Abstract
Automated vehicle storage and retrieval systems (AVSRS) allow for a flexible and powerful way to supply picking or manufacturing areas based on the goods-to-person principle. AVSRS with tier- and aisle-captive vehicles achieve the highest throughput capacity which can be further increased by deploying more than one vehicle on each tier of an aisle. With these high-powered AVSRS, it is possible to compensate for the reduction of throughput capacity in multi-deep storage systems where relocation operations occur frequently and result in more transportation tasks. In this work, we present and compare different algorithms for efficiently accomplishing relocation operations in high-powered AVSRS. We conduct a series of simulation experiments to analyze the performance of these algorithms and evaluate the application of high-powered AVSRS in multi-deep storage.
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References
VDI 2692 Part 1 (2015). Automated vehicle storage and retrieval systems for small unit loads, Beuth.
FEM 9.860 (2017). Cycle time calculation for automated vehicle storage and retrieval systems. European Materials Handling Federation.
Lienert, T., & Fottner, J. (2018). Routing-based sequencing applied to shuttle systems. In 21st International Conference 2018 (pp. 2949–2954).
Carlo, H. J., & Vis, I. F. A. (2012). Sequencing dynamic storage systems with multiple lifts and shuttles. International Journal of Production Economics, 140, 844–853.
Zhao, N., Luo, L., & Lodewijks, G. (2018). Scheduling two lifts on a common rail considering acceleration and deceleration in a shuttle based storage and retrieval system. Computers & Industrial Engineering, 124, 48–57.
Kress, D., Dornseifer, J., & Jaehn, F. (2019). An exact solution approach for scheduling cooperative gantry cranes. European Journal of Operational Research, 273(1), 82–101.
Peterson, B., Harjunkoski, I., Hoda, S., & Hooker, J. N. (2014). Scheduling multiple factory cranes on a common track. Computers & Operations Research, 48, 102–112.
Takahashi, S., Kita, H., Suzuki, H., Sudo, T., & Markon, S. (2003). Simulation-based optimization of a controller for multi-car elevators using a genetic algorithm for noisy fitness function. In The 2003 Congress on Evolutionary Computation, CEC 2003, Canberra, Australia (pp. 1582–1587).
Erdoğan, G., Battarra, M., & Laporte, G. (2014). Scheduling twin robots on a line. Naval Research Logistics, 61(2), 119–130.
Atz T. Eine algorithmenbasierte Methode zur ganzheitlichen Systemplanung automatischer Hochregallager. Dissertation, Technische Universität München. https://doi.org/10.2195/lj_proc_doerr_de_201610_01.
Dörr, K., & Furmans, K. Durchsatzbetrachtungen für doppeltiefe Lager unter dem Einsatz von zwei Lastaufnahmemitteln, 3-8316-0581-53-8316-0581-5.
Seemüller, S. (2006). Durchsatzberechnung automatischer Kleinteilelager im Umfeld des elektronischen Handels. München: Utz.
Lerher, T. (2016). Travel time model for double-deep shuttle-based storage and retrieval systems. International Journal of Production Research, 54(9), 2519–2540.
Tappia, E., Roy, D., de Koster, M.B.M., & Melacini, M. Modeling, analysis, and design insights for shuttle-based compact storage systems. Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam ERS-2015-010-LIS. https://ideas.repec.org/p/ems/eureri/78379.html.
Kemme, N. (2011). RMG crane scheduling and stacking. In J. W. Böse (Ed.), Operations Research/Computer Science Interfaces Series, Handbook of Terminal Planning (pp. 271–301). New York: Springer.
Maccarthy, B. L., & Liu, J. (1993). Addressing the gap in scheduling research: A review of optimization and heuristic methods in production scheduling. International Journal of Production Research, 31(1), 59–79.
Habl, A., Lienert, T., Pradines, G., & Fottner, J. (2019). Vehicle coordination and configuration in high-powered automated vehicle storage and retrieval systems. 18. ASIM-Fachtagung Simulation in Produktion und Logistik, Chemnitz (Submitted).
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Habl, A., Plapp, V., Fottner, J. (2020). Operating High-Powered Automated Vehicle Storage and Retrieval Systems in Multi-deep Storage. In: Zhang, J., Dresner, M., Zhang, R., Hua, G., Shang, X. (eds) LISS2019. Springer, Singapore. https://doi.org/10.1007/978-981-15-5682-1_52
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DOI: https://doi.org/10.1007/978-981-15-5682-1_52
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