Advancing multi-objective optimization in resource management using the NSGA-III algorithm

This paper presents a method for applying a more comprehensive approach to the resource-constrained project scheduling problem, considering task start dates and allowing for project time extensions to better ensure cost and resource leveling factors. This study employs an improved version of the non-dominated sorting genetic algorithm III (NSGA-III), developed from the genetic algorithm (GA), to address the multi-objective problem. The research results in a set of Pareto-optimal solutions superior to those obtained in previous studies. Through the integration of NSGA-III, this research contributes to advancing the field of multi-objective optimization in resource-constrained project scheduling, offering promising avenues for future research and practical applications.