Parallel Genetic Algorithm for job shop heterogeneous multi-objectives scheduling problem

Consider a special group of job shop scheduling problems, where both customers and manufacturer have independent and different objectives. It is specified as a two-layer optimization model based on noncooperative game. Nash equilibrium (NE) schedule for heterogeneous customers is defined. A parallel genetic algorithm (PGA) based solving method is designed. Each customer is assigned a subpopulation and evolves synchronously to achieve a set of competitive equilibrium, i.e., NE schedule. The manufacturer chooses the best schedule according to its system objective to influence customerpsilas strategic behaviors. Tests indicate that the proposed algorithm can well coordinate the requirements of the customers and manufacturer.