Solution to the Time-Cost-Quality Trade-off Problem in Construction Projects Based on Immune Genetic Particle Swarm Optimization

Abstract

The importance of time-cost-quality trade-off in construction projects has been widely recognized by the construction industry. In this paper, we develop an integrated optimization model on the basis of improved time-cost and quality-time models. We improve the traditional cost-time model by taking reward and punishment into consideration. Further, we build a new quality model, called quality performance index (QPI), to describe system reliability. The avoidance of assigning the node weights and referring to expert experience adds to the practicality of the quality calculation. In the process of decision-making, we use contractual time, cost, and quality as benchmarks for evaluation of feasible solutions. Then, we combine an immune genetic algorithm with a constriction factor particle swarm optimization to get a new algorithm, called immune genetic particle swarm optimization (IGPSO). We test the effectiveness of IGPSO using two typical test functions and solve a practical example. Optimization results proved the practicability and validity of the model. We offer several Pareto solutions for a decision-maker to choose from in accordance with their expertise and project considerations.