This study introduces a comprehensive and automated framework that leverages data-driven method-ologies to address various challenges in shale gas development and production.Specifically,it harnesses the power of Auto...This study introduces a comprehensive and automated framework that leverages data-driven method-ologies to address various challenges in shale gas development and production.Specifically,it harnesses the power of Automated Machine Learning(AutoML)to construct an ensemble model to predict the estimated ultimate recovery(EUR)of shale gas wells.To demystify the“black-box”nature of the ensemble model,KernelSHAP,a kernel-based approach to compute Shapley values,is utilized for elucidating the influential factors that affect shale gas production at both global and local scales.Furthermore,a bi-objective optimization algorithm named NSGA-Ⅱ is seamlessly incorporated to opti-mize hydraulic fracturing designs for production boost and cost control.This innovative framework addresses critical limitations often encountered in applying machine learning(ML)to shale gas pro-duction:the challenge of achieving sufficient model accuracy with limited samples,the multidisciplinary expertise required for developing robust ML models,and the need for interpretability in“black-box”models.Validation with field data from the Fuling shale gas field in the Sichuan Basin substantiates the framework's efficacy in enhancing the precision and applicability of data-driven techniques.The test accuracy of the ensemble ML model reached 83%compared to a maximum of 72%of single ML models.The contribution of each geological and engineering factor to the overall production was quantitatively evaluated.Fracturing design optimization raised EUR by 7%-34%under different production and cost tradeoff scenarios.The results empower domain experts to conduct more precise and objective data-driven analyses and optimizations for shale gas production with minimal expertise in data science.展开更多
Traffic congestion in road transportation networks is a persistent problem in major metropolitan cities around the world.In this context,this paper deals with exploiting underutilized road capacities in a network to l...Traffic congestion in road transportation networks is a persistent problem in major metropolitan cities around the world.In this context,this paper deals with exploiting underutilized road capacities in a network to lower the congestion on overutilized links while simultaneously satisfying the system optimal flow assignment for sustainable transportation.Four congestion mitigation strategies are identified based on deviation and relative deviation of link volume from the corresponding capacity.Consequently,four biobjective mathematical programming optimal flow distribution(OFD)models are proposed.The case study results demonstrate that all the proposed models improve system performance and reduce congestion on high volume links by shifting flows to low volumeto-capacity links compared to UE and SO models.Among the models,the system optimality with minimal sum and maximum absolute relative-deviation models(SO-SAR and SO-MAR)showed superior results for different performance measures.The SO-SAR model yielded 50%and 30%fewer links at higher link utilization factors than UE and SO models,respectively.Also,it showed more than 25%improvement in path travel times compared to UE travel time for about 100 paths and resulted in the least network congestion index of1.04 compared to the other OFD and UE models.Conversely,the SO-MAR model yielded the least total distance and total system travel time,resulting in lower fuel consumption and emissions,thus contributing to sustainability.The proposed models contribute towards efficient transportation infrastructure management and will be of interest to transportation planners and traffic managers.展开更多
Faced with economic recession,firms struggle to find ways to stay competitive and maintain market share.Effective coordination of the supply chain can solve this problem,but this may fail if existing capital constrain...Faced with economic recession,firms struggle to find ways to stay competitive and maintain market share.Effective coordination of the supply chain can solve this problem,but this may fail if existing capital constraints and financial flows are ignored.This study addresses the challenge by exploiting coordination through joint decision-making on the physical and financial flows of a capital-constrained supply chain.We also consider the capital-constrained member’s financing limitations that lead to lost sales.Two scenarios based on non-coordinated and coordinated structures are modeled in the form of bi-objective optimization problems that simultaneously optimize system costs and service levels.The models are solved using the-constraint method.The results indicate that the non-coordinated model cannot satisfy more than about 50%of the demand due to capital shortage and financing limitations,while the coordinated model can satisfy all of the demand via internal financing.Furthermore,the proposed coordination scheme leads to cost reduction for the members and the total system.To motivate all members to accept the proposed coordination scheme,a cost-sharing mechanism is applied to the coordination procedure.Finally,a sensitivity analysis concerning financial parameters is provided for validating the coordination model.展开更多
