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Optimal Storage Assignment in Automatic Warehousing Systems

Hausman, Warren H., Schwarz, Leroy B., Graves, Stephen C.·1976·Management Science

Abstract

In the past few years, increasing numbers of automatic warehousing systems using computer-controlled stacker cranes have been installed. Our research concerns the scientific scheduling and design of these systems. There are three elements to scheduling: the assignment of multiple items to the same pallet (Pallet Assignment); the assignment of pallet loads to storage locations (Storage Assignment); and rules for sequencing storage and retrieve requests (Interleaving). This paper deals with optimal storage assignment. Results are obtained which compare the operating performance of three storage assignment rules: random assignment, which is similar to the closest-open-location rule used by many currently operating systems; full turnover-based assignment: and class-based turnover assignment. It is shown that significant reductions in crane travel time (and distance) are obtainable from turnover-based rules. These improvements can, under certain circumstances, be directly translated into increased throughput capacity for existing systems, and may be used to alter the design (e.g., size and number of racks, speed of cranes, etc.) of proposed systems in order to achieve a more desirable system balance between throughput and storage capacity.

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Metadata

Authors
Hausman, W. H., Schwarz, L. B., & Graves, S. C.
Venue
Management Science
Published
1976-02
Volume
22
Issue
6
Pages
629-638
ISSN
  • 0025-1909 (ISSN-L)
  • 1526-5501
OpenAlex
W2101601405
Cited by
628
References
0
FWCI
22.00
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Optimal Storage Assignment in Automatic Warehousing Systems. https://app.cochat.ai/papers/10.1287/mnsc.22.6.629
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Hausman, W. H., Schwarz, L. B., & Graves, S. C. (1976). Optimal Storage Assignment in Automatic Warehousing Systems. Management Science. https://doi.org/10.1287/mnsc.22.6.629

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.