Robust pricing for airlines with partial information
Abstract In the spot market for air cargo, airlines typically adopt dynamic pricing to tackle demand uncertainty, for which it is difficult to accurately estimate the distribution. This study addresses the problem where a dominant airline dynamically sets prices to sell its capacities within a two-p...
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| Autor*in: |
Feng, Bo [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Annals of operations research - Springer US, 1984, 310(2021), 1 vom: 26. Feb., Seite 49-87 |
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volume:310 ; year:2021 ; number:1 ; day:26 ; month:02 ; pages:49-87 |
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| DOI / URN: |
10.1007/s10479-020-03926-9 |
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| 520 | |a Abstract In the spot market for air cargo, airlines typically adopt dynamic pricing to tackle demand uncertainty, for which it is difficult to accurately estimate the distribution. This study addresses the problem where a dominant airline dynamically sets prices to sell its capacities within a two-phase sales period with only partial information. That partial information may show as the moments (upper and lower bounds and mean) and the median of the demand distribution. We model the problem of dynamic pricing as a distributional robust stochastic programming, which minimizes the expected regret value under the worst-case distribution in the presence of partial information. We further reformulate the proposed non-convex model to show that the closed-form formulae of the second-stage maximal expected regret are well-structured. We also design an efficient algorithm to characterize robust pricing strategies in a polynomial-sized running time. Using numerical analysis, we present several useful managerial insights for airline managers to strategically collect demand information and make prices for their capacities in different market situations. Moreover, we verify that additional information will not compromise the viability of the pricing strategies being implemented. Therefore, the method we present in this paper is easier for airlines to use. | ||
| 650 | 4 | |a Robust pricing | |
| 650 | 4 | |a Distributional robust stochastic programming | |
| 650 | 4 | |a Air cargo | |
| 650 | 4 | |a Partial information | |
| 700 | 1 | |a Zhao, Jixin |4 aut | |
| 700 | 1 | |a Jiang, Zheyu |4 aut | |
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10.1007/s10479-020-03926-9 doi (DE-627)OLC2078138452 (DE-He213)s10479-020-03926-9-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Feng, Bo verfasserin (orcid)0000-0002-2455-5751 aut Robust pricing for airlines with partial information 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract In the spot market for air cargo, airlines typically adopt dynamic pricing to tackle demand uncertainty, for which it is difficult to accurately estimate the distribution. This study addresses the problem where a dominant airline dynamically sets prices to sell its capacities within a two-phase sales period with only partial information. That partial information may show as the moments (upper and lower bounds and mean) and the median of the demand distribution. We model the problem of dynamic pricing as a distributional robust stochastic programming, which minimizes the expected regret value under the worst-case distribution in the presence of partial information. We further reformulate the proposed non-convex model to show that the closed-form formulae of the second-stage maximal expected regret are well-structured. We also design an efficient algorithm to characterize robust pricing strategies in a polynomial-sized running time. Using numerical analysis, we present several useful managerial insights for airline managers to strategically collect demand information and make prices for their capacities in different market situations. Moreover, we verify that additional information will not compromise the viability of the pricing strategies being implemented. Therefore, the method we present in this paper is easier for airlines to use. Robust pricing Distributional robust stochastic programming Air cargo Partial information Zhao, Jixin aut Jiang, Zheyu aut Enthalten in Annals of operations research Springer US, 1984 310(2021), 1 vom: 26. Feb., Seite 49-87 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:310 year:2021 number:1 day:26 month:02 pages:49-87 https://doi.org/10.1007/s10479-020-03926-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT AR 310 2021 1 26 02 49-87 |
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10.1007/s10479-020-03926-9 doi (DE-627)OLC2078138452 (DE-He213)s10479-020-03926-9-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Feng, Bo verfasserin (orcid)0000-0002-2455-5751 aut Robust pricing for airlines with partial information 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract In the spot market for air cargo, airlines typically adopt dynamic pricing to tackle demand uncertainty, for which it is difficult to accurately estimate the distribution. This study addresses the problem where a dominant airline dynamically sets prices to sell its capacities within a two-phase sales period with only partial information. That partial information may show as the moments (upper and lower bounds and mean) and the median of the demand distribution. We model the problem of dynamic pricing as a distributional robust stochastic programming, which minimizes the expected regret value under the worst-case distribution in the presence of partial information. We further reformulate the proposed non-convex model to show that the closed-form formulae of the second-stage maximal expected regret are well-structured. We also design an efficient algorithm to characterize robust pricing strategies in a polynomial-sized running time. Using numerical analysis, we present several useful managerial insights for airline managers to strategically collect demand information and make prices for their capacities in different market situations. Moreover, we verify that additional information will not compromise the viability of the pricing strategies being implemented. Therefore, the method we present in this paper is easier for airlines to use. Robust pricing Distributional robust stochastic programming Air cargo Partial information Zhao, Jixin aut Jiang, Zheyu aut Enthalten in Annals of operations research Springer US, 1984 310(2021), 1 vom: 26. Feb., Seite 49-87 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:310 year:2021 number:1 day:26 month:02 pages:49-87 https://doi.org/10.1007/s10479-020-03926-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT AR 310 2021 1 26 02 49-87 |