Ship air emissions are recognized as one of the key concerns of the maritime industry.Competent authorities have issued various regulations to manage air emissions from ships.Although the authorities are policy makers...Ship air emissions are recognized as one of the key concerns of the maritime industry.Competent authorities have issued various regulations to manage air emissions from ships.Although the authorities are policy makers,the effectiveness of policies is up to the shipping industry who operates the vessels and terminals to fulfill maritime transportation works.Given this characteristic,bi-level optimization model has been widely adopted in studies that optimize policy design or evaluate its effectiveness.The framework of a typical bi-level optimization model for ship emission management problem is given to show the basic structure of similar issues.A series of applications of bi-level optimization model in managing ship emissions is reviewed,including cases of Energy Efficiency Design Index,Emissions Control Area,Market Based Measure,Carbon Intensity Indicator,and Vessel Speed Reduction Incentive Program.We hope this paper can enlighten scholars interested in this area and provide help for them.展开更多
In order to address the optimal distance toll design problem for cordon-based congestion pricing incorporating the issue of equity,this paper presents a toll user equilibrium( TUE) model based on a transformed network...In order to address the optimal distance toll design problem for cordon-based congestion pricing incorporating the issue of equity,this paper presents a toll user equilibrium( TUE) model based on a transformed network with elastic demand,to evaluate any given toll charge function. A bi-level programming model is developed for determining the optimal toll levels,with the TUE being represented at the lower level.The upper level optimizes the total equity level over the transport network,represented by the Gini coefficient,where a constraint is imposed to the total travel impedance of each OD pair after the levy. A genetic algorithm( GA) is implemented to solve the bi-level model,which is verified by a numerical example.展开更多
Optimizing the allocation of water resources is critical for promoting the optimization and upgrading of industrial structure and coordinated development in the Beijing-Tianjin-Hebei regions of China.Based on specific...Optimizing the allocation of water resources is critical for promoting the optimization and upgrading of industrial structure and coordinated development in the Beijing-Tianjin-Hebei regions of China.Based on specific regional and water conditions,to strengthen the constraints on water resources,the“three-step”adaptive management approach of“scheme design-scheme diagnosis-scheme optimization”of water resource allocation are adopted to facilitate the coordinated optimal allocation of water resources and industrial structure in the Beijing-Tianjin-Hebei regions.First,from the level of overall industry,a water resource allocation scheme for the regions is designed by applying the master-slave hierarchical mode and a bi-level optimal model to determine the ideal amount of water resource allocation for the regions and respective industries.Second,the diagnostic criteria of spatial balance,structural matching,and coordinated development are constructed to determine the rationality of the water resource allocation scheme.Then a benefit compensation function with water market transactions is developed,to adaptively adjust the water resource allocation scheme.Finally,the optimization and upgrading of industrial structure are promoted to improve water consumption efficiency and the coordinated development of the Beijing-Tianjin-Hebei regions.The study can provide reference for the Beijing-Tianjin-Hebei regions to realize the comprehensive optimal allocation of water resources in the regions and improve the adaptability of water resources and industrial structure optimization.展开更多
The operation characteristics of energy storage can help the distribution network absorb more renewable energy while improving the safety and economy of the power system.Mobile energy storage systems(MESSs)have a broa...The operation characteristics of energy storage can help the distribution network absorb more renewable energy while improving the safety and economy of the power system.Mobile energy storage systems(MESSs)have a broad application market compared with stationary energy storage systems and electric vehicles due to their flexible mobility and good dispatch ability.However,when urban traffic flows rise,the congested traffic environment will prolong the transit time of MESS,which will ultimately affect the operation state of the power networks and the economic benefits of MESS.This paper proposes a bi-level optimization model for the economic operation of MESS in coupled transportation-power networks,considering road congestion and the operation constraints of the power networks.The upper-level model depicts the daily operation scheme of MESS devised by the distribution network operator(DNO)in order to maximize the total revenue of the system.With fuzzy time windows and fuzzy road congestion indexes,the lower-level model optimizes the route for the transit problem of MESS.Therefore,road congestion that affects the transit time of MESS can be fully incorporated in the optimal operation scheme.Both the IEEE 33-bus distribution network and the 29-node transportation network are used to verify and examine the effectiveness of the proposed model.The simulation results demonstrate that the operation scheme of MESS will avoid the congestion period when considering road congestion.Besides,the transit energy consumption and the impact of the traffic environment on the economic benefits of MESS can be reduced.展开更多
