Delay is one of the most common and serious problems in the construction industry. This problem may be avoided by anticipating the estimate schedule at completion (ESAC). After that, when the ESAC does not meet objective time completion, project managers used crashing technique to minimize additional cost in order to compress the total project duration. Construction managers estimate construction duration according to their previous experience based on budget planning, which can be inaccurate and costly. Several attempts have already situated on solving these kind of problems. For example, Earn value management (EVM) is a common approach used for predicting ESAC and other methods like some artificial intelligence techniques have already been applied in prediction. However, few works had been able to develop accurate prediction on ESAC which is primarily important to define the delay in the project. In light of this matter, this work presented in two different approaches addressing the problem respectively. This study consists of two parts: the first part is prediction part and the second part is optimization part. The first part uses neural network- long short-term memory (NN-LSTM) to predict ESAC. For the optimization part, the study develops Symbiotic Organisms Search (SOS) algorithm which is already proved to be a high-performance optimization method in the past to optimize project cost. An example network and a real project are provided to demonstrate the proposed algorithm. According to the results, the developed model successfully gains the optimum cost corresponding with the total crashing duration. It provides project managers with reliable schedule estimate and minimum cost that aims at project monitoring and timely decision making.

Delay is one of the most common and serious problems in the construction industry. This problem may be avoided by anticipating the estimate schedule at completion (ESAC). After that, when the ESAC does not meet objective time completion, project managers used crashing technique to minimize additional cost in order to compress the total project duration. Construction managers estimate construction duration according to their previous experience based on budget planning, which can be inaccurate and costly. Several attempts have already situated on solving these kind of problems. For example, Earn value management (EVM) is a common approach used for predicting ESAC and other methods like some artificial intelligence techniques have already been applied in prediction. However, few works had been able to develop accurate prediction on ESAC which is primarily important to define the delay in the project. In light of this matter, this work presented in two different approaches addressing the problem respectively. This study consists of two parts: the first part is prediction part and the second part is optimization part. The first part uses neural network- long short-term memory (NN-LSTM) to predict ESAC. For the optimization part, the study develops Symbiotic Organisms Search (SOS) algorithm which is already proved to be a high-performance optimization method in the past to optimize project cost. An example network and a real project are provided to demonstrate the proposed algorithm. According to the results, the developed model successfully gains the optimum cost corresponding with the total crashing duration. It provides project managers with reliable schedule estimate and minimum cost that aims at project monitoring and timely decision making.

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