Optimal lane expansion model for a battery electric vehicle transportation network considering range anxiety and demand uncertainty
This paper investigates a novel design problem involving an optimal government lane expansion scheme for a battery electric vehicle (BEV) transportation network. A lane expansion model is established considering the BEV charging time, driver range anxiety and uncertain transportation demand. This mo...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cheng, Kai [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Self-assembled 3D hierarchical MnCO - Rajendiran, Rajmohan ELSEVIER, 2020, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:276 ; year:2020 ; day:10 ; month:12 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.jclepro.2020.124198 |
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| Katalog-ID: |
ELV051715740 |
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| 520 | |a This paper investigates a novel design problem involving an optimal government lane expansion scheme for a battery electric vehicle (BEV) transportation network. A lane expansion model is established considering the BEV charging time, driver range anxiety and uncertain transportation demand. This model aims to minimize the total travel time (i.e., sum of the driving time and charging time) of all drivers in the transportation network and optimize the lane expansion scheme (i.e., the number and location of extended lanes in the network) under the established investment ceiling. To address demand uncertainty, an improved adjustable robust optimization method is further proposed to relax the model by introducing two control parameters. Based on the framework of the active set algorithm, a local optimal solution algorithm is designed to effectively solve the abovementioned model. Column generation is embedded in the above algorithm to avoid path enumeration. Sensitivity analyses are conducted for different control parameters and government investment scales. The results show that the model and algorithm we proposed can provide a theoretical basis for the government to improve the traffic efficiency of the transportation network and achieve the goal of sustainable transport. | ||
| 520 | |a This paper investigates a novel design problem involving an optimal government lane expansion scheme for a battery electric vehicle (BEV) transportation network. A lane expansion model is established considering the BEV charging time, driver range anxiety and uncertain transportation demand. This model aims to minimize the total travel time (i.e., sum of the driving time and charging time) of all drivers in the transportation network and optimize the lane expansion scheme (i.e., the number and location of extended lanes in the network) under the established investment ceiling. To address demand uncertainty, an improved adjustable robust optimization method is further proposed to relax the model by introducing two control parameters. Based on the framework of the active set algorithm, a local optimal solution algorithm is designed to effectively solve the abovementioned model. Column generation is embedded in the above algorithm to avoid path enumeration. Sensitivity analyses are conducted for different control parameters and government investment scales. The results show that the model and algorithm we proposed can provide a theoretical basis for the government to improve the traffic efficiency of the transportation network and achieve the goal of sustainable transport. | ||
| 650 | 7 | |a User equilibrium |2 Elsevier | |
| 650 | 7 | |a Column generation |2 Elsevier | |
| 650 | 7 | |a Network design |2 Elsevier | |
| 650 | 7 | |a Robust optimization |2 Elsevier | |
| 650 | 7 | |a Battery electric vehicle (BEV) |2 Elsevier | |
| 650 | 7 | |a Demand uncertainty |2 Elsevier | |
| 700 | 1 | |a Zou, Yajie |4 oth | |
| 700 | 1 | |a Xin, Xu |4 oth | |
| 700 | 1 | |a Gong, Shuaiyu |4 oth | |
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10.1016/j.jclepro.2020.124198 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001837.pica (DE-627)ELV051715740 (ELSEVIER)S0959-6526(20)34243-8 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Cheng, Kai verfasserin aut Optimal lane expansion model for a battery electric vehicle transportation network considering range anxiety and demand uncertainty 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper investigates a novel design problem involving an optimal government lane expansion scheme for a battery electric vehicle (BEV) transportation network. A lane expansion model is established considering the BEV charging time, driver range anxiety and uncertain transportation demand. This model aims to minimize the total travel time (i.e., sum of the driving time and charging time) of all drivers in the transportation network and optimize the lane expansion scheme (i.e., the number and location of extended lanes in the network) under the established investment ceiling. To address demand uncertainty, an improved adjustable robust optimization method is further proposed to relax the model by introducing two control parameters. Based on the framework of the active set algorithm, a local optimal solution algorithm is designed to effectively solve the abovementioned model. Column generation is embedded in the above algorithm to avoid path enumeration. Sensitivity analyses are conducted for different control parameters and government investment scales. The results show that the model and algorithm we proposed can provide a theoretical basis for the government