Optimization of time-cost-quality trade-off problems using meta-heuristic algorithms in construction projects

Today, an intense competition has arisen due to the fact that there are many construction companies in the business world and the investments of the construction sector are generally large projects. Construction projects are increasingly complex and more difficult to implement. Therefore, managers must be able to plan the project down to the smallest detail. Due to the complexity of implementations and limited resources, it is to keep time and cost at the planned levels in projects, which are the main goals of construction companies. It is remarkable that the demands of the project stakeholders and the quality of project implementation have also been included in the contracts in recent years. One of the main concerns of project managers is to complete the project in the shortest time, with high quality and the lowest cost to achieve high profits. For this reason, balancing time, cost and quality plays a very important role both for the successful completion of the project and for competition. In construction projects, a complex relationship between time, cost and quality and an integrated NP-hard problem occurs. It is very difficult to choose among the factors in question. In recent years, algorithms have been developed to solve complex and optimization problems or to reach the closest solution to the solution. In order to reach the most possible and optimum solutions of optimization problems, meta-heuristic algorithms derived from social life or nature, which have similarities with natural systems, attract more attention. Within the scope of this thesis, one of the new and powerful meta-heuristic algorithms, the cuckoo search algorithm, has been used to obtain the closest solution to the time-cost-quality trade-off problem in construction projects. In the thesis, it was aimed to obtain the best or the closest results in the shortest time with the cuckoo search algorithm. The results show that the thesis objectives have been accomplished. The solutions obtained in the results of the thesis were compared with other algorithm solutions.