Probabilistic multiobjective optimization of sustainable engineering design

As the global population reaches 7 billion and standards of living are increasing, engineers are being pressured to use limited natural resources to satisfy ever-increasing demand. Project engineers are currently charged with achieving a balance between cost and duration, and must consider environmental factors to reach sustainable development. This work proposes a novel probabilistic multi-objective optimization algorithm to attain sustainable construction cost, project duration, and CO2 emissions simultaneously in an uncertain project environment. The novel algorithm, which is based on Particle Swarm Optimization integrated with Monte Carlo simulation, is applied to generate a low-carbon economy and cleaner production. A typical construction project is selected to demonstrate the application of proposed algorithm for making sustainable decisions under multi-objectives. The proposed method demonstrated its risk analysis capacity by obtaining non-dominant optimized solutions for cleaner construction. This paper contributes to facilitating project managers in achieving a design that satisfies technical and quality requirements with lowest cost, shortest duration, and minimal adverse impacts on the environment.