Cold chain distribution: How to deal with node and arc time windows?
Abstract Commonly encountered in cold chain logistics, third-party distribution firms are required to deliver temperature-sensitive food products to various retailers with two kinds of time-window constraints: (1) the delivery service must begin within the time windows imposed by the retailers (call...
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Gespeichert in:
| Autor*in: |
Zhang, Yi [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Annals of operations research - Springer US, 1984, 291(2018), 1-2 vom: 13. Okt., Seite 1127-1151 |
|---|---|
| Übergeordnetes Werk: |
volume:291 ; year:2018 ; number:1-2 ; day:13 ; month:10 ; pages:1127-1151 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10479-018-3071-0 |
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OLC2118471041 |
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| 520 | |a Abstract Commonly encountered in cold chain logistics, third-party distribution firms are required to deliver temperature-sensitive food products to various retailers with two kinds of time-window constraints: (1) the delivery service must begin within the time windows imposed by the retailers (called node time windows) and (2) each vehicle route is available only in a predefined time interval prescribed by the government (called arc time windows). We study the effects of the retailer time window type (i.e., density of the node time-window constraints) and other cost-related factors on a distribution firm’s legitimacy choice (i.e., the firm chooses to either comply with or violate the governmental time-window policy), food quality, and pollutant emissions in the urban environment. We model the problem as an intractable vehicle routing problem with node and arc time windows and develop a genetic algorithm to tackle it. We conduct a case study to generate the managerial insights on dealing with time windows. We find that the governmental time windows will increase the distribution cost. The governmental time windows has a negative effect on pollutant emissions while showing a positive effect on food safety. Given governmental time windows, a higher demand for node time windows will result in more governmental time-window violations or lower vehicle load factor, which depends on the vehicle fixed cost, fuel price, and government penalty. | ||
| 650 | 4 | |a Cold chain distribution | |
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| 650 | 4 | |a Node time windows | |
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| 700 | 1 | |a Cheng, T. C. E. |0 (orcid)0000-0001-5127-6419 |4 aut | |
| 700 | 1 | |a Zhang, Juliang |4 aut | |
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10.1007/s10479-018-3071-0 doi (DE-627)OLC2118471041 (DE-He213)s10479-018-3071-0-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Zhang, Yi verfasserin aut Cold chain distribution: How to deal with node and arc time windows? 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Commonly encountered in cold chain logistics, third-party distribution firms are required to deliver temperature-sensitive food products to various retailers with two kinds of time-window constraints: (1) the delivery service must begin within the time windows imposed by the retailers (called node time windows) and (2) each vehicle route is available only in a predefined time interval prescribed by the government (called arc time windows). We study the effects of the retailer time window type (i.e., density of the node time-window constraints) and other cost-related factors on a distribution firm’s legitimacy choice (i.e., the firm chooses to either comply with or violate the governmental time-window policy), food quality, and pollutant emissions in the urban environment. We model the problem as an intractable vehicle routing problem with node and arc time windows and develop a genetic algorithm to tackle it. We conduct a case study to generate the managerial insights on dealing with time windows. We find that the governmental time windows will increase the distribution cost. The governmental time windows has a negative effect on pollutant emissions while showing a positive effect on food safety. Given governmental time windows, a higher demand for node time windows will result in more governmental time-window violations or lower vehicle load factor, which depends on the vehicle fixed cost, fuel price, and government penalty. Cold chain distribution Urban freight transport Node time windows Arc time windows Vehicle routing Hua, Guowei aut Cheng, T. C. E. (orcid)0000-0001-5127-6419 aut Zhang, Juliang aut Enthalten in Annals of operations research Springer US, 1984 291(2018), 1-2 vom: 13. Okt., Seite 1127-1151 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:291 year:2018 number:1-2 day:13 month:10 pages:1127-1151 https://doi.org/10.1007/s10479-018-3071-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT AR 291 2018 1-2 13 10 1127-1151 |
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10.1007/s10479-018-3071-0 doi (DE-627)OLC2118471041 (DE-He213)s10479-018-3071-0-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Zhang, Yi verfasserin aut Cold chain distribution: How to deal with node and arc time windows? 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Commonly encountered in cold chain logistics, third-party distribution firms are required to deliver temperature-sensitive food products to various retailers with two kinds of time-window constraints: (1) the delivery service must begin within the time windows imposed by the retailers (called node time windows) and (2) each vehicle route is available only in a predefined time interval prescribed by the government (called arc time windows). We study the effects of the retailer time window type (i.e., density of the node time-window constraints) and other cost-related factors on a distribution firm’s legitimacy choice (i.e., the firm chooses to either comply with or violate the governmental time-window policy), food quality, and pollutant emissions in the urban environment. We model the problem as an intractable vehicle routing problem with node and arc time windows and develop a genetic algorithm to tackle it. We conduct a case