to improve the traffic efficiency of the transportation network and achieve the goal of sustainable transport. This paper investigates a novel design problem involving an optimal government lane expansion scheme for a battery electric vehicle (BEV) transportation network. A lane expansion model is established considering the BEV charging time, driver range anxiety and uncertain transportation demand. This model aims to minimize the total travel time (i.e., sum of the driving time and charging time) of all drivers in the transportation network and optimize the lane expansion scheme (i.e., the number and location of extended lanes in the network) under the established investment ceiling. To address demand uncertainty, an improved adjustable robust optimization method is further proposed to relax the model by introducing two control parameters. Based on the framework of the active set algorithm, a local optimal solution algorithm is designed to effectively solve the abovementioned model. Column generation is embedded in the above algorithm to avoid path enumeration. Sensitivity analyses are conducted for different control parameters and government investment scales. The results show that the model and algorithm we proposed can provide a theoretical basis for the government to improve the traffic efficiency of the transportation network and achieve the goal of sustainable transport. User equilibrium Elsevier Column generation Elsevier Network design Elsevier Robust optimization Elsevier Battery electric vehicle (BEV) Elsevier Demand uncertainty Elsevier Zou, Yajie oth Xin, Xu oth Gong, Shuaiyu oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:276 year:2020 day:10 month:12 pages:0 https://doi.org/10.1016/j.jclepro.2020.124198 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 276 2020 10 1210 0 |
| spelling |
10.1016/j.jclepro.2020.124198 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001837.pica (DE-627)ELV051715740 (ELSEVIER)S0959-6526(20)34243-8 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Cheng, Kai verfasserin aut Optimal lane expansion model for a battery electric vehicle transportation network considering range anxiety and demand uncertainty 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper investigates a novel design problem involving an optimal government lane expansion scheme for a battery electric vehicle (BEV) transportation network. A lane expansion model is established considering the BEV charging time, driver range anxiety and uncertain transportation demand. This model aims to minimize the total travel time (i.e., sum of the driving time and charging time) of all drivers in the transportation network and optimize the lane expansion scheme (i.e., the number and location of extended lanes in the network) under the established investment ceiling. To address demand uncertainty, an improved adjustable robust optimization method is further proposed to relax the model by introducing two control parameters. Based on the framework of the active set algorithm, a local optimal solution algorithm is designed to effectively solve the abovementioned model. Column generation is embedded in the above algorithm to avoid path enumeration. Sensitivity analyses are conducted for different control parameters and government investment scales. The results show that the model and algorithm we proposed can provide a theoretical basis for the government to improve the traffic efficiency of the transportation network and achieve the goal of sustainable transport. This paper investigates a novel design problem involving an optimal government lane expansion scheme for a battery electric vehicle (BEV) transportation network. A lane expansion model is established considering the BEV charging time, driver range anxiety and uncertain transportation demand. This model aims to minimize the total travel time (i.e., sum of the driving time and charging time) of all drivers in the transportation network and optimize the lane expansion scheme (i.e., the number and location of extended lanes in the network) under the established investment ceiling. To address demand uncertainty, an improved adjustable robust optimization method is further proposed to relax the model by introducing two control parameters. Based on the framework of the active set algorithm, a local optimal solution algorithm is designed to effectively solve the abovementioned model. Column generation is embedded in the above algorithm to avoid path enumeration. Sensitivity analyses are conducted for different control parameters and government investment scales. The results show that the model and algorithm we proposed can provide a theoretical basis for the government to improve the traffic efficiency of the transportation network and achieve the goal of sustainable transport. User equilibrium Elsevier Column generation Elsevier Network design Elsevier Robust optimization Elsevier Battery electric vehicle (BEV) Elsevier Demand uncertainty Elsevier Zou, Yajie oth Xin, Xu oth Gong, Shuaiyu oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:276 year:2020 day:10 month:12 pages:0 https://doi.org/10.1016/j.jclepro.2020.124198 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 276 2020 10 1210 0 |
