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An Optimization Model for Selecting Sample Days

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  • Tao Li

    (The Institute for Transportation Research and Education, North Carolina State University, Raleigh, North Carolina 27606, United States)

Abstract

Sample day selection method plays an important role in managerial decisions which require analyses that are prohibitively expensive to apply to a large number of days. We develop a general sample day selection model that selects sample days based on the cumulative distributions of airspace conditions and characteristics (C&C) by considering factors such as sampling targets, degree of diversity and coverage of the selected days. We introduce indicators that capture the airspace C&C of the North Atlantic region (NAT) and apply the model to select sample days for the NAT. The results show that the model outperforms the methods used by the U.S. Federal Aviation Administration.

Suggested Citation

  • Tao Li, 2021. "An Optimization Model for Selecting Sample Days," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 38(04), pages 1-22, August.
    • Handle: RePEc:wsi:apjorx:v:38:y:2021:i:04:n:s0217595920500529
    DOI: 10.1142/S0217595920500529
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    Cited by:

    1. Li, Tao & Wan, Yan, 2021. "A fuel savings and benefit analysis of reducing separation standards in the oceanic airspace managed by the New York Air Route Traffic Control Center," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).

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