Optimization of construction program for economic and environmental sustainability

Abstract

The construction industry plays a significant role in promoting economic development; however, it causes several environmental problems. Balancing economic and environmental sustainability (EES) is a key challenge for contractors. Obtaining a sustainable construction program necessitates a significant amount of time, manpower, and expertise in practice. This study develops a method to optimize the construction program with the goal of EES, which deploys the non-dominated sorting genetic algorithm III to rapidly solve this multi-objective optimization problem through Pareto front approximation. Variables and search spaces are defined, and two databases are established for global search. A fast and automated tool is developed based on the proposed optimization method, featuring a convenient workflow and user-friendly graphic user interfaces to lower the entry barrier. The effectiveness, applicability, and robustness of the developed method were validated through four diverse case studies, demonstrating its capacity to achieve carbon reductions of up to 49.02 % and cost savings of 34.54 %. This research helps contractors make informed decisions about the design of optimal construction programs and the sustainability challenges in construction. In addition, it contributes to the intelligent development of the construction industry.