A new travel time reliability-based traffic assignment model is proposed to investigate the effects of an advanced transportation information system (ATIS) on drivers' risk-taking path choice behaviours in transpo...A new travel time reliability-based traffic assignment model is proposed to investigate the effects of an advanced transportation information system (ATIS) on drivers' risk-taking path choice behaviours in transportation networks with demand uncertainty. In the model, drivers are divided into two classes. The first class is not equipped with ATIS, while the second class is equipped with ATIS. Different risk-taking path choice behaviours of the two classes are studied, respectively. A corresponding mixed equilibrium traffic assignment model is formulated as a variational inequality problem in terms of path flows, which is solved by a heuristic solution algorithm. Numerical results indicate that the ATIS can influence the drivers' risk-taking path choice behaviours and the total system travel time in transportation networks with demand uncertainty. It is also found that under higher demand levels, the benefits of ATIS for network performance enhancement may be more obvious.展开更多
Due to its harmful nature,any incident associated with hazardous material(hazmat)may cause tremendous impacts on the surrounding people and the environment.Focusing on the incident involving this specific type of good...Due to its harmful nature,any incident associated with hazardous material(hazmat)may cause tremendous impacts on the surrounding people and the environment.Focusing on the incident involving this specific type of good,we develop a reliable and robust emergency logistics network that considers both demand uncertainty and possible unavailability of particular links.A time-based risk measure is carefully designed upon the traditional risk assessment to reflect the stakeholder’s sensitivity to risk over response time.The disruption and uncertainty are modeled as two sets of scenarios which are integrated into a bi-objective robust model to evaluate the trade-offs between risk and cost.The effectiveness of the emergency response can be assured by expenditures that add extra capacities to certain links or establish additional facilities that aid recovery from incidents.We apply our model and approach to a real-world network in Guangdong China.Analytical results reveal the necessity of embedding consideration of uncertainty and unreliability into emergency network design problems;outline the importance of hedging against unpredictability by system redundancies;and indicate the impact of stakeholder’s orientation towards cost and risk on the location,allocation,and routing decisions in hazmat emergency response.展开更多
Agriculture is a key facilitator of economic prosperity and nourishes the huge global population.To achieve sustainable agriculture,several factors should be considered,such as increasing nutrient and water efficiency...Agriculture is a key facilitator of economic prosperity and nourishes the huge global population.To achieve sustainable agriculture,several factors should be considered,such as increasing nutrient and water efficiency and/or improving soil health and quality.Using fertilizer is one of the fastest and easiest ways to improve the quality of nutrients inland and increase the effectiveness of crop yields.Fertilizer supplies most of the necessary nutrients for plants,and it is estimated that at least 30%-50%of crop yields is attributable to commercial fertilizer nutrient inputs.Fertilizer is always a major concern in achieving sustainable and efficient agriculture.Applying reasonable and customized fertilizerswill require a significant increase in the number of formulae,involving increasing costs and the accurate forecasting of the right time to apply the suitable formulae.An alternative solution is given by two-stage production planning under stochastic demand,which divides a planning schedule into two stages.The primary stage has non-existing demand information,the inputs of which are the proportion of raw materials needed for producing fertilizer products,the cost for purchasing materials,and the production cost.The total quantity of purchased material and produced products to be used in the blending process must be defined to meet as small as possible a paid cost.At the second stage,demand appears under multiple scenarios and their respective possibilities.This stage will provide a solution for each occurring scenario to achieve the best profit.The two-stage approach is presented in this paper,the mathematical model of which is based on linear integer programming.Considering the diversity of fertilizer types,themathematicalmodel can advise manufacturers about which products will generate as much as profit as possible.Specifically,two objectives are taken into account.First,the paper’s thesis focuses on minimizing overall system costs,e.g.,including inventory cost,purchasing cost,unit cost,and ordering cost at Stage 1.Second,the thesis pays attention tomaximizing total profit based on information from customer demand,as well as being informed regarding concerns about system cost at Stage 2.展开更多
Consider an optimal procurement strategy for fresh produce,which is a type of perishable product.Due to the different quality provided by each manufacturer,the fresh produce qualification rates are dissimilar.Simultan...Consider an optimal procurement strategy for fresh produce,which is a type of perishable product.Due to the different quality provided by each manufacturer,the fresh produce qualification rates are dissimilar.Simultaneously,consumers demand is influenced by the freshness and price of products,as a result,the demand in the market is not fixed.In this scenario,how a particular retailer should develop an optimal procurement strategy will be a core issue in supply chain management.In order to address the above questions,the Bayesian approach is adopted to explore retailer optimal procurement strategies with uncertainty about product supply and market demand.Finally,simulation data are used to analyse the results of the proposed model and expected non-random model to illustrate the validity and feasibility of the proposed model.展开更多