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10.1007/s10479-020-03926-9 doi (DE-627)OLC2078138452 (DE-He213)s10479-020-03926-9-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Feng, Bo verfasserin (orcid)0000-0002-2455-5751 aut Robust pricing for airlines with partial information 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract In the spot market for air cargo, airlines typically adopt dynamic pricing to tackle demand uncertainty, for which it is difficult to accurately estimate the distribution. This study addresses the problem where a dominant airline dynamically sets prices to sell its capacities within a two-phase sales period with only partial information. That partial information may show as the moments (upper and lower bounds and mean) and the median of the demand distribution. We model the problem of dynamic pricing as a distributional robust stochastic programming, which minimizes the expected regret value under the worst-case distribution in the presence of partial information. We further reformulate the proposed non-convex model to show that the closed-form formulae of the second-stage maximal expected regret are well-structured. We also design an efficient algorithm to characterize robust pricing strategies in a polynomial-sized running time. Using numerical analysis, we present several useful managerial insights for airline managers to strategically collect demand information and make prices for their capacities in different market situations. Moreover, we verify that additional information will not compromise the viability of the pricing strategies being implemented. Therefore, the method we present in this paper is easier for airlines to use. Robust pricing Distributional robust stochastic programming Air cargo Partial information Zhao, Jixin aut Jiang, Zheyu aut Enthalten in Annals of operations research Springer US, 1984 310(2021), 1 vom: 26. Feb., Seite 49-87 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:310 year:2021 number:1 day:26 month:02 pages:49-87 https://doi.org/10.1007/s10479-020-03926-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT AR 310 2021 1 26 02 49-87 |
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10.1007/s10479-020-03926-9 doi (DE-627)OLC2078138452 (DE-He213)s10479-020-03926-9-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Feng, Bo verfasserin (orcid)0000-0002-2455-5751 aut Robust pricing for airlines with partial information 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract In the spot market for air cargo, airlines typically adopt dynamic pricing to tackle demand uncertainty, for which it is difficult to accurately estimate the distribution. This study addresses the problem where a dominant airline dynamically sets prices to sell its capacities within a two-phase sales period with only partial information. That partial information may show as the moments (upper and lower bounds and mean) and the median of the demand distribution. We model the problem of dynamic pricing as a distributional robust stochastic programming, which minimizes the expected regret value under the worst-case distribution in the presence of partial information. We further reformulate the proposed non-convex model to show that the closed-form formulae of the second-stage maximal expected regret are well-structured. We also design an efficient algorithm to characterize robust pricing strategies in a polynomial-sized running time. Using numerical analysis, we present several useful managerial insights for airline managers to strategically collect demand information and make prices for their capacities in different market situations. Moreover, we verify that additional information will not compromise the viability of the pricing strategies being implemented. Therefore, the method we present in this paper is easier for airlines to use. Robust pricing Distributional robust stochastic programming Air cargo Partial information Zhao, Jixin aut Jiang, Zheyu aut Enthalten in Annals of operations research Springer US, 1984 310(2021), 1 vom: 26. Feb., Seite 49-87 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:310 year:2021 number:1 day:26 month:02 pages:49-87 https://doi.org/10.1007/s10479-020-03926-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT AR 310 2021 1 26 02 49-87 |
