Changhyun Kwon

Multi-period planning for electric car charging station locations: A case of Korean Expressways

One of the most critical barriers to widespread adoption of electric cars is the lack of charging station infrastructure. Although it is expected that a sufficient number of charging stations will be constructed eventually, due to various practical reasons they may have to be introduced gradually over time. In this paper, we formulate a multi-period optimization model based on a flow-refueling location model for strategic charging station location planning. We also propose two myopic methods and develop a case study based on the real traffic flow data of the Korean Expressway network in 2011. We discuss the performance of the three proposed methods.

Value-at-Risk model for hazardous material transportation

This paper introduces a Value-at-Risk (VaR) model to generate route choices for a hazmat shipment based on a specified risk confidence level. VaR is a threshold value such that the probability of the loss exceeding the VaR value is less than a given probability level. The objective is to determine a route which minimizes the likelihood that the risk will be greater than a set threshold. Several properties of the VaR model are established. An exact solution procedure is proposed and tested to solve the single-trip problem. To test the applicability of the approach, routes obtained from the VaR model are compared with those obtained from other hazmat objectives, on a numerical example as well as a hazmat routing scenario derived from the Albany district of New York State. Depending on the choice of the confidence level, the VaR model gives different paths from which we conclude that the route choice is a function of the level of risk tolerance of the decision-maker. Further refinements of the VaR model are also discussed.

Generalized route planning model for hazardous material transportation with VaR and equity considerations

Recently, the Value-at-Risk (VaR) framework was introduced for the routing problem of a single hazmat trip. In this paper, we extend the VaR framework in two important ways. First, we show how to apply the VaR concept to a more realistic multi-trip multi-hazmat type framework, which determines routes that minimize the global VaR value while satisfying equity constraints. Second, we show how to embed the algorithm for the single hazmat trip problem into a Lagrangian relaxation framework to obtain an efficient solution method for this general case. We test our computational experience based on a real-life hazmat routing scenario in the Albany district of New York State. Our results indicate that one can achieve a high degree of risk dispersion while controlling the VaR value within the desired confidence level.

Online advertisement service pricing and an option contract

For the Internet advertisement market, we consider a contract problem between advertisers and publishers. Among several ways of pricing online advertisements, the methods based on cost-per-impression (CPM) and cost-per-click (CPC) are the two most popular. The CPC fee is proportional to the click-through rate (CTR), which is uncertain and makes decisions of advertisers and publishers difficult. In this paper, we suggest a hybrid pricing scheme: advertisers pay the minimum of CPM and CPC fees by purchasing an option from publishers. To determine the option price, we consider a Nash bargaining game for negotiation between an advertiser and a publisher and provide the solution. Further, we show that such option contracts will help the advertiser avoid high cost and the publisher generate more revenue. The option contract will also improve the contract feasibility, compared to CPM and CPC.

Non-cooperative competition among revenue maximizing service providers with demand learning

This paper recognizes that in many decision environments in which revenue optimization is attempted, an actual demand curve and its parameters are generally unobservable. Herein, we describe the dynamics of demand as a continuous time differential equation based on an evolutionary game theory perspective. We then observe realized sales data to obtain estimates of parameters that govern the evolution of demand; these are refined on a discrete time scale. The resulting model takes the form of a differential variational inequality. We present an algorithm based on a gap function for the differential variational inequality and report its numerical performance for an example revenue optimization problem.

Demand Learning and Dynamic Pricing under Competition in a State-Space Framework

In this paper, we propose a revenue optimization framework integrating demand learning and dynamic pricing for firms in monopoly or oligopoly markets. We introduce a state-space model for this revenue management problem, which incorporates game-theoretic demand dynamics and nonparametric techniques for estimating the evolution of underlying state variables. Under this framework, stringent model assumptions are removed. We develop a new demand learning algorithm using Markov chain Monte Carlo methods to estimate model parameters, unobserved state variables, and functional coefficients in the nonparametric part. Based on these estimates, future price sensitivities can be predicted, and the optimal pricing policy for the next planning period is obtained. To test the performance of demand learning strategies, we solve a monopoly firms revenue maximizing problem in simulation studies. We then extend this paradigm to dynamic competition, where the problem is formulated as a differential variational inequality. Numerical examples show that our demand learning algorithm is efficient and robust.

Value-at-Risk and Conditional Value-at-Risk Minimization for Hazardous Materials Routing

This chapter provides fundamentals of value-at-risk and conditional value-at-risk models applied to routing problems in hazardous materials transportation.

Analytical Dynamic Traffic Assignment Model

The rapid development of intelligent transportation system technologies and the policy emphasis on their deployment have increased the importance of predictive dynamic network flow models, especially so-called dynamic network loading and dynamic traffic assignment models. In this chapter we provide a critical review of analytic models used in predicting time-varying urban network flows. Specifically, we examine and compare four types of dynamics used as the foundation of dynamic network models:

Worst-Case Conditional Value-at-Risk Minimization for Hazardous Materials Transportation

Despite significant advances in risk management, the routing of hazardous materials (hazmat) has relied on relatively simplistic methods. In this paper, we apply an advanced risk measure, called conditional value-at-risk (CVaR), for routing hazmat trucks. CVaR offers a flexible, risk-averse, and computationally tractable routing method that is appropriate for hazmat accident mitigation strategies. The two important data types in hazmat transportation are accident probabilities and accident consequences, both of which are subject to many ambiguous factors. In addition, historical data are usually insufficient to construct a probability distribution of accident probabilities and consequences. This motivates our development of a new robust optimization approach for considering the worst-case CVaR (WCVaR) under data uncertainty. We study important axioms to ensure that both the CVaR and WCVaR risk measures are coherent and appropriate in the context of hazmat transportation. We also devise a computational met...

Conditional value-at-risk model for hazardous materials transportation

This paper investigates how the conditional value-at-risk (CVaR) can be used to mitigate risk in hazardous materials (hazmat) transportation. Routing hazmat must consider accident probabilities and accident consequences that depend on the hazmat types and route choices. This paper proposes a new method for mitigating risk based on CVaR measure. While the CVaR model is popularly used in financial portfolio optimization problems, its application in hazmat transportation is new. A computational method for determining the optimal CVaR route is proposed and illustrated by a case study in the road network surrounding Albany, NY.