An adaptive SVM-based real-time scheduling mechanism and simulation for multiple-load carriers in automobile assembly lines

Abstract

Multiple-load carriers are widely introduced for material delivery in manufacturing systems. The real-time scheduling of multiple-load carriers is so complex that it deserves attention to pursue higher productivity and better system performance. In this paper, a support vector machine (SVM)-based real-time scheduling mechanism was proposed to tackle the scheduling problem of parts replenishment with multiple-load carriers in automobile assembly plants under dynamic environment. The SVM-based scheduling mechanism was trained first and then used to make the optimal real-time decisions between “wait” and “deliver” on the basis of real-time system states. An objective function considering throughput and delivery distances was established to evaluate the system performance. Moreover, a simulation model in eM-Plant software was developed to validate and compare the proposed SVM-based scheduling mechanism with the classic minimum batch size (MBS) heuristic. It simulated both the steady and dynamic environments which are characterized by the uncertainty of demands or scheduling criteria. The simulation results demonstrated that the SVM-based scheduling mechanism could dynamically make optimal real-time decisions for multiple-load carriers and outperform the MBS heuristic as well.