Job Shop Scheduling Based On Petri Net Modeling Intelligent Algorithm

Job shop system is one of the most important issues in the industrial production. With the development of the industrial technology, the process of job shop system has become more complex. In order to reduce the cost of production, improve the quality of product, and shorten the cycle of the production, some optimization rules are used to solve these problems. Job shop scheduling problem has been proven to be NP-hard, and we need some better modeling tools.Petri net model has been used to describe the process of manufacturing systems. It contains strict mathematical logic, and typically used for analysis the discrete systems. Petri net model's advantages include dealing with conflict, mutex, deadlock and so on. According to the characteristic information of job shop problem, the Petri net can satisfy all the constraint conditions about the system, and we can build reasonable Petri net model, when task requirements or system resources change, the Petri net can be modifying at the same time.With the development of artificial intelligence research, in order to resolve the job shop problems, quite a lot of approaches have been proposed, such as Tabu search, simulated annealing, and genetic algorithm (GA). All of these are heuristic approaches, and they have received much attention over the years. However, heuristic dispatch rules sometimes rely on empirical experience, when the job shop system becomes more complicated, a pure optimization method may be impractical in many cases, so we need some improved methods.In this paper, we explore the use of genetic algorithm (GA) and artificial immune algorithm (AI) to schedule job shops, which include multiple machine types, generic precedence constraints. Firstly, we presents modeling and scheduling approaches for job shops system (JSP) using Petri net (PN). Secondly, the solution forms the basis of genetic algorithm (GA) and artificial immune (AI) algorithm that generates a feasible schedule. Thirdly, numerical examples are taken from some representative industrial job shops. According to these experiments, proves that our algorithm is effective and correct.