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10.1016/j.jclepro.2020.124198 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001837.pica (DE-627)ELV051715740 (ELSEVIER)S0959-6526(20)34243-8 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Cheng, Kai verfasserin aut Optimal lane expansion model for a battery electric vehicle transportation network considering range anxiety and demand uncertainty 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper investigates a novel design problem involving an optimal government lane expansion scheme for a battery electric vehicle (BEV) transportation network. A lane expansion model is established considering the BEV charging time, driver range anxiety and uncertain transportation demand. This model aims to minimize the total travel time (i.e., sum of the driving time and charging time) of all drivers in the transportation network and optimize the lane expansion scheme (i.e., the number and location of extended lanes in the network) under the established investment ceiling. To address demand uncertainty, an improved adjustable robust optimization method is further proposed to relax the model by introducing two control parameters. Based on the framework of the active set algorithm, a local optimal solution algorithm is designed to effectively solve the abovementioned model. Column generation is embedded in the above algorithm to avoid path enumeration. Sensitivity analyses are conducted for different control parameters and government investment scales. The results show that the model and algorithm we proposed can provide a theoretical basis for the government to improve the traffic efficiency of the transportation network and achieve the goal of sustainable transport. This paper investigates a novel design problem involving an optimal government lane expansion scheme for a battery electric vehicle (BEV) transportation network. A lane expansion model is established considering the BEV charging time, driver range anxiety and uncertain transportation demand. This model aims to minimize the total travel time (i.e., sum of the driving time and charging time) of all drivers in the transportation network and optimize the lane expansion scheme (i.e., the number and location of extended lanes in the network) under the established investment ceiling. To address demand uncertainty, an improved adjustable robust optimization method is further proposed to relax the model by introducing two control parameters. Based on the framework of the active set algorithm, a local optimal solution algorithm is designed to effectively solve the abovementioned model. Column generation is embedded in the above algorithm to avoid path enumeration. Sensitivity analyses are conducted for different control parameters and government investment scales. The results show that the model and algorithm we proposed can provide a theoretical basis for the government to improve the traffic efficiency of the transportation network and achieve the goal of sustainable transport. User equilibrium Elsevier Column generation Elsevier Network design Elsevier Robust optimization Elsevier Battery electric vehicle (BEV) Elsevier Demand uncertainty Elsevier Zou, Yajie oth Xin, Xu oth Gong, Shuaiyu oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:276 year:2020 day:10 month:12 pages:0 https://doi.org/10.1016/j.jclepro.2020.124198 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 276 2020 10 1210 0 |
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10.1016/j.jclepro.2020.124198 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001837.pica (DE-627)ELV051715740 (ELSEVIER)S0959-6526(20)34243-8 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Cheng, Kai verfasserin aut Optimal lane expansion model for a battery electric vehicle transportation network considering range anxiety and demand uncertainty 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper investigates a novel design problem involving an optimal government lane expansion scheme for a battery electric vehicle (BEV) transportation network. A lane expansion model is established considering the BEV charging time, driver range anxiety and uncertain transportation demand. This model aims to minimize the total travel time (i.e., sum of the driving time and charging time) of all drivers in the transportation network and optimize the lane expansion scheme (i.e., the number and location of extended lanes in the network) under the established investment ceiling. To address demand uncertainty, an improved adjustable robust optimization method is further proposed to relax the model by introducing two control parameters. Based on the framework of the active set algorithm, a local optimal solution algorithm is designed to effectively solve the abovementioned model. Column generation is embedded in the above algorithm to avoid path enumeration. Sensitivity analyses are conducted for different control parameters and government investment scales. The results show that the model and algorithm we proposed can provide a theoretical basis for the government to improve the traffic efficiency of the transportation network and achieve the goal of sustainable transport. This paper investigates a novel design problem involving an optimal government lane expansion scheme for a battery electric vehicle (BEV) transportation network. A lane expansion model is established considering the BEV charging time, driver range anxiety and uncertain transportation demand. This model aims to minimize the total travel time (i.e., sum of the driving time and charging time) of all drivers in the transportation network and optimize the lane expansion scheme (i.e., the number and location of extended lanes in the network) under the established investment ceiling. To address demand uncertainty, an improved adjustable robust optimization method is further proposed to relax the model by