This paper presents an effective evolutionary hybrid for solving the permutation flowshop scheduling problem. Based on a memetic algorithm, the procedure uses a construction component that generates initial solutions through the use of a novel reblocking mechanism operating according to a biased random sampling technique. This component is aimed at forcing the operations having smaller processing times to appear on the critical path. The goal of the construction component is to fill an initial pool with high-quality solutions for a memetic algorithm that looks for even higher-quality solutions. In the memetic algorithm, whenever a crossover operator and possibly a mutation are performed, the offspring genome is fine-tuned by a combination of 2-exchange swap and insertion local searches. The same with the employed construction method; in these local searches, the critical path notion has been used to exploit the structure of the problem. The results of computational experiments on the benchmark instances indicate that these components have strong synergy, and their integration has created a robust and effective procedure that outperforms several state-of-the-art procedures on a number of the benchmark instances. By deactivating different components enhancing the evolutionary module of the procedure, the effects of these components have also been examined.

An effective hybrid evolutionary search method is presented which integrates a genetic algorithm with a local search. Whereas its genetic algorithm improves the solutions obtained by its local search, its local search component utilizes a synergy between two neighborhood schemes in diversifying the pool used by the genetic algorithm. Through the integration of these two searches, the crossover operators further enhance the solutions that are initially local optimal for both neighborhood schemes; and the employed local search provides fresh solutions for the pool whenever needed. The joint endeavor of its local search mechanism and its genetic algorithm component has made the method both robust and effective. The local search component examines unvisited regions of search space and consequently diversifies the search; and the genetic algorithm component recombines essential pieces of information existing in several high-quality solutions and intensifies the search. It is through striking such a balance between diversification and intensification that the method exploits the structure of search space and produces superb solutions. The method has been implemented as a procedure for the resource-constrained project scheduling problem. The computational experiments on 2,040 benchmark instances indicate that the procedure is very effective.