Time Cost Optimization Using Genetic Algorithm of a Construction Project

Construction projects often go through delays due to various reasons, which create a dreadful financial influence on the project. For minimizing this scenario, cost and time optimization of a construction project is effectively used. Cost and time optimization method is the most effective and time efficient method with highest achievable performance under specific condition in a construction project. This method is mainly required for cost and time optimization in a construction project. This thesis work also highlights the various innovative techniques that are required for cost and time optimization of the project. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods are considered the advanced innovative techniques which are being used continuously by the construction companies for cost and time optimization. The advance work of Genetic Algorithm(GA) method in the form of GA with Dev-C ++ 4.9.9.2, GA with Line of Balance(LOB), GA with Modified Adaptive Weight Approach (MAWA), GA with Critical Path Method (CPM) along with new methods Linear Programming (LP), Non-Linear Integer Programming Model (NLIP), Discounted Cash Flow Method (DCF), Maximum Flow-Minimal Cut Theory and Artificial Neural Networks Method (ANN) are also included in the various innovative techniques of cost and time optimization process. Furthermore, the method of Genetic Algorithm (GA) which is specified in the thesis work is classified into two parameters where the global parallel GA method provides more effectiveness and efficiency than coarse-grained parallel GA method. Also, it is found through researchers and investigators that the Non-Linear Integer Programming (NLIP) method and Line of Balance (LOB) with GA method both have an efficient and optimum solution for time cost trade-off problem, along with PSO method which is best for Pareto-compromise solution and Direct Cash Flow (DCF) method which optimizes cost and time within the project boundaries. Finally, it is observed GA along with its advanced parameters, ANN method and NLP techniques are better for solving time cost trade-off problems.