In this letter, we propose a market-based bi-level conic optimal energy flow (OEF) model of integrated electricity and natural gas systems (IENGSs). Conic alternating current optimal power flow (ACOPF) is formulated i...In this letter, we propose a market-based bi-level conic optimal energy flow (OEF) model of integrated electricity and natural gas systems (IENGSs). Conic alternating current optimal power flow (ACOPF) is formulated in the upper-level model, and the generation cost of natural gas fired generation units (NGFGUs) is calculated based on natural gas locational marginal prices (NG-LMPs). The market clearing process of natural gas system is modeled in the lower-level model. The bi-level model is then transferred into a mixed-integer second-order cone programming (MISOCP) problem. Simulation results demonstrate the effectiveness of the proposed conic OEF model.展开更多
A chaotic algorithm for providing a solution to the bi-level Discrete Equilibrium Network Design Problem （NDP） is discussed following an introduction of the Discrete Network Design Problem （DNDP） model and Chaos O...A chaotic algorithm for providing a solution to the bi-level Discrete Equilibrium Network Design Problem （NDP） is discussed following an introduction of the Discrete Network Design Problem （DNDP） model and Chaos Optimization Algorithms （COA）. A description of the chaotic approach for the DNDP model is described in details. Then a numerical example for the DNDP is carried out to investigate the chaotic approach. The results have been encouraging, indicating that the chaotic approach has great potential ability in finding the optimal solution of DNDP models.展开更多
This study presents a descriptive and prescriptive analysis of rail service subsidies for China Railway Express(CRE)in the China-Europe freight transportation market.The analysis is conducted by advanced mathematical ...This study presents a descriptive and prescriptive analysis of rail service subsidies for China Railway Express(CRE)in the China-Europe freight transportation market.The analysis is conducted by advanced mathematical modeling and programming methods.Specifically,we implemented a multicommodity multimodal freight transportation network equilib-rium model that can be used for predicting the commodity-specific mode-route cargo flow pattern and hence for assessing the effectiveness and limitations of the current CRE subsidy scheme.To properly quantify the impact of subsidies on individual shippers’decision mak-ing,the model explicitly characterizes individual shippers’mode-route choice behavior and takes into account shipping cost,transit time,capacity-induced congestion surcharge,and unobserved transportation impedances as shippers’disutility.The solution of the net-work equilibrium model resorts to a disaggregate simplicial decomposition(DSD)algo-rithm within the well-known Lagrangian relaxation framework.A bi-level network-based subsidy optimization model is constructed,in which the upper level aims at mini-mizing the sum of revenue loss and congestion charge,and the lower level is the aforemen-tioned freight transportation network equilibrium model.A tabu search procedure is proposed and implemented to derive the solution of the bi-level model.The above models and algorithms are then applied to the China-Europe containerized freight transportation network,which comprises all China-Europe liner shipping lines,all CRE service lines,and the highway networks in China and Europe.The evaluation and optimization results show that the current subsidy scheme creates an imbalanced capacity utilization pattern across CRE service lines while an optimized line-specific subsidy solution can yield note-worthy improvements in the service utilization and economic efficiency of CRE.展开更多
Energy issues in transportation systems have garnered increasing attention recently.This study proposes a systematic methodology for policy-makers to minimize energy consumption in multimodal intercity transportation ...Energy issues in transportation systems have garnered increasing attention recently.This study proposes a systematic methodology for policy-makers to minimize energy consumption in multimodal intercity transportation systems considering suppliers’operational constraints and travelers’mobility requirements.A bi-level optimization model is developed for this purpose and considers the air,rail,private auto,and transit modes.The upper-level model is a mixed integer nonlinear program aiming to minimize energy consumption subject to transportation suppliers’operational constraints and traffic demand distribution to paths resulting from the lower-level model.The lower-level model is a linear program seeking to maximize the trip utilities of travelers.The interactions between the multimodal transportation suppliers and intercity traffic demand are considered under the goal of minimizing system energy consumption.The proposed bi-level mixed integer model is relaxed and transformed into a mathematical program with complementarity constraints,and solved using a customized branch-and-bound algorithm.Numerical experiments,conducted using multimodal travel options between Lafayette,Indiana and Washington,D.C.reiterate that shifting traffic demand from private cars to the transit and rail modes significantly reduce energy consumption.Moreover,the proposed methodology provides tools to quantitatively analyze system energy consumption and traffic demand distribution among transportation modes under specific policy instruments.The results illustrate the need to systematically incorporate the interactions among traveler preferences,network structure,and supplier operational schemes to provide policy-makers insights for developing traffic demand shift mechanisms to minimize system energy consumption.Hence,the proposed methodology provide policy-makers the capability to analyze energy consumption in the transportation sector by a holistic approach.展开更多
