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Project Cost and Duration Optimization Using Soft Computing Techniques

Gopal M. Naik and M. Kumar
Department of Civil Engineering University College of Engineering (A), Osmania University, Hyderabad-500007
Abstract—The Artificial Neural Networks is now used in many fields. They have become well established as viable, multipurpose, robust computational methodologies with solid theoretic support and with strong potential to be effective in any discipline, especially in construction. The input of the Artificial Neural Network (ANN) characterizes the different realizations of resources. The output is capable of characterizing the objectives and constraints of the optimization, such as attainment of regulatory goals, value of cost functions and time. The supervised learning algorithm of back propagation was used to train the network, once trained, the ANN begins a search through various realizations of pumping patterns to determine whether or not they will be successful. The simple genetic algorithm technique has also been used for optimization of project cost and time. A case study of a project under JNNURM program being executed by K P C Projects Ltd. has been presented in this study. Due to the recent severe global recession, company is facing severe problem and all the construction activities slowed down. To increase the productivity of all resources, it is necessary to forecast the costs arriving from resources so that the total cost of project can be reduced. The present case study deals with the construction of 512 Houses in (G+3) pattern, in 32 blocks located at Karmanghat, Hyderabad. It is observed from the results that the Neural Networks approach has optimized the total project cost by 3.91%, and the duration of the project has been reduced around 5% of the total duration of the project.

Index Terms—resource optimization, artificial neural network, project cost, project duration

Cite: Gopal M. Naik and M. Kumar, "Project Cost and Duration Optimization Using Soft Computing Techniques," Journal of Advanced Management Science, Vol. 1, No. 3, pp. 299-303, September 2013. doi: 10.12720/joams.1.3.299-303
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