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.