This paper presents a planning and real-time pricing approach for EV charging stations(CSs).The approach takes the form of a bi-level model to fully consider the interest of both the government and EV charging station...This paper presents a planning and real-time pricing approach for EV charging stations(CSs).The approach takes the form of a bi-level model to fully consider the interest of both the government and EV charging station operators in the planning process.From the perspective of maximizing social welfare,the government acts as the decision-maker of the upper level that optimizes the charging price matrix,and uses it as a transfer variable to indirectly influence the decisions of the lower level operators.Then each operator at the lower level determines their scale according to the goal of maximizing their own revenue,and feeds the scale matrix back to the upper level.A Logit model is applied to predict the drivers’preference when selecting a CS.Furthermore,an improved particle swarm optimization(PSO)with the utilization of a penalty function is introduced to solve the nonlinear nonconvex bi-level model.The paper applies the proposed Bi-level planning model to a singlecenter small/medium-sized city with three scenarios to evaluate its performance,including the equipment utilization rate,payback period,traffic attraction ability,etc.The result verifies that the model performs very well in typical CS distribution scenarios with a reasonable station payback period(average 6.5 years),and relatively high equipment utilization rate,44.32%.展开更多
基金funded by the National Natural Science Foundation of China(42050104).
文摘This study introduces a comprehensive and automated framework that leverages data-driven method-ologies to address various challenges in shale gas development and production.Specifically,it harnesses the power of Automated Machine Learning(AutoML)to construct an ensemble model to predict the estimated ultimate recovery(EUR)of shale gas wells.To demystify the“black-box”nature of the ensemble model,KernelSHAP,a kernel-based approach to compute Shapley values,is utilized for elucidating the influential factors that affect shale gas production at both global and local scales.Furthermore,a bi-objective optimization algorithm named NSGA-Ⅱ is seamlessly incorporated to opti-mize hydraulic fracturing designs for production boost and cost control.This innovative framework addresses critical limitations often encountered in applying machine learning(ML)to shale gas pro-duction:the challenge of achieving sufficient model accuracy with limited samples,the multidisciplinary expertise required for developing robust ML models,and the need for interpretability in“black-box”models.Validation with field data from the Fuling shale gas field in the Sichuan Basin substantiates the framework's efficacy in enhancing the precision and applicability of data-driven techniques.The test accuracy of the ensemble ML model reached 83%compared to a maximum of 72%of single ML models.The contribution of each geological and engineering factor to the overall production was quantitatively evaluated.Fracturing design optimization raised EUR by 7%-34%under different production and cost tradeoff scenarios.The results empower domain experts to conduct more precise and objective data-driven analyses and optimizations for shale gas production with minimal expertise in data science.
文摘Traffic congestion in road transportation networks is a persistent problem in major metropolitan cities around the world.In this context,this paper deals with exploiting underutilized road capacities in a network to lower the congestion on overutilized links while simultaneously satisfying the system optimal flow assignment for sustainable transportation.Four congestion mitigation strategies are identified based on deviation and relative deviation of link volume from the corresponding capacity.Consequently,four biobjective mathematical programming optimal flow distribution(OFD)models are proposed.The case study results demonstrate that all the proposed models improve system performance and reduce congestion on high volume links by shifting flows to low volumeto-capacity links compared to UE and SO models.Among the models,the system optimality with minimal sum and maximum absolute relative-deviation models(SO-SAR and SO-MAR)showed superior results for different performance measures.The SO-SAR model yielded 50%and 30%fewer links at higher link utilization factors than UE and SO models,respectively.Also,it showed more than 25%improvement in path travel times compared to UE travel time for about 100 paths and resulted in the least network congestion index of1.04 compared to the other OFD and UE models.Conversely,the SO-MAR model yielded the least total distance and total system travel time,resulting in lower fuel consumption and emissions,thus contributing to sustainability.The proposed models contribute towards efficient transportation infrastructure management and will be of interest to transportation planners and traffic managers.