study to generate the managerial insights on dealing with time windows. We find that the governmental time windows will increase the distribution cost. The governmental time windows has a negative effect on pollutant emissions while showing a positive effect on food safety. Given governmental time windows, a higher demand for node time windows will result in more governmental time-window violations or lower vehicle load factor, which depends on the vehicle fixed cost, fuel price, and government penalty. Cold chain distribution Urban freight transport Node time windows Arc time windows Vehicle routing Hua, Guowei aut Cheng, T. C. E. (orcid)0000-0001-5127-6419 aut Zhang, Juliang aut Enthalten in Annals of operations research Springer US, 1984 291(2018), 1-2 vom: 13. Okt., Seite 1127-1151 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:291 year:2018 number:1-2 day:13 month:10 pages:1127-1151 https://doi.org/10.1007/s10479-018-3071-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT AR 291 2018 1-2 13 10 1127-1151 |
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Cold chain distribution: How to deal with node and arc time windows? |
| abstract |
Abstract Commonly encountered in cold chain logistics, third-party distribution firms are required to deliver temperature-sensitive food products to various retailers with two kinds of time-window constraints: (1) the delivery service must begin within the time windows imposed by the retailers (called node time windows) and (2) each vehicle route is available only in a predefined time interval prescribed by the government (called arc time windows). We study the effects of the retailer time window type (i.e., density of the node time-window constraints) and other cost-related factors on a distribution firm’s legitimacy choice (i.e., the firm chooses to either comply with or violate the governmental time-window policy), food quality, and pollutant emissions in the urban environment. We model the problem as an intractable vehicle routing problem with node and arc time windows and develop a genetic algorithm to tackle it. We conduct a case study to generate the managerial insights on dealing with time windows. We find that the governmental time windows will increase the distribution cost. The governmental time windows has a negative effect on pollutant emissions while showing a positive effect on food safety. Given governmental time windows, a higher demand for node time windows will result in more governmental time-window violations or lower vehicle load factor, which depends on the vehicle fixed cost, fuel price, and government penalty. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstractGer |
Abstract Commonly encountered in cold chain logistics, third-party distribution firms are required to deliver temperature-sensitive food products to various retailers with two kinds of time-window constraints: (1) the delivery service must begin within the time windows imposed by the retailers (called node time windows) and (2) each vehicle route is available only in a predefined time interval prescribed by the government (called arc time windows). We study the effects of the retailer time window type (i.e., density of the node time-window constraints) and other cost-related factors on a distribution firm’s legitimacy choice (i.e., the firm chooses to either comply with or violate the governmental time-window policy), food quality, and pollutant emissions in the urban environment. We model the problem as an intractable vehicle routing problem with node and arc time windows and develop a genetic algorithm to tackle it. We conduct a case study to generate the managerial insights on dealing with time windows. We find that the governmental time windows will increase the distribution cost. The governmental time windows has a negative effect on pollutant emissions while showing a positive effect on food safety. Given governmental time windows, a higher demand for node time windows will result in more governmental time-window violations or lower vehicle load factor, which depends on the vehicle fixed cost, fuel price, and government penalty. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Commonly encountered in cold chain logistics, third-party distribution firms are required to deliver temperature-sensitive food products to various retailers with two kinds of time-window constraints: (1) the delivery service must begin within the time windows imposed by the retailers (called node time windows) and (2) each vehicle route is available only in a predefined time interval prescribed by the government (called arc time windows). We study the effects of the retailer time window type (i.e., density of the node time-window constraints) and other cost-related factors on a distribution firm’s legitimacy choice (i.e., the firm chooses to either comply with or violate the governmental time-window policy), food quality, and pollutant emissions in the urban environment. We model the problem as an intractable vehicle routing problem with node and arc time windows and develop a genetic algorithm to tackle it. We conduct a case study to generate the managerial insights on dealing with time windows. We find that the governmental time windows will increase the distribution cost. The governmental time windows has a negative effect on pollutant emissions while showing a positive effect on food safety. Given governmental time windows, a higher demand for node time windows will result in more governmental time-window violations or lower vehicle load factor, which depends on the vehicle fixed cost, fuel price, and government penalty. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| title_short |
Cold chain distribution: How to deal with node and arc time windows? |
| url |
https://doi.org/10.1007/s10479-018-3071-0 |
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| author2 |
Hua, Guowei Cheng, T. C. E. Zhang, Juliang |
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Hua, Guowei Cheng, T. C. E. Zhang, Juliang |
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| doi_str |
10.1007/s10479-018-3071-0 |
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2024-07-03T19:35:24.988Z |
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