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A Bayesian decision model with hurricane forecast updates for emergency supplies inventory management.

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Title: A Bayesian decision model with hurricane forecast updates for emergency supplies inventory management.
Authors: Taskin, S.1 STaskin@aselsan.com.tr, Lodree, E. J.2 eldree@auburn.edu
Source: Journal of the Operational Research Society. Jun2011, Vol. 62 Issue 6, p1098-1108. 11p.
Subjects: Hurricanes, Bayesian analysis, Prediction models, Emergency management, Cost effectiveness, Inventory control, National Hurricane Center
Abstract: Hurricane forecasts are intended to convey information that is useful in helping individuals and organizations make decisions. For example, decisions include whether a mandatory evacuation should be issued, where emergency evacuation shelters should be located, and what are the appropriate quantities of emergency supplies that should be stockpiled at various locations. This paper incorporates one of the National Hurricane Center's official prediction models into a Bayesian decision framework to address complex decisions made in response to an observed tropical cyclone. The Bayesian decision process accounts for the trade-off between improving forecast accuracy and deteriorating cost efficiency (with respect to implementing a decision) as the storm evolves, which is characteristic of the above-mentioned decisions. The specific application addressed in this paper is a single-supplier, multi-retailer supply chain system in which demand at each retailer location is a random variable that is affected by the trajectory of an observed hurricane. The solution methodology is illustrated through numerical examples, and the benefit of the proposed approach compared to a traditional approach is discussed. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the Operational Research Society is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: A Bayesian decision model with hurricane forecast updates for emergency supplies inventory management.
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  Data: <searchLink fieldCode="AR" term="%22Taskin%2C+S%2E%22">Taskin, S.</searchLink><relatesTo>1</relatesTo><i> STaskin@aselsan.com.tr</i><br /><searchLink fieldCode="AR" term="%22Lodree%2C+E%2E+J%2E%22">Lodree, E. J.</searchLink><relatesTo>2</relatesTo><i> eldree@auburn.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+Operational+Research+Society%22">Journal of the Operational Research Society</searchLink>. Jun2011, Vol. 62 Issue 6, p1098-1108. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Hurricanes%22">Hurricanes</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Emergency+management%22">Emergency management</searchLink><br /><searchLink fieldCode="DE" term="%22Cost+effectiveness%22">Cost effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Inventory+control%22">Inventory control</searchLink><br /><searchLink fieldCode="DE" term="%22National+Hurricane+Center%22">National Hurricane Center</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Hurricane forecasts are intended to convey information that is useful in helping individuals and organizations make decisions. For example, decisions include whether a mandatory evacuation should be issued, where emergency evacuation shelters should be located, and what are the appropriate quantities of emergency supplies that should be stockpiled at various locations. This paper incorporates one of the National Hurricane Center's official prediction models into a Bayesian decision framework to address complex decisions made in response to an observed tropical cyclone. The Bayesian decision process accounts for the trade-off between improving forecast accuracy and deteriorating cost efficiency (with respect to implementing a decision) as the storm evolves, which is characteristic of the above-mentioned decisions. The specific application addressed in this paper is a single-supplier, multi-retailer supply chain system in which demand at each retailer location is a random variable that is affected by the trajectory of an observed hurricane. The solution methodology is illustrated through numerical examples, and the benefit of the proposed approach compared to a traditional approach is discussed. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of the Operational Research Society is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1057/jors.2010.14
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 1098
    Subjects:
      – SubjectFull: Hurricanes
        Type: general
      – SubjectFull: Bayesian analysis
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Emergency management
        Type: general
      – SubjectFull: Cost effectiveness
        Type: general
      – SubjectFull: Inventory control
        Type: general
      – SubjectFull: National Hurricane Center
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              Text: Jun2011
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              Y: 2011
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