文摘Faced with economic recession,firms struggle to find ways to stay competitive and maintain market share.Effective coordination of the supply chain can solve this problem,but this may fail if existing capital constraints and financial flows are ignored.This study addresses the challenge by exploiting coordination through joint decision-making on the physical and financial flows of a capital-constrained supply chain.We also consider the capital-constrained member’s financing limitations that lead to lost sales.Two scenarios based on non-coordinated and coordinated structures are modeled in the form of bi-objective optimization problems that simultaneously optimize system costs and service levels.The models are solved using the-constraint method.The results indicate that the non-coordinated model cannot satisfy more than about 50%of the demand due to capital shortage and financing limitations,while the coordinated model can satisfy all of the demand via internal financing.Furthermore,the proposed coordination scheme leads to cost reduction for the members and the total system.To motivate all members to accept the proposed coordination scheme,a cost-sharing mechanism is applied to the coordination procedure.Finally,a sensitivity analysis concerning financial parameters is provided for validating the coordination model.
基金supported by the National Natural Science Foundation of China(72071173,71831008).
文摘Ship air emissions are recognized as one of the key concerns of the maritime industry.Competent authorities have issued various regulations to manage air emissions from ships.Although the authorities are policy makers,the effectiveness of policies is up to the shipping industry who operates the vessels and terminals to fulfill maritime transportation works.Given this characteristic,bi-level optimization model has been widely adopted in studies that optimize policy design or evaluate its effectiveness.The framework of a typical bi-level optimization model for ship emission management problem is given to show the basic structure of similar issues.A series of applications of bi-level optimization model in managing ship emissions is reviewed,including cases of Energy Efficiency Design Index,Emissions Control Area,Market Based Measure,Carbon Intensity Indicator,and Vessel Speed Reduction Incentive Program.We hope this paper can enlighten scholars interested in this area and provide help for them.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61374195 and 71501038)the Fundamental Research Funds for the Central Universities(Grant No.2242015R30036)the Natural Science Foundation of Jiangsu Province in China(Grant No.BK20150603)
文摘In order to address the optimal distance toll design problem for cordon-based congestion pricing incorporating the issue of equity,this paper presents a toll user equilibrium( TUE) model based on a transformed network with elastic demand,to evaluate any given toll charge function. A bi-level programming model is developed for determining the optimal toll levels,with the TUE being represented at the lower level.The upper level optimizes the total equity level over the transport network,represented by the Gini coefficient,where a constraint is imposed to the total travel impedance of each OD pair after the levy. A genetic algorithm( GA) is implemented to solve the bi-level model,which is verified by a numerical example.
基金supported by the Humanities and Social Science Foundation of Ministry of Education“Research on the Optimal Adaptability of Basin Initial Water Rights and Industrial Structures under the Rigid Constraints of Water Resource”[Grant number.21YJCZH176]Beijing Municipal Natural Science Foundation of China“Research on Bi-directional Optimal Adaptability of Water Resource and Industrial Structures under the Coordinated Development of the Beijing-Tianjin-Hebei Region”(Grant number.9202005)+1 种基金the Humanities and Social Science Foundation of Ministry of Education“Research on Complex System Model of Industrial Water Rights Trading Based on Experimental Economics and Dynamic Simulation under Dual Control Action”[Grant number.20YJCZH095]General Projects of Social Science Plan of Beijing Municipal Education Commission[Grant number.SM201910009007].
