Abstract
Automated warehousing systems need to balance operational efficiency, energy consumption and overall system cost in sustainable supply chains. This paper presents an analytical model-based Decision Support System (DSS) for sustainable system design of Shuttle-Based Storage and Retrieval System (SBS/RS). Multiple system design indicators, namely mean service time, mean energy consumption, and overall system cost, are considered in a mathematical model. A simulation model is developed to validate the accuracy of the mathematical model. Extensive numerical experiments explore the impacts of rack design and equipment operating parameters on various system performance indicators and summarize balanced equipment operating settings. Overall, this study provides an analytical-model based DSS on sustainable SBS/RS configurations for decision-making managers and system designers.
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Notes
Full results and Python codes are available via https://doi.org/10.7910/DVN/JRXMEP.
Abbreviations
- \(a_{S}\) :
-
Shuttle acceleration
- \(a_{L}\) :
-
Lift acceleration
- \(v_{S}\) :
-
Maximum velocity of shuttles
- \(v_{L}\) :
-
Maximum velocity of the lift
- \(d_{H}\) :
-
Lane height (meter)
- \(d_{L}\) :
-
Lane length (meter)
- \(N\) :
-
Number of layers in the SBS/RS
- \(C\) :
-
Number of lanes in the SBS/RS
- \(g\) :
-
Gravitational acceleration
- \(K_{r}\) :
-
Energy recovery coefficient
- \(c_{e}\) :
-
Rotating mass transfer coefficient
- \(f_{r}\) :
-
Coefficient of friction
- \(\eta\) :
-
Mechanical efficiency
- \(m_{S}\) :
-
Sum of mass of a shuttle and loaded goods
- \(m_{L}\) :
-
Sum of mass of the lift and loaded goods
- \(F_{{S_{A} }}\) :
-
Traction force in shuttle acceleration
- \(P_{{S_{A} }}\) :
-
Engine power in shuttle acceleration
- \(F_{{S_{B} }}\) :
-
Braking force in shuttle deceleration
- \(P_{{S_{D} }}\) :
-
Engine power in shuttle deceleration
- \(F_{{S_{C} }}\) :
-
Traction force in shuttle movement with constant velocity
- \(F_{{S_{D} }}\) :
-
Traction force in shuttle deceleration
- \(F_{I}\) :
-
Inertia force in shuttle movement
- \(F_{M}\) :
-
Friction force in shuttle movement
- \(F_{{L_{A} }}\) :
-
Traction force in lift acceleration
- \(F_{{L_{D} }}\) :
-
Traction force in lift deceleration
- \(F_{{L_{C} }}\) :
-
Traction force in lift movement with constant velocity
- \(W_{S\_A}\) :
-
Expected energy consumed for a shuttle at acceleration stage
- \(W_{S\_D}\) :
-
Expected energy consumed for a shuttle at deceleration stage
- \(W_{S\_C}\) :
-
Expected energy consumed for a shuttle with constant speed
- \(E\left( {W_{S} } \right)\) :
-
Expected energy consumed for a shuttle
- \(E\left( {W_{L} } \right)\) :
-
Expected energy consumed for the lift
- \(E\left( {RW_{S} } \right)\) :
-
Expected energy recovery of a shuttle
- \(E\left( {RW_{L} } \right)\) :
-
Expected energy recovery of the lift
- \(E_{REAL}\) :
-
Average real energy consumption
- \(C_{L}\) :
-
Investment for a lift (RMB)
- \(C_{S}\) :
-
Investment for shuttles (RMB)
- \(C_{P}\) :
-
Investment for storage slot (RMB per slot)
- \(T_{P}\) :
-
Expected life (year)
- \(C_{R}\) :
-
Warehouse rent cost per square meter (RMB sq. m.)
- \(d_{S}\) :
-
Aisle (of shuttles) width (meter)
- \(d_{W}\) :
-
Width of a storage slot (meter)
- \(N_{total}\) :
-
Monthly total tasks
- \(C_{IE}\) :
-
Cost of industrial electricity per unit (RMB/kw·h)
- \(C_{TAX}\) :
-
Carbon emission tax per ton (RMB/ton)
- \(f_{e}\) :
-
Electricity-CO2 transform coefficient kg CO2/kw·h
References
Borovinšek M, Ekren BY, Burinskienė A, Lerher T (2016) Multi-objective optimisation model of shuttle-based storage and retrieval system. Transport 32(2):120–137. https://doi.org/10.3846/16484142.2016.1186732
Cai X, Heragu SS, Liu Y (2014) Modeling and evaluating the AVS/RS with tier-to-tier vehicles using a semi-open queueing network. IIE Trans 46(9):905–927. https://doi.org/10.1080/0740817X.2013.849832
Ekren BY (2020) A simulation-based experimental design for SBS/RS warehouse design by considering energy related performance metrics. Simul Model Pract Theory 98:101991. https://doi.org/10.1016/j.simpat.2019.101991
Ekren BY (2021) A multi-objective optimisation study for the design of an AVS/RS warehouse. Int J Prod Res 59(4):1107–1126. https://doi.org/10.1080/00207543.2020.1720927
Ekren BY, Akpunar A, Sari Z, Lerher T (2018) A tool for time, variance and energy related performance estimations in a shuttle-based storage and retrieval system. Appl Math Model 63:109–127. https://doi.org/10.1016/j.apm.2018.06.037
Ekren BY, Heragu SS, Krishnamurthy A, Malmborg CJ (2010) Simulation based experimental design to identify factors affecting performance of AVS/RS. Comput Ind Eng 58(1):175–185. https://doi.org/10.1016/j.cie.2009.10.004
Fukunari M (2005) Analytical foundations for autonomous vehicle storage and retrieval systems using load transfer station based dwell point strategies. Dissertation, Rensselaer Polytechnic Institute.
