Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

MA30

3 - Modeling On-demand Mobility Service: Competition, Surge Pricing, and Subsidy Xinwu Qian, Purdue University, 3326 Putnam Street, West Lafayette, IN, 47906, United States, Satish Ukkusuri The study presents the mathematical model for the competition among stakeholders, riders, and drivers in the market of on-demand mobility service. We consider that the stakeholders propose the surge pricing and subisdy policies to maximize their revenue, riders decide whether or not make the trip based on the perceived travel cost, and the drivers decide whether they will leave/enter the market and the location to pick up riders based on their perceived utility. Our results indicate that market efficiency depends on how drivers value their lost for leaving the market. We also observe that subsidizing occupied and vacant trips are important as a regulation to drivers’ selfish behavior. 4 - Modeling Competition of Intermodal Infrastructure Investors Factoring Their Heterogeneity and Maritime Carrier Behavior Irina Benedyk, Purdue University, 2411 Neil Armstrong Drrive, # 2A, West Lafayette, IN, 47906, United States, Srinivas Peeta This study develops a game theoretical framework that allows one-to-many relationship between investors and intermodal ports, and factors impacts of maritime carrier competing behavior, and investor heterogeneity (defined as a number of intermodal ports under their control) on the investment decision- making process. A solution algorithm is developed to identify and analyze the Nash equilibrium and demonstrate its applicability using the case study of the Northeast U.S. intermodal ports. The study findings can be used by the public sector to evaluate impacts of policies and incentives on intermodal infrastructure development. n MA30 North Bldg 221C Emerging Urban Facilities and Logistics Sponsored: TSL/Facility Logistics Sponsored Session Chair: Wei Qi, McGill University, McGill University, Montreal, QC, H3A 1G5, Canada Co-Chair: Guangrui Ma, Tianjin University, THong Kong, 300072, China 1 - Congestion Reduction through Efficient Empty Container Movement Santiago Carvajal, University of Southern California, Los Angeles, CA, 90007, United States My presentation will revisit the Empty Container Problem using double container trucks. This research was started by a METRANS project with the Ports of Los Angeles and Long Beach in an attempt to reduce congestion at the ports. The problem is divide into two sub-problems, a container assignment problem and a vehicle routing problem. In this presentation I will present my methodology for solving the problem and my mathematical models, as well as presenting some general results as well as results from the Ports of Los Angeles and Long Beach. 2 - Dynamic Electrical Vehicle Sharing System Design Xin Wang, University of Wisconsin-Madison, 8916 Red Beryl Drive, Middleton, WI, 53562-4278, United States, Yikang Hua, Dongfang Zhao, Xiaopeng Li Electrical vehicle (EV) sharing advances the green city development, however it faces various challenges in practical operations such as infrastructure deployment and vehicle relocation. We build a multi-stage stochastic model aiming to reduce cost and customer queueing for EV sharing system. We provide an algorithm which combines Lagrangian relaxation and Benders Decomposition to solve the model. Our model helps design EV sharing infrastructure under uncertain demand arrival together with the daily dispatch and relocation decisions. 3 - Inventory Repositioning in On-demand Product Rental Networks Saif Benjaafar, University of Minnesota, 111 Church Street SE, Department of Industrial and Systems Engr, Minneapolis, MN, 55419, United States, Xiaobo Li We consider a product rental network with a fixed number of rental units distributed across multiple locations. Customers can pick a unit at one location and return it to another. Both demand for rentals and rental durations are random. To improve the matching of supply and demand, units are periodically repositioned. We formulate the inventory repositioning problem as Markov decision process and characterize the optimal policy. We also describe an efficient heuristic.

4 - Shared Autonomous Electric Vehicles for Strengthening Future Urban Microgrids Wei Qi, McGill University, 1001 Sherbrooke Street West, Montreal, QC, H3A 1G5, Canada, Mengyi Sha, Shanling Li, Hong Chi We envision the prospect where shared autonomous electric vehicles (SAEVs) will reinforce future urban electricity infrastructure in the form of solar-powered microgrids. We integrate cross-disciplinary modelling of transport and power systems with optimization to investigate the potential of SAEVs for improving the self-sufficiency and resilience of urban microgrids. Our model prescribes optimal citywide SAEV fleet operations of ride-sharing, repositioning, charging and discharging amidst various urban heterogeneities. Our findings highlight the value of centralized dispachability of SAEVs. n MA31 North Bldg 222A Shared Mobility Optimization for Public Transportation Sponsored: TSL/Intelligent Transportation Systems (ITS) Sponsored Session Chair: Jiangtao Liu, Arizona State University, Tempe, AZ, 85282, United States Co-Chair: Xuesong Zhou, Arizona State University, Tempe, AZ, 85281, United States 1 - Designing Dynamic and Personalized Incentives in Sustainable Ridesharing Systems Yanshuo Sun, Florida State University, Tallahassee, FL, 20742, United States This study considers the option of influencing ridesharing participants’ original travel schedules by applying monetary incentives. In this system, drivers can opt for an incentive program where a driver specifies how much she/he expects to be compensated if the earliest departure time at the origin is shifted to be earlier than the originally scheduled time. The level of behavioral realism is thus improved by considering the possibility of drivers’ misrepresenting their required compensations to modify their travel schedules. The contribution of this study is to propose a mechanism which ensures only “efficient drivers are eligible for incentives given an incentive budget limit. 2 - Accessibility with Time and Resource Constraints Monireh Mahmoudi, Assistant Professor, Michigan State University, East Lansing, MI, 48824, United States, Ying Song, Harvey Miller, Xuesong Zhou A common accessibility measure in transportation science is the space-time prisms (STPs) and the network-time prisms (NTPs). STPs and NTPs focus on time as the scarce resource limiting accessibility. However, other resource constraints can constrain space-time accessibility, such as limits or “budgets for energy, emissions, or monetary expenses. This research extends NTPs to include other resource constraints in addition to time. We conceptualize resource hyper-prisms (RHPs) as a constrained optimization problem and develop a resource-dependent time- dependent forward and backward dynamic programming to determine the boundaries of a RHP given time and other resource budgets. 3 - Integrated Train Timetabling and Locomotive Assignment Zhou Xu, The Hong Kong Polytechnic University, Hong Kong, China, Xiaoming Xu, Chung-Lun Li This work focuses on modeling and solving an integrated train timetabling and locomotive assignment problem. To solve this integrated problem, we first construct a three-dimensional state-space-time network in which a state is used to indicate which train a locomotive is serving. We then formulate the problem as a minimum cost multi-commodity network flow problem with incompatible arcs and integer flow restrictions, and present a Lagrangian relaxation heuristic for solving problem. 4 - Dynamic Dial-a-ride and Pricing with Look Ahead for Competitive On-demand Mobility Systems Hamid Sayarshad, Cornell University, Ithaca, NY, 14850, United States, H. Oliver Gao We propose a competitive on-demand mobility model using a multi-server queue system under infinite-horizon look-ahead. The proposed approach includes a novel dynamic optimization algorithm which employs a Markov decision process (MDP) and provides opportunities to revolutionize conventional transit services that are plagued by high cost, low ridership, and general inefficiency, particularly in disadvantaged communities and low-income areas. We develop a dynamic pricing scheme that utilizes a balking rule that incorporates socially efficient level and the revenue-maximizing price, and an equilibrium-joining threshold obtained by imposing a toll on the customers who join the system.

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