Operational scheduling for rough mills using a virtual manufacturing environment

Production scheduling in wood product plants, such as furniture factories, is highly complex due to its sensitivity to defects occurring in wood. Due to the huge number of choices and uncertainty of the outcome an optimum schedule cannot be computed mathematically. These choices, with complex interdependencies, have to be made on an hourly basis at different machines and must be coordinated to achieve the overall production,objectives. A decision support tool, using a 3D simulation model, is provided to predict the production performance of the null under various operational schedules, before a selected schedule is implemented. A 3D simulation model was developed accurately depicting the layout and operation of a rough mill. A historical database, some of it maintained via human input and some of it collected automatically, was analyzed to provide the basis for predicting the behavior of the mill when processing different type of raw materials and when producing different combination of products. The validated model, which links to the scheduling database (i.e., raw material available, products required) is used to test-fly alternate production schedules as often as required, thus identifying potential production problems before they occur in the mill. The next step will be to incorporate intelligent scheduling agents using techniques, such as fuzzy logic and genetic algorithms, to provide scheduling recommendations on individual machines. In the future these will be coordinated in a holonic manufacturing execution system to achieve better global performance. Performance measurement will require assessment based upon multiple criteria (e.g., productivity, recovery, grade utilization, and satisfaction of order files)