A modified genetic algorithm for job shop scheduling

As a class of typical production scheduling problems, job shop scheduling is one of the strongly NP-complete combinatorial optimisation problems, for which an enhanced genetic algorithm is proposed in this paper. An effective crossover operation for operation-based representation is used to guarantee the feasibility of the solutions, which are decoded into active schedules during the search process. The classical mutation operator is replaced by the metropolis sample process of simulated annealing with a probabilistic jumping property, to enhance the neighbourhood search and to avoid premature convergence with controllable deteriorating probability, as well as avoiding the difficulty of choosing the mutation rate. Multiple state generators are applied in a hybrid way to enhance the exploring potential and to enrich the diversity of neighbourhoods. Simulation results demonstrate the effectiveness of the proposed algorithm, whose optimisation performance is markedly superior to that of a simple genetic algorithm and simulated annealing and is comparable to the best result reported in the literature.