This paper considers the problem of supply-demand imbalances in Mobility-on-Demand(MoD)services.These imbalances occur due to uneven stochastic travel demand and can be mitigated by proactively rebalancing empty vehic...This paper considers the problem of supply-demand imbalances in Mobility-on-Demand(MoD)services.These imbalances occur due to uneven stochastic travel demand and can be mitigated by proactively rebalancing empty vehicles to areas where the demand is high.To achieve this,we propose a method that takes into account uncertainties of predicted travel demand while minimizing pick-up time and rebalance mileage for autonomous MoD ride-hailing.More precisely,first travel demand is predicted using Gaussian Process Regression(GPR)which provides uncertainty bounds on the prediction.We then formulate a stochastic model predictive control(MPC)for the autonomous ride-hailing service and integrate the demand predictions with uncertainty bounds.In order to guarantee constraint satisfaction in the optimization under estimated stochastic demand prediction,we employ a probabilistic constraining method with user-defined confidence interval,using Chance Constrained MPC(CCMPC).The benefits of the proposed method are twofold.First,travel demand uncertainty prediction from data can naturally be embedded into the MoD optimization framework,allowing us to keep the imbalance at each station below a certain threshold with a user-defined probability.Second,CCMPC can be relaxed into a Mixed-Integer-Linear-Program(MILP)and the MILP can be solved as a corresponding Linear-Program,which always admits an integral solution.Our transportation simulations show that by tuning the confidence bound on the chance constraint,close to optimal oracle performance can be achieved,with a median customer wait time reduction of 4%compared to using only the mean prediction of the GPR.展开更多
This paper examines whether customer base composition in the US,that is,whether a firm's major customers are government entities or publicly traded companies,affects the properties of its management earnings forec...This paper examines whether customer base composition in the US,that is,whether a firm's major customers are government entities or publicly traded companies,affects the properties of its management earnings forecasts(MEFs).Using a sample of 1,168 MEFs from 1998 to 2014,we find that firms whose major customers are government entities(i.e.,govemment suppliers)issue more precise and more accurate MEFs than firms whose major customers are public companies(i.e.,corporate suppliers).Moreover,when managers disclose negative information to the market,earnings forecasts issued by government suppliers have greater price impact than those issued by corporate suppliers.Collectively,our empirical results suggest that having major govemment customers has a positive impact on the quality of MEFs.展开更多
基金The National High Technology Research and Development Program of China (863 Program) (No.2006AA11Z209)Youth Science and Technology Foundation of China University of Mining and Technology (No.2007A028)Natural Science Foundation of Beijing (No.9073018)
文摘A new travel time reliability-based traffic assignment model is proposed to investigate the effects of an advanced transportation information system (ATIS) on drivers' risk-taking path choice behaviours in transportation networks with demand uncertainty. In the model, drivers are divided into two classes. The first class is not equipped with ATIS, while the second class is equipped with ATIS. Different risk-taking path choice behaviours of the two classes are studied, respectively. A corresponding mixed equilibrium traffic assignment model is formulated as a variational inequality problem in terms of path flows, which is solved by a heuristic solution algorithm. Numerical results indicate that the ATIS can influence the drivers' risk-taking path choice behaviours and the total system travel time in transportation networks with demand uncertainty. It is also found that under higher demand levels, the benefits of ATIS for network performance enhancement may be more obvious.
基金This research has been supported by Discovery Grants from the Natural Sciences and Engineering Research Council of Canada(grant#:RGPIN-2015-04013,RGPIN-2022-03514).
文摘Due to its harmful nature,any incident associated with hazardous material(hazmat)may cause tremendous impacts on the surrounding people and the environment.Focusing on the incident involving this specific type of good,we develop a reliable and robust emergency logistics network that considers both demand uncertainty and possible unavailability of particular links.A time-based risk measure is carefully designed upon the traditional risk assessment to reflect the stakeholder’s sensitivity to risk over response time.The disruption and uncertainty are modeled as two sets of scenarios which are integrated into a bi-objective robust model to evaluate the trade-offs between risk and cost.The effectiveness of the emergency response can be assured by expenditures that add extra capacities to certain links or establish additional facilities that aid recovery from incidents.We apply our model and approach to a real-world network in Guangdong China.Analytical results reveal the necessity of embedding consideration of uncertainty and unreliability into emergency network design problems;outline the importance of hedging against unpredictability by system redundancies;and indicate the impact of stakeholder’s orientation towards cost and risk on the location,allocation,and routing decisions in hazmat emergency response.