introducing two control parameters. Based on the framework of the active set algorithm, a local optimal solution algorithm is designed to effectively solve the abovementioned model. Column generation is embedded in the above algorithm to avoid path enumeration. Sensitivity analyses are conducted for different control parameters and government investment scales. The results show that the model and algorithm we proposed can provide a theoretical basis for the government to improve the traffic efficiency of the transportation network and achieve the goal of sustainable transport. User equilibrium Elsevier Column generation Elsevier Network design Elsevier Robust optimization Elsevier Battery electric vehicle (BEV) Elsevier Demand uncertainty Elsevier Zou, Yajie oth Xin, Xu oth Gong, Shuaiyu oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:276 year:2020 day:10 month:12 pages:0 https://doi.org/10.1016/j.jclepro.2020.124198 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 276 2020 10 1210 0 |
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10.1016/j.jclepro.2020.124198 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001837.pica (DE-627)ELV051715740 (ELSEVIER)S0959-6526(20)34243-8 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Cheng, Kai verfasserin aut Optimal lane expansion model for a battery electric vehicle transportation network considering range anxiety and demand uncertainty 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper investigates a novel design problem involving an optimal government lane expansion scheme for a battery electric vehicle (BEV) transportation network. A lane expansion model is established considering the BEV charging time, driver range anxiety and uncertain transportation demand. This model aims to minimize the total travel time (i.e., sum of the driving time and charging time) of all drivers in the transportation network and optimize the lane expansion scheme (i.e., the number and location of extended lanes in the network) under the established investment ceiling. To address demand uncertainty, an improved adjustable robust optimization method is further proposed to relax the model by introducing two control parameters. Based on the framework of the active set algorithm, a local optimal solution algorithm is designed to effectively solve the abovementioned model. Column generation is embedded in the above algorithm to avoid path enumeration. Sensitivity analyses are conducted for different control parameters and government investment scales. The results show that the model and algorithm we proposed can provide a theoretical basis for the government to improve the traffic efficiency of the transportation network and achieve the goal of sustainable transport. This paper investigates a novel design problem involving an optimal government lane expansion scheme for a battery electric vehicle (BEV) transportation network. A lane expansion model is established considering the BEV charging time, driver range anxiety and uncertain transportation demand. This model aims to minimize the total travel time (i.e., sum of the driving time and charging time) of all drivers in the transportation network and optimize the lane expansion scheme (i.e., the number and location of extended lanes in the network) under the established investment ceiling. To address demand uncertainty, an improved adjustable robust optimization method is further proposed to relax the model by introducing two control parameters. Based on the framework of the active set algorithm, a local optimal solution algorithm is designed to effectively solve the abovementioned model. Column generation is embedded in the above algorithm to avoid path enumeration. Sensitivity analyses are conducted for different control parameters and government investment scales. The results show that the model and algorithm we proposed can provide a theoretical basis for the government to improve the traffic efficiency of the transportation network and achieve the goal of sustainable transport. User equilibrium Elsevier Column generation Elsevier Network design Elsevier Robust optimization Elsevier Battery electric vehicle (BEV) Elsevier Demand uncertainty Elsevier Zou, Yajie oth Xin, Xu oth Gong, Shuaiyu oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:276 year:2020 day:10 month:12 pages:0 https://doi.org/10.1016/j.jclepro.2020.124198 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 276 2020 10 1210 0 |
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Enthalten in Self-assembled 3D hierarchical MnCO Amsterdam [u.a.] volume:276 year:2020 day:10 month:12 pages:0 |
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A lane expansion model is established considering the BEV charging time, driver range anxiety and uncertain transportation demand. This model aims to minimize the total travel time (i.e., sum of the driving time and charging time) of all drivers in the transportation network and optimize the lane expansion scheme (i.e., the number and location of extended lanes in the network) under the established investment ceiling. To address demand uncertainty, an improved adjustable robust optimization method is further proposed to relax the model by introducing two control parameters. Based on the framework of the active set algorithm, a local optimal solution algorithm is designed to effectively solve the abovementioned model. Column generation is embedded in the above algorithm to avoid path enumeration. Sensitivity analyses are conducted for different control parameters and government investment scales. The results show that the model and algorithm we proposed can provide a theoretical basis for the government to improve the traffic efficiency of the transportation network and achieve the goal of sustainable transport.