Solving a stochastic time-cost-quality trade-off problem by meta-heuristic optimization algorithms

Actual time, cost, and quality of execution options for various activities within a considered project cannot be certainly determined prior to construction, there could be three different values of time and cost for each execution option, namely, optimistic value, most likely or normal value, and pessimistic value; and the quality could be described in linguistic terms. The objective of this research is to optimize time, cost, and quality of construction projects under uncertainty utilizing the program evaluation and review technique. In this study, multi-objective functions are used to decrease total project time and total project cost while maximizing overall project quality. For satisfying time-cost-quality trade-off optimization, a multi-objective optimization strategy is required. The non-dominating sorting-II concept and the crowding distance computation mechanism are combined with the teaching learning-based optimization algorithm to optimize time-cost-quality optimization problems. Non-dominating sorting-II teaching learning-based optimization algorithm is coded in MATLAB to optimize the trade-off between time, cost, and quality optimization problems. In the proposed model, the non-dominating sorting-II approach and crowding distance computation mechanism are responsible for handling objectives effectively and efficiently. Teaching learning-based optimization algorithm’s teacher and learner phases ensure that the searched solution space is explored and exploited. The proposed algorithm is applied to a 13-activity example problem, and the results show that it provides satisfactory results.