Adaptive genetic algorithms for multi-resource constrained project scheduling problem with multiple modes

In modern manufacturing systems like multi-resource constrained project scheduling problem with the multiple modes (mcPSP-mM) is complicated because of the complex interrelationships between the units of the different stages. In this paper, we develop an adaptive genetic algorithm (aGA) to solve the mcPSP-mM which is a well known NP-hard problem. A new aGA algorithm approach for solving these mcPSP-mM problems is 1) the design of priority-based encoding for activity priority and multistage-based encoding for activity mode, 2) order-based crossover operator for activity priority and local search-based mutation operator for activity mode, 3) iterative hill-climbing method in GA loop, 4) auto-tuning for the rates of crossover and mutation operators. The numerical experiments show that the proposed aGA is effective to the mcPSP-mM.