Guerrazzi E, Mininno V, Aloini D, Dulmin R, Scarpelli C, Sabatini M (2019) Energy evaluation of deep-lane autonomous vehicle storage and retrieval system. Sustain (basel, Switzerland) 11(14):3817–3831. https://doi.org/10.3390/su11143817
Heragu SS, Cai X, Krishnamurthy A, Malmborg CJ (2011) Analytical models for analysis of automated warehouse material handling systems. Int J Prod Res 49(22):6833–6861. https://doi.org/10.1080/00207543.2010.518994
Kuo P-H, Krishnamurthy A, Malmborg CJ (2007) Design models for unit load storage and retrieval systems using autonomous vehicle technology and resource conserving storage and dwell point policies. Appl Math Model 31(10):2332–2346. https://doi.org/10.1016/j.apm.2006.09.011
Kuo P-H, Krishnamurthy A, Malmborg CJ (2008) Performance modelling of autonomous vehicle storage and retrieval systems using class-based storage policies. Int J Comput Appl Technol 31(3/4):238–248. https://doi.org/10.1504/IJCAT.2008.018160
Küçükyaşar M, Ekren BY, Lerher T (2021) Cost and performance comparison for tier-captive and tier-to-tier SBS/RS warehouse configurations. Int Trans Oper Res 28(4):1847–1863. https://doi.org/10.1111/itor.12864
Lerher, T. (2013). Modern automation in warehousing by using the shuttle based technology. Automation systems of the 21st Century: New technologies, applications and impacts on the environment & industrial processes, pp. 51–86.
Lerher T, Yetkin Ekren B, & Akpunar A (2016). Simulation-based energy and cycle time analysis of shuttle-based storage and retrieval system. 14th IMHRC Proceedings (Karlsruhe, Germany). p. 15.
Liu Z, Wang Y, Jin M, Wu H, Dong W (2021) Energy consumption model for shuttle-based Storage and Retrieval Systems. J Clean Prod 282:11. https://doi.org/10.1016/j.jclepro.2020.124480
Malmborg CJ (2002) Conceptualizing tools for autonomous vehicle storage and retrieval systems. Int J Prod Res 40(8):1807–1822. https://doi.org/10.1080/00207540110118668
Marchet G, Melacini M, Perotti S, Tappia E (2012) Analytical model to estimate performances of autonomous vehicle storage and retrieval systems for product totes. Int J Prod Res 50(24):7134–7148. https://doi.org/10.1080/00207543.2011.639815
Roy D, Krishnamurthy A, Heragu SS, Malmborg CJ (2012) Performance analysis and design trade-offs in warehouses with autonomous vehicle technology. IIE Trans 44(12):1045–1060. https://doi.org/10.1080/0740817X.2012.665201
Tappia E, Roy D, De Koster R, Melacini MJTS (2017) Modeling, analysis, and design insights for shuttle-based compact storage systems. Transp Sci 51(1):269–295. https://doi.org/10.1287/trsc.2016.0699
Wang Y, Mou S, Wu Y (2015) Task scheduling for multi-tier shuttle warehousing systems. Int J Prod Res 53(19):5884–5895. https://doi.org/10.1080/00207543.2015.1012604
Wang W, Wu Y, Zheng J, Chi C (2020) A comprehensive framework for the design of modular robotic mobile fulfillment systems. IEEE Access 8:13259–13269. https://doi.org/10.1109/ACCESS.2020.2966403
Zhao X, Zhang R, Zhang N, Wang Y, Jin M, Mou S (2020) Analysis of the shuttle-based storage and retrieval system. IEEE Access 8:1–1. https://doi.org/10.1109/ACCESS.2020.3014102
Zou B, Xu X, Gong Y, De Koster R (2016) Modeling parallel movement of lifts and vehicles in tier-captive vehicle-based warehousing systems. Eur J Oper Res 254(1):51–67. https://doi.org/10.1016/j.ejor.2016.03.039
Acknowledgements
This work was supported by Natural Science Foundation of Shandong Province (Grant No., ZR2020MF085) and Shenzhen Science and Technology Research and Development Funds (Grant No., JCYJ20190807094803721). National Natural Science Foundation of China (Grant No. 72001224) also in part supports this work.
Funding
This work was supported by Natural Science Foundation of Shandong Province (Grant No., ZR2020MF085) and Shenzhen Science and Technology Research and Development Funds (Grant No., JCYJ20190807094803721). National Natural Science Foundation of China (Grant no. 72001224) also in part supports this work.
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YW contributed to the conception of the study. JQ contributed to the revision of the study and the summary of literature. SM contributed significantly to write the manuscript. KH performed the experiment and data analysis. XZ helped perform the analysis with constructive discussions. All authors read and approved the final manuscript.
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Full results and Python codes are available via https://doi.org/10.7910/DVN/JRXMEP.
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Wang, Y., Qin, J., Mou, S. et al. DSS approach for sustainable system design of shuttle-based storage and retrieval systems. Flex Serv Manuf J 35, 698–726 (2023). https://doi.org/10.1007/s10696-022-09447-w
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DOI: https://doi.org/10.1007/s10696-022-09447-w