The DOI matches its Crossref record.
A Min-Max Solution of an Inventory Problem
Abstract
The paper is concerned with the problem of purchasing a quantity of an item in anticipation of a future demand. It is assumed that only the mean and standard deviation of the demand distribution are known. A stock level is found which maximizes the minimum profit for all demand distributions with this mean and standard deviation. This stock level is then compared with the levels obtained by assuming several specific distributions. (Author)
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Citations
Cited by
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Khouja, M. (1999) · Omega · doi:10.1016/s0305-0483(99)00017-1 · Cited by 908
Distributionally Robust Optimization and Its Tractable Approximations
Goh, J., & Sim, M. (2010) · Operations Research · doi:10.1287/opre.1090.0795 · Cited by 668
Distributionally Robust Stochastic Optimization with Wasserstein Distance
Gao, R., & Kleywegt, A. (2023) · Mathematics of Operations Research · doi:10.1287/moor.2022.1275 · Cited by 630
Infrastructure planning for electric vehicles with battery swapping
Mak, H.-Y., Rong, Y., & Shen, Z.-J. M. (2013) · Management Science · doi:10.1287/mnsc.1120.1672 · Cited by 570
Metadata
- Authors
- Scarf, H.
- Venue
- Herbert Scarf’s Contributions to Economics, Game Theory and Operations Research
- Publisher
- Palgrave Macmillan UK
- Published
- 2005
- Pages
- 19-27
- OpenAlex
- W2558813012
- Cited by
- 552
- References
- 0
- FWCI
- 73.79
- Type
- book chapter
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A Min-Max Solution of an Inventory Problem. https://app.cochat.ai/papers/10.1057/9781137024381_3
- The paper
- Scarf, H. (2005). A Min-Max Solution of an Inventory Problem. Herbert Scarf’s Contributions to Economics, Game Theory and Operations Research. https://doi.org/10.1057/9781137024381_3
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
