| Summary: In the real world, there are plenty of problems that require finding the best solution meeting many objectives. Multiobjective optimization models are needed to obtain this solution. For this purpose, in this study, to perform such a multiobjective optimization process, an efficient Teaching Learning-Based Optimization (TLBO) algorithm has been employed. Its performance is tested on several construction projects varying from an 18-activity to 630-activity. The applied model integrates the modified adaptive weight as well as non-dominated sorting approaches to find out the Pareto front solution. Furthermore, a slight modification is made in the non-dominating sorting version of the classical sole-TLBO algorithm by adding a certain portion of pre-known solutions to the initial population of model in order to achieve an enhancement in the exploration capacity of the proposed algorithm. Thus, the Pareto front performance of the utilized model is validated re-solving the benchmark optimization problems taken from the literature. Hence, the multiobjective optimization model based on TLBO developed in this study can be preferred another alternative tool to solve time-cost trade-off problem in construction engineering and management. Thereby, the main contribution of this study can be clearly seen from the application of TLBO for the first time to solve TCTP problems in the construction management field. |
