A job shop scheduling approach based on simulation optimization

With regards to the problem of the traditional scheduling approach can't establish the precise scheduling models and obtain the satisfied scheduling results at the same time, a new scheduling approach based on simulation optimization methodology is presented. The approach comprises two modules: genetic algorithm (GA) based optimizer and discrete event simulation model. Candidate scheduling schemes represented by a serial of scheduling rules are suggest by GA that automatically guides the system towards better solutions. Simulation models are used to evaluate the performance of candidate scheduling schemes, the results of evaluation are returned to the GA to be utilized in selection of the next generation of candidate scheduling schemes to be evaluated. This process continues until a satisfactory solution is obtained. In addition, In order to build simulation model rapidly for the similar production conditions, a simulation modeling approach based on modular control models including shop level controller model, cell level controller model and equipment level controller model is present. The approach encompasses control logic, which are separated from the basic modeling elements in the simulation model, of different levels in production system. Finally, a case study is presented to illustrate the application of the proposed approach.