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10.1007/s10479-020-03926-9 doi (DE-627)OLC2078138452 (DE-He213)s10479-020-03926-9-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Feng, Bo verfasserin (orcid)0000-0002-2455-5751 aut Robust pricing for airlines with partial information 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract In the spot market for air cargo, airlines typically adopt dynamic pricing to tackle demand uncertainty, for which it is difficult to accurately estimate the distribution. This study addresses the problem where a dominant airline dynamically sets prices to sell its capacities within a two-phase sales period with only partial information. That partial information may show as the moments (upper and lower bounds and mean) and the median of the demand distribution. We model the problem of dynamic pricing as a distributional robust stochastic programming, which minimizes the expected regret value under the worst-case distribution in the presence of partial information. We further reformulate the proposed non-convex model to show that the closed-form formulae of the second-stage maximal expected regret are well-structured. We also design an efficient algorithm to characterize robust pricing strategies in a polynomial-sized running time. Using numerical analysis, we present several useful managerial insights for airline managers to strategically collect demand information and make prices for their capacities in different market situations. Moreover, we verify that additional information will not compromise the viability of the pricing strategies being implemented. Therefore, the method we present in this paper is easier for airlines to use. Robust pricing Distributional robust stochastic programming Air cargo Partial information Zhao, Jixin aut Jiang, Zheyu aut Enthalten in Annals of operations research Springer US, 1984 310(2021), 1 vom: 26. Feb., Seite 49-87 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:310 year:2021 number:1 day:26 month:02 pages:49-87 https://doi.org/10.1007/s10479-020-03926-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT AR 310 2021 1 26 02 49-87 |
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Abstract In the spot market for air cargo, airlines typically adopt dynamic pricing to tackle demand uncertainty, for which it is difficult to accurately estimate the distribution. This study addresses the problem where a dominant airline dynamically sets prices to sell its capacities within a two-phase sales period with only partial information. That partial information may show as the moments (upper and lower bounds and mean) and the median of the demand distribution. We model the problem of dynamic pricing as a distributional robust stochastic programming, which minimizes the expected regret value under the worst-case distribution in the presence of partial information. We further reformulate the proposed non-convex model to show that the closed-form formulae of the second-stage maximal expected regret are well-structured. We also design an efficient algorithm to characterize robust pricing strategies in a polynomial-sized running time. Using numerical analysis, we present several useful managerial insights for airline managers to strategically collect demand information and make prices for their capacities in different market situations. Moreover, we verify that additional information will not compromise the viability of the pricing strategies being implemented. Therefore, the method we present in this paper is easier for airlines to use. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
| abstractGer |
Abstract In the spot market for air cargo, airlines typically adopt dynamic pricing to tackle demand uncertainty, for which it is difficult to accurately estimate the distribution. This study addresses the problem where a dominant airline dynamically sets prices to sell its capacities within a two-phase sales period with only partial information. That partial information may show as the moments (upper and lower bounds and mean) and the median of the demand distribution. We model the problem of dynamic pricing as a distributional robust stochastic programming, which minimizes the expected regret value under the worst-case distribution in the presence of partial information. We further reformulate the proposed non-convex model to show that the closed-form formulae of the second-stage maximal expected regret are well-structured. We also design an efficient algorithm to characterize robust pricing strategies in a polynomial-sized running time. Using numerical analysis, we present several useful managerial insights for airline managers to strategically collect demand information and make prices for their capacities in different market situations. Moreover, we verify that additional information will not compromise the viability of the pricing strategies being implemented. Therefore, the method we present in this paper is easier for airlines to use. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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Abstract In the spot market for air cargo, airlines typically adopt dynamic pricing to tackle demand uncertainty, for which it is difficult to accurately estimate the distribution. This study addresses the problem where a dominant airline dynamically sets prices to sell its capacities within a two-phase sales period with only partial information. That partial information may show as the moments (upper and lower bounds and mean) and the median of the demand distribution. We model the problem of dynamic pricing as a distributional robust stochastic programming, which minimizes the expected regret value under the worst-case distribution in the presence of partial information. We further reformulate the proposed non-convex model to show that the closed-form formulae of the second-stage maximal expected regret are well-structured. We also design an efficient algorithm to characterize robust pricing strategies in a polynomial-sized running time. Using numerical analysis, we present several useful managerial insights for airline managers to strategically collect demand information and make prices for their capacities in different market situations. Moreover, we verify that additional information will not compromise the viability of the pricing strategies being implemented. Therefore, the method we present in this paper is easier for airlines to use. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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Robust pricing for airlines with partial information |
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Zhao, Jixin Jiang, Zheyu |
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