A project scheduling problem investigates a set of activities that have to be scheduled
due to precedence priority and resource constraints in order to optimize project-related
objective functions. This paper focuses on the multi-mode project scheduling problem concerning
resource constraints (MRCPSP). Resource allocation and leveling, renewable and
non-renewable resources, and time-cost trade-off are some essential characteristics which are
considered in the proposed multi-objective scheduling problem. In this paper, a novel hybrid
algorithm is proposed based on non-dominated sorting ant colony optimization and genetic
algorithm (NSACO-GA). It uses the genetic algorithm as a local search strategy in order to
improve the efficiency of the ant colony algorithm. The test problems are generated based on
the project scheduling problem library (PSPLIB) to compare the efficiency of the proposed
algorithm with the non-dominated sorting genetic algorithm (NSGA-II). The numerical result
verifies the efficiency of the proposed hybrid algorithm in comparison to the NSGA-II
algorithm.