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The impact of batch retrievals on throughput performance of a carousel system serviced by a storage and retrieval machine

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  • Pazour, Jennifer A.
  • Meller, Russell D.

Abstract

A carousel system is an example of an automated storage and retrieval system that is used in distribution centers and manufacturing facilities. We analyze the impact batch retrieval processing has on throughput performance for horizontal carousel systems that use automated storage and retrieval machines as robotic pickers. By developing an analytical cycle-time model we provide the theoretical base necessary to support the management decision of whether to batch or not. We conduct a computational experiment to test the accuracy of our analytical model, to demonstrate how our approach can guide management decisions, and to illustrate the throughput improvements that can be realized through the use of a batch policy. Testing indicates that batch processing increases carousel throughput performance over sequential processing. For the instances tested, batch processing resulted in an average decrease in cycle time over sequential sequencing of 20%. Our results indicate that with batch retrievals, adding to the carousel's length increases storage capacity with a negligible impact on the carousel's throughput performance.

Suggested Citation

  • Pazour, Jennifer A. & Meller, Russell D., 2013. "The impact of batch retrievals on throughput performance of a carousel system serviced by a storage and retrieval machine," International Journal of Production Economics, Elsevier, vol. 142(2), pages 332-342.
    • Handle: RePEc:eee:proeco:v:142:y:2013:i:2:p:332-342
    DOI: 10.1016/j.ijpe.2012.12.005
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    References listed on IDEAS

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    1. Hwang, Hark & Ha, Jae-Won, 1991. "Cycle time models for single/double carousel system," International Journal of Production Economics, Elsevier, vol. 25(1-3), pages 129-140.
    2. Killmann Frank & von Collani Elart, 2001. "A Note on the Convolution of the Uniform and Related Distributions and Their Use in Quality Control," Stochastics and Quality Control, De Gruyter, vol. 16(1), pages 17-41, January.
    3. Wen, Ue-Pyng & Chang, Ding-Tsair, 1988. "Picking rules for a carousel conveyor in an automated warehouse," Omega, Elsevier, vol. 16(2), pages 145-151.
    4. Vickson, R. G. & Lu, Xinjian, 1998. "Optimal product and server locations in one-dimensional storage racks," European Journal of Operational Research, Elsevier, vol. 105(1), pages 18-28, February.
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    Cited by:

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    2. Azadeh, K. & de Koster, M.B.M. & Roy, D., 2017. "Robotized Warehouse Systems: Developments and Research Opportunities," ERIM Report Series Research in Management ERS-2017-009-LIS, 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.
    3. Aqlan, Faisal & Lam, Sarah S. & Ramakrishnan, Sreekanth, 2014. "An integrated simulation–optimization study for consolidating production lines in a configure-to-order production environment," International Journal of Production Economics, Elsevier, vol. 148(C), pages 51-61.
    4. Dukic, Goran & Opetuk, Tihomir & Lerher, Tone, 2015. "A throughput model for a dual-tray Vertical Lift Module with a human order-picker," International Journal of Production Economics, Elsevier, vol. 170(PC), pages 874-881.
    5. Kaveh Azadeh & René De Koster & Debjit Roy, 2019. "Robotized and Automated Warehouse Systems: Review and Recent Developments," Transportation Science, INFORMS, vol. 53(4), pages 917-945, July.

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