文摘Optimizing the allocation of water resources is critical for promoting the optimization and upgrading of industrial structure and coordinated development in the Beijing-Tianjin-Hebei regions of China.Based on specific regional and water conditions,to strengthen the constraints on water resources,the“three-step”adaptive management approach of“scheme design-scheme diagnosis-scheme optimization”of water resource allocation are adopted to facilitate the coordinated optimal allocation of water resources and industrial structure in the Beijing-Tianjin-Hebei regions.First,from the level of overall industry,a water resource allocation scheme for the regions is designed by applying the master-slave hierarchical mode and a bi-level optimal model to determine the ideal amount of water resource allocation for the regions and respective industries.Second,the diagnostic criteria of spatial balance,structural matching,and coordinated development are constructed to determine the rationality of the water resource allocation scheme.Then a benefit compensation function with water market transactions is developed,to adaptively adjust the water resource allocation scheme.Finally,the optimization and upgrading of industrial structure are promoted to improve water consumption efficiency and the coordinated development of the Beijing-Tianjin-Hebei regions.The study can provide reference for the Beijing-Tianjin-Hebei regions to realize the comprehensive optimal allocation of water resources in the regions and improve the adaptability of water resources and industrial structure optimization.
基金supported in part by the National Natural Science Foundation of China(No.51777126).
文摘The operation characteristics of energy storage can help the distribution network absorb more renewable energy while improving the safety and economy of the power system.Mobile energy storage systems(MESSs)have a broad application market compared with stationary energy storage systems and electric vehicles due to their flexible mobility and good dispatch ability.However,when urban traffic flows rise,the congested traffic environment will prolong the transit time of MESS,which will ultimately affect the operation state of the power networks and the economic benefits of MESS.This paper proposes a bi-level optimization model for the economic operation of MESS in coupled transportation-power networks,considering road congestion and the operation constraints of the power networks.The upper-level model depicts the daily operation scheme of MESS devised by the distribution network operator(DNO)in order to maximize the total revenue of the system.With fuzzy time windows and fuzzy road congestion indexes,the lower-level model optimizes the route for the transit problem of MESS.Therefore,road congestion that affects the transit time of MESS can be fully incorporated in the optimal operation scheme.Both the IEEE 33-bus distribution network and the 29-node transportation network are used to verify and examine the effectiveness of the proposed model.The simulation results demonstrate that the operation scheme of MESS will avoid the congestion period when considering road congestion.Besides,the transit energy consumption and the impact of the traffic environment on the economic benefits of MESS can be reduced.
基金The authors would like to thank the support in part by National Key Research and Development Program of China(No.2017YFB0903400)National Natural Science Foundation of China(Grant No.52007026)in part by CURENT,a U.S.NSF/DOE Engineering Research Center funded under NSF award EEC-1041877.
文摘In this letter, we propose a market-based bi-level conic optimal energy flow (OEF) model of integrated electricity and natural gas systems (IENGSs). Conic alternating current optimal power flow (ACOPF) is formulated in the upper-level model, and the generation cost of natural gas fired generation units (NGFGUs) is calculated based on natural gas locational marginal prices (NG-LMPs). The market clearing process of natural gas system is modeled in the lower-level model. The bi-level model is then transferred into a mixed-integer second-order cone programming (MISOCP) problem. Simulation results demonstrate the effectiveness of the proposed conic OEF model.
基金This project is supported partly by National 0utstanding Young Investigation of National Natural Science Foundation of China（70225005,70471088,70501004 and 70501005）, the Special Research Found for Doctoral Programs in State Education Ministry （20050004005）, the 211 Project of Discipline Construction of Beijing Jiaotong University and Rencai Foundation of Beijing Jiaotong University （2003RC010）
文摘A chaotic algorithm for providing a solution to the bi-level Discrete Equilibrium Network Design Problem （NDP） is discussed following an introduction of the Discrete Network Design Problem （DNDP） model and Chaos Optimization Algorithms （COA）. A description of the chaotic approach for the DNDP model is described in details. Then a numerical example for the DNDP is carried out to investigate the chaotic approach. The results have been encouraging, indicating that the chaotic approach has great potential ability in finding the optimal solution of DNDP models.
基金sponsored by National Natural Science Foundation of China(Nos.72171175 and 71771150)the Open Foundation of Key Laboratory of Transport Industry of Comprehensive Transportation Theory,and the Fundamental Research Funds of Central Universities at Tongji University.