文摘Agriculture is a key facilitator of economic prosperity and nourishes the huge global population.To achieve sustainable agriculture,several factors should be considered,such as increasing nutrient and water efficiency and/or improving soil health and quality.Using fertilizer is one of the fastest and easiest ways to improve the quality of nutrients inland and increase the effectiveness of crop yields.Fertilizer supplies most of the necessary nutrients for plants,and it is estimated that at least 30%-50%of crop yields is attributable to commercial fertilizer nutrient inputs.Fertilizer is always a major concern in achieving sustainable and efficient agriculture.Applying reasonable and customized fertilizerswill require a significant increase in the number of formulae,involving increasing costs and the accurate forecasting of the right time to apply the suitable formulae.An alternative solution is given by two-stage production planning under stochastic demand,which divides a planning schedule into two stages.The primary stage has non-existing demand information,the inputs of which are the proportion of raw materials needed for producing fertilizer products,the cost for purchasing materials,and the production cost.The total quantity of purchased material and produced products to be used in the blending process must be defined to meet as small as possible a paid cost.At the second stage,demand appears under multiple scenarios and their respective possibilities.This stage will provide a solution for each occurring scenario to achieve the best profit.The two-stage approach is presented in this paper,the mathematical model of which is based on linear integer programming.Considering the diversity of fertilizer types,themathematicalmodel can advise manufacturers about which products will generate as much as profit as possible.Specifically,two objectives are taken into account.First,the paper’s thesis focuses on minimizing overall system costs,e.g.,including inventory cost,purchasing cost,unit cost,and ordering cost at Stage 1.Second,the thesis pays attention tomaximizing total profit based on information from customer demand,as well as being informed regarding concerns about system cost at Stage 2.
基金This research was funded by the National Natural Science Foundation of China(NSFC)[Grant number 71671048,71901075]National Social Science Fund of China(NSSFC)Research of Public Choice Based on Arrow Axiom System and Arrow Impossibility Theorem[Grant number 17BJL025]+2 种基金Science Foundation of Ministry of Education of China(SFMEC):Research on The Influence Mechanism Of Social Trust Based on Multi-Modal Relationship of Sharing Economy[19YJCZH278]the Co-Construction Project of Philosophy and Social Science Planning Discipline in Guangdong Province[GD18XGL37]Innovative Talents Project of general universities in Guangdong Province[2018WQNCX146].
文摘Consider an optimal procurement strategy for fresh produce,which is a type of perishable product.Due to the different quality provided by each manufacturer,the fresh produce qualification rates are dissimilar.Simultaneously,consumers demand is influenced by the freshness and price of products,as a result,the demand in the market is not fixed.In this scenario,how a particular retailer should develop an optimal procurement strategy will be a core issue in supply chain management.In order to address the above questions,the Bayesian approach is adopted to explore retailer optimal procurement strategies with uncertainty about product supply and market demand.Finally,simulation data are used to analyse the results of the proposed model and expected non-random model to illustrate the validity and feasibility of the proposed model.
基金co-funded by Vinnova,Sweden through the project:Simulation,analysis and modeling of future efficient traffic systems.
文摘This paper considers the problem of supply-demand imbalances in Mobility-on-Demand(MoD)services.These imbalances occur due to uneven stochastic travel demand and can be mitigated by proactively rebalancing empty vehicles to areas where the demand is high.To achieve this,we propose a method that takes into account uncertainties of predicted travel demand while minimizing pick-up time and rebalance mileage for autonomous MoD ride-hailing.More precisely,first travel demand is predicted using Gaussian Process Regression(GPR)which provides uncertainty bounds on the prediction.We then formulate a stochastic model predictive control(MPC)for the autonomous ride-hailing service and integrate the demand predictions with uncertainty bounds.In order to guarantee constraint satisfaction in the optimization under estimated stochastic demand prediction,we employ a probabilistic constraining method with user-defined confidence interval,using Chance Constrained MPC(CCMPC).The benefits of the proposed method are twofold.First,travel demand uncertainty prediction from data can naturally be embedded into the MoD optimization framework,allowing us to keep the imbalance at each station below a certain threshold with a user-defined probability.Second,CCMPC can be relaxed into a Mixed-Integer-Linear-Program(MILP)and the MILP can be solved as a corresponding Linear-Program,which always admits an integral solution.Our transportation simulations show that by tuning the confidence bound on the chance constraint,close to optimal oracle performance can be achieved,with a median customer wait time reduction of 4%compared to using only the mean prediction of the GPR.
基金the financial support for the research project No.15507217 by the General Research Fund from the Research Grants Council of the Hong Kong Special Administrative Region Government.
文摘This paper examines whether customer base composition in the US,that is,whether a firm's major customers are government entities or publicly traded companies,affects the properties of its management earnings forecasts(MEFs).Using a sample of 1,168 MEFs from 1998 to 2014,we find that firms whose major customers are government entities(i.e.,govemment suppliers)issue more precise and more accurate MEFs than firms whose major customers are public companies(i.e.,corporate suppliers).Moreover,when managers disclose negative information to the market,earnings forecasts issued by government suppliers have greater price impact than those issued by corporate suppliers.Collectively,our empirical results suggest that having major govemment customers has a positive impact on the quality of MEFs.