Optimizing time, cost, environmental impact, and client satisfaction in sustainable construction projects using LHS-NSGA-III: a multi-objective approach

This paper introduces a comprehensive multi-objective optimization model for sustainable construction projects, targeting the minimization of project duration, construction cost, environmental impact, and the maximization of client satisfaction. The proposed approach combines Latin Hypercube Sampling (LHS) with the Non-dominated Sorting Genetic Algorithm III (NSGA-III) to form the LHS-NSGA-III framework. This model addresses the growing need for sustainable construction by balancing key trade-offs between time, cost, environmental impact, and client satisfaction. The optimization process begins with LHS, ensuring a diverse and well-distributed initial population, followed by genetic operations such as crossover and mutation to maintain diversity across generations. The model evaluates multiple execution modes for project activities, each with associated resource utilizations that affect time, cost, environmental impact, and satisfaction outcomes. A case study of a one-story building project with 21 activities and five execution modes per activity demonstrates the applicability of the model. The results showcase a set of Pareto-optimal solutions, providing decision-makers with balanced trade-offs across objectives. This LHS-NSGA-III model offers an effective approach for optimizing sustainable construction projects, helping managers achieve efficiency, cost-effectiveness, and higher client satisfaction while minimizing environmental impact.