文摘This study presents a descriptive and prescriptive analysis of rail service subsidies for China Railway Express(CRE)in the China-Europe freight transportation market.The analysis is conducted by advanced mathematical modeling and programming methods.Specifically,we implemented a multicommodity multimodal freight transportation network equilib-rium model that can be used for predicting the commodity-specific mode-route cargo flow pattern and hence for assessing the effectiveness and limitations of the current CRE subsidy scheme.To properly quantify the impact of subsidies on individual shippers’decision mak-ing,the model explicitly characterizes individual shippers’mode-route choice behavior and takes into account shipping cost,transit time,capacity-induced congestion surcharge,and unobserved transportation impedances as shippers’disutility.The solution of the net-work equilibrium model resorts to a disaggregate simplicial decomposition(DSD)algo-rithm within the well-known Lagrangian relaxation framework.A bi-level network-based subsidy optimization model is constructed,in which the upper level aims at mini-mizing the sum of revenue loss and congestion charge,and the lower level is the aforemen-tioned freight transportation network equilibrium model.A tabu search procedure is proposed and implemented to derive the solution of the bi-level model.The above models and algorithms are then applied to the China-Europe containerized freight transportation network,which comprises all China-Europe liner shipping lines,all CRE service lines,and the highway networks in China and Europe.The evaluation and optimization results show that the current subsidy scheme creates an imbalanced capacity utilization pattern across CRE service lines while an optimized line-specific subsidy solution can yield note-worthy improvements in the service utilization and economic efficiency of CRE.
基金funding provided by the U.S.Department of Transportation through the NEXTRANS Center,the USDOT Region 5 University Transportation Center.
文摘Energy issues in transportation systems have garnered increasing attention recently.This study proposes a systematic methodology for policy-makers to minimize energy consumption in multimodal intercity transportation systems considering suppliers’operational constraints and travelers’mobility requirements.A bi-level optimization model is developed for this purpose and considers the air,rail,private auto,and transit modes.The upper-level model is a mixed integer nonlinear program aiming to minimize energy consumption subject to transportation suppliers’operational constraints and traffic demand distribution to paths resulting from the lower-level model.The lower-level model is a linear program seeking to maximize the trip utilities of travelers.The interactions between the multimodal transportation suppliers and intercity traffic demand are considered under the goal of minimizing system energy consumption.The proposed bi-level mixed integer model is relaxed and transformed into a mathematical program with complementarity constraints,and solved using a customized branch-and-bound algorithm.Numerical experiments,conducted using multimodal travel options between Lafayette,Indiana and Washington,D.C.reiterate that shifting traffic demand from private cars to the transit and rail modes significantly reduce energy consumption.Moreover,the proposed methodology provides tools to quantitatively analyze system energy consumption and traffic demand distribution among transportation modes under specific policy instruments.The results illustrate the need to systematically incorporate the interactions among traveler preferences,network structure,and supplier operational schemes to provide policy-makers insights for developing traffic demand shift mechanisms to minimize system energy consumption.Hence,the proposed methodology provide policy-makers the capability to analyze energy consumption in the transportation sector by a holistic approach.
基金supported by the National Natural Science Foundation of China under Grant 51807024。
文摘This paper presents a planning and real-time pricing approach for EV charging stations(CSs).The approach takes the form of a bi-level model to fully consider the interest of both the government and EV charging station operators in the planning process.From the perspective of maximizing social welfare,the government acts as the decision-maker of the upper level that optimizes the charging price matrix,and uses it as a transfer variable to indirectly influence the decisions of the lower level operators.Then each operator at the lower level determines their scale according to the goal of maximizing their own revenue,and feeds the scale matrix back to the upper level.A Logit model is applied to predict the drivers’preference when selecting a CS.Furthermore,an improved particle swarm optimization(PSO)with the utilization of a penalty function is introduced to solve the nonlinear nonconvex bi-level model.The paper applies the proposed Bi-level planning model to a singlecenter small/medium-sized city with three scenarios to evaluate its performance,including the equipment utilization rate,payback period,traffic attraction ability,etc.The result verifies that the model performs very well in typical CS distribution scenarios with a reasonable station payback period(average 6.5 years),and relatively high equipment utilization rate,44.32%.
