Integrated Process Planning and Scheduling in Flexible Job Shops
Process planning and scheduling are two important functions of real-life manufacturing systems. The former function defines the set of processes to be used for manufacturing, i.e., the set of operations needed to manufacture the products as well as the technological precedence constraints between the operations. The latter function defines the temporal assignment of the selected operations to the production resources. Traditionally, process planning is carried out before scheduling and the selected process plans become fixed, or a rather small amount of alternative process plans is provided. However, such an approach can lead to low-quality or even infeasible solutions in dynamic production environments. Hence, integrating process planning and scheduling is of a high practical interest. In this work, we present an iterative decomposition approach for integrated process planning and scheduling. Sophisticated heuristics are proposed on the process planning and the scheduling levels. We benchmark the proposed solution approach on a number of problem instances found in the related literature and generated by ourselves. Furthermore, a mixed-integer programming model for the integrated problem is developed. We assess the performance of the heuristics based on the results provided by IBM CPLEX for small-scale problem instances. Next, we assess the performance of the proposed approach in a dynamic manufacturing environment modeled using simulation software. Understanding challenges of future manufacturing systems in view of Industry 4.0, we describe on a high level how the developed solution approaches can be incorporated into a multi-agent system by presenting different software agents and the rules of inter-agent communication.
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