Development of Multi-objective Optimization model integrating Construction Time, Cost, Quality and Risk using Ant Colony Algorithm

Abstract

It is a well-known fact that in a complex construction project that involves resources and operations, estimating the duration and cost of each activity is a challenge. In addition to time and cost, other parameters like quality and risk also have to be optimized to ensure the success of a project. Lack of quality and risk measures may affect productivity and the quality of workmanship. Therefore, it is important to engage quality and risk parameters in the planning stage, along with time and cost. The main aim of this paper is to integrate quality and risk into a scheduling model and to develop a multi-objective scheduling model to solve the time-cost trade-off problems. To achieve this aim, the data of fifteen large-scale residential, commercial, and industrial projects concerning time, cost, quality, and risk were collected. Then, to optimize this data, a project model was developed using the Python-based ACO algorithm. Then, by using the ACO algorithm, optimal time and cost solutions were found. The model has been validated by comparing the proposed model results with the actual time and cost of the project. A correlation analysis was done to study the interrelationship among the time, cost, quality, and risk components. The solutions were also tested with sensitivity analysis, and Pareto-optimal solutions were compared with previous solutions. Finally, it is inferred that the estimated time and costs obtained using the ACO algorithm were approximately equal to the actual time and costs obtained by taking quality and risk constraints into account. Hence, in the planning stage itself, the time and cost can be predicted using the proposed model for various scenarios of quality and risk.