Solving resource-constrained project scheduling problem by a genetic local search approach

The resource-constrained project scheduling problem is a general scheduling problem which involving activities need to be scheduled such that the makespan is minimized. However, the RCPSP is confirmed to be an NP-hard combinatorial problem. Restated, it is hard to be solved in a reasonable computational time. Therefore, numerous metaheuristics-based approaches have been developed for finding near-optimal solution for RCPSP. Genetic algorithms have been applied to a wide variety of combinatorial optimization problems and have proved their efficiency. However, prematurely convergence may lead to search stagnation on restricted regions of the search space. To deal with this drawback and beside the good performances attained by local search procedures, a genetic local search algorithm for solving the RCPSP is proposed. Simulation results demonstrate that the proposed GLSA provides an effective and efficient approach for solving RCPSP.