An Immune Particle Swarm Optimization Approach To Engineering Project Time-quality-cost Trade-off

Along with the development of global construction market and project management technology, more attention is being paid on the efficiency of execution and the optimization of time-quality-cost as well as other objectives of engineering projects, Multi-objective Optimization technology of Engineering Project is becoming an important and practical research field.Previous research on multi-objective optimization of engineering project mainly focused on the following aspects: firstly analyze the qualitative and quantitative relations and constraints of one or two objectives, and then apply the traditional mathematic method to optimize the optimization model. Most of these research bases on some premises away from engineering practice and ignores the interdependent relationship between the targets ,say, never treats it as a whole. What's worse, the traditional mathematic optimization methods often lack precision, efficiency and universality.On these grounds, this dissertation firstly analyses the relations between project schedule vs. quality, schedule vs. cost, and cost vs. quality qualitatively and quantitatively, then optimizes the schedule in PERT networks, defines the quality performance index to qualify the objective of quality on the base of system reliability theory, raises a new approach to the cost breakdown structure, and makes the tradeoff among these interdependent and exclusive objectives by formulating a new fitness function to appraise each feasible alternative on the base of the accepted tender. Also, an improved immune particle swarm optimization algorism is proposed to solve this problem. At the end of this dissertation the computer simulation is introduced into a practical engineering case which will show that the proposed tradeoff model is efficacious and the selected algorithm is efficient to converge and excels in the parallel computation and global optimization. The result of this research not only enriches the academic knowledge of engineering multi-objective optimization but also provides an effective objective-oriented technology of project management.