Evolution program for resource constrained project scheduling problem

The paper describes an implementation of an evolution program for a resource constrained project scheduling problem, which is much more complex than other scheduling problems. The traditional order-based crossover operators are not well suited for this problem without modification. The approach adopted is based on the augmentation of the evolution program with domain-specific knowledge. It undertakes the burden of devising appropriate genetic operators for this problem to guarantee a feasible schedule. A new discipline is addressed for designing the genetic operators. In the implementation, crossover is designed to perform blind search to explore the area beyond local optima, and mutation is designed to perform intensive search to produce an improved solution. The proposed approach has been tested on two standard test problems and the results show that it can find the known optimum very rapidly and is superior to existing heuristic techniques. The suggested approach can significantly improve the performance of evolution program both in terms of speed and accuracy and can be applied to other difficult combinatorial optimization problems