is paper studies the performance of static and dynamic scheduling approaches in vehicle-based internal transport (VBIT) systems and is one of the first to systematically investigate under which circumstances, which scheduling method helps in improving performance. In practice, usually myopic dispatching heuristics are used, often using look-ahead information. We argue more advanced scheduling methods can help, depending on circumstances. We introduce three basic scheduling approaches (insertion, combined and column generation) for the static problem. We then extend these to a dynamic, real-time setting with rolling horizons. We propose two further real-time scheduling approaches: dynamic assignment with and without look-ahead. The performances of the above five scheduling approaches are compared with two of the best performing look-ahead dispatching rules known from the literature. The performance of the various approaches depends on the facility layout and work distribution. However, column generation, the combined heuristic, and the assignment approach with look-ahead consistently outperform dispatching rules. Column generation can require substantial calculation time but delivers very good performance if sufficient look-ahead information is available. For large scale systems, the combined heuristic and the dynamic assignment approach with look ahead are recommended and have acceptable calculation times.

Additional Metadata
Keywords dispatching, dynamic scheduling, material handling, vehicle-based internal transport
JEL Business Administration and Business Economics; Marketing; Accounting (jel M), Production Management (jel M11), Transportation Systems (jel R4), Transportation Systems: Other (jel R49)
Persistent URL dx.doi.org/10.1080/00207540903443279, hdl.handle.net/1765/20308
Series ERIM Top-Core Articles
Journal International Journal of Production Research
Citation
Le-Anh, T, de Koster, M.B.M, & Yu, Y. (2010). Performance evaluation of dynamic scheduling approaches in vehicle-based internal transport systems. International Journal of Production Research, 48(24), 7219–7242. doi:10.1080/00207540903443279