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper investigates a novel design problem involving an optimal government lane expansion scheme for a battery electric vehicle (BEV) transportation network. A lane expansion model is established considering the BEV charging time, driver range anxiety and uncertain transportation demand. This model aims to minimize the total travel time (i.e., sum of the driving time and charging time) of all drivers in the transportation network and optimize the lane expansion scheme (i.e., the number and location of extended lanes in the network) under the established investment ceiling. To address demand uncertainty, an improved adjustable robust optimization method is further proposed to relax the model by introducing two control parameters. Based on the framework of the active set algorithm, a local optimal solution algorithm is designed to effectively solve the abovementioned model. Column generation is embedded in the above algorithm to avoid path enumeration. Sensitivity analyses are conducted for different control parameters and government investment scales. 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Optimal lane expansion model for a battery electric vehicle transportation network considering range anxiety and demand uncertainty |
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This paper investigates a novel design problem involving an optimal government lane expansion scheme for a battery electric vehicle (BEV) transportation network. A lane expansion model is established considering the BEV charging time, driver range anxiety and uncertain transportation demand. This model aims to minimize the total travel time (i.e., sum of the driving time and charging time) of all drivers in the transportation network and optimize the lane expansion scheme (i.e., the number and location of extended lanes in the network) under the established investment ceiling. To address demand uncertainty, an improved adjustable robust optimization method is further proposed to relax the model by introducing two control parameters. Based on the framework of the active set algorithm, a local optimal solution algorithm is designed to effectively solve the abovementioned model. Column generation is embedded in the above algorithm to avoid path enumeration. Sensitivity analyses are conducted for different control parameters and government investment scales. The results show that the model and algorithm we proposed can provide a theoretical basis for the government to improve the traffic efficiency of the transportation network and achieve the goal of sustainable transport. |
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This paper investigates a novel design problem involving an optimal government lane expansion scheme for a battery electric vehicle (BEV) transportation network. A lane expansion model is established considering the BEV charging time, driver range anxiety and uncertain transportation demand. This model aims to minimize the total travel time (i.e., sum of the driving time and charging time) of all drivers in the transportation network and optimize the lane expansion scheme (i.e., the number and location of extended lanes in the network) under the established investment ceiling. To address demand uncertainty, an improved adjustable robust optimization method is further proposed to relax the model by introducing two control parameters. Based on the framework of the active set algorithm, a local optimal solution algorithm is designed to effectively solve the abovementioned model. Column generation is embedded in the above algorithm to avoid path enumeration. Sensitivity analyses are conducted for different control parameters and government investment scales. The results show that the model and algorithm we proposed can provide a theoretical basis for the government to improve the traffic efficiency of the transportation network and achieve the goal of sustainable transport. |
| abstract_unstemmed |
This paper investigates a novel design problem involving an optimal government lane expansion scheme for a battery electric vehicle (BEV) transportation network. A lane expansion model is established considering the BEV charging time, driver range anxiety and uncertain transportation demand. This model aims to minimize the total travel time (i.e., sum of the driving time and charging time) of all drivers in the transportation network and optimize the lane expansion scheme (i.e., the number and location of extended lanes in the network) under the established investment ceiling. To address demand uncertainty, an improved adjustable robust optimization method is further proposed to relax the model by introducing two control parameters. Based on the framework of the active set algorithm, a local optimal solution algorithm is designed to effectively solve the abovementioned model. Column generation is embedded in the above algorithm to avoid path enumeration. Sensitivity analyses are conducted for different control parameters and government investment scales. The results show that the model and algorithm we proposed can provide a theoretical basis for the government to improve the traffic efficiency of the transportation network and achieve the goal of sustainable transport. |
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