) has been proposed to develop a comprehensive approach to optimize project performance in construction. The proposed multi-objective GA can be used as a decision support system for the detailed design stage of a construction project to detect better and alternative detailed design and construction solutions. A GA is an Artificial Intelligence application (AI) that develops an evolutionary learning optimization process that discards worse solutions and re-introduces better solutions with an iterative process. Therefore, the most suitable solution can be found by performing a trade-off between the three indicators. The research aims to demonstrate the availability of AI applications to understand and perform the Time–Cost–Quality trade-off for construction projects. The developed procedure has been tested on a simple pilot study of a building renovation project, and the best-found optimized results have been detected with Solver® and discussed. Future research work will be aimed at improving the procedure’s efficiency as to be implemented in larger projects.
2023
A genetic algorithm-based approach for the time, cost, and quality trade-off problem for construction projects / Bragadin, Marco Alvise; Kähkönen, Kalle; Pozzi, Luca. - In: TEMA. - ISSN 2421-4574. - ELETTRONICO. - 9:2(2023), pp. 121-134. [10.30682/tema090012]
Bragadin, Marco Alvise; Kähkönen, Kalle; Pozzi, Luca
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/955392
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