An object-coding genetic algorithm for integrated process planning and scheduling

Process planning and jobshop scheduling problems are both crucial functions in manufacturing. In reality, dynamic disruptions such as machine breakdown or rush order will affect the feasibility and optimality of the sequentially-generated process plans and machining schedules. With the approach of integrated process planning and scheduling (IPPS), the actual process plan and the schedule are determined dynamically in accordance with the order details and the status of the manufacturing system. In this paper, an object-coding genetic algorithm (OCGA) is proposed to resolve the IPPS problems in a jobshop type of flexible manufacturing systems. An effective object-coding representation and its corresponding genetic operations are suggested, where real objects like machining operations are directly used to represent genes. Based on the object-coding representation, customized methods are proposed to fulfill the genetic operations. An unusual selection and a replacement strategy are integrated systematically for the population evolution, aiming to achieve near-optimal solutions through gradually improving the overall quality of the population, instead of exploring neighborhoods of good individuals. Experiments show that the proposed genetic algorithm can generate outstanding outcomes for complex IPPS instances.