Informs Annual Meeting 2017

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INFORMS Houston – 2017

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4 - CPV vs CPC in Internet Video Advertising Zhen Sun, Information Systems, The George Washington University, Washington, DC, 20052, United States, zhens@email.gwu.edu, Sameer Mehta, Vijay S. Mookerjee This study is aim to conduct an economical analysis on the two pricing models in Internet video advertising.

361F Advanced Vehicle Control and Traffic Operations with Connected Vehicles Contributed Session Chair: Lili Du, Illinois Institute of Technology, 3201 South Dearborn Street, Chicago, IL, 60616, United States, ldu3@iit.edu 1 - Facilitating the Emergency Response Vehicles’ Movement Through a Transportation Network Link in the Connected Vehicle Environment Gaby Joe Hannoun, Graduate Student, Virginia Polytechnic Institute and State University, 7054 Haycock Rd, Falls Church, VA, 22043, United States, gabyjoe@vt.edu, Pamela Murray-Tuite, Kevin Heaslip Due to the increased traffic especially in urban areas and the risky maneuvers that Emergency Response Vehicles (ERVs) are forced to perform, a new mathematical program leveraging the vehicle to vehicle communications is proposed to facilitate the movement of the ERVs. The integer linear program finds the fastest passage for the ERV along a transportation link while avoiding conflict with nearby vehicles. It can be adapted to different road types, ERV characteristics, and surrounding conditions. When compared to current practices, the proposed formulation offers the same or faster ERV passage along with increased safety since it limits confusion and unexpected incidents caused by human factors. 2 - Multi Vehicle Coordination Strategy for Vehicles Approaching Signalized Intersection to Reduce Emissions using Connected Vehicles Technology Vaibhav Rungta, Graduate Assistant, Rochester Institute of Technology, Rochester Institute of Technology, James E. Gleason Building, 81 Lomb Memorial Drive, Rochester, NY, 14623, United States, vr2002@g.rit.edu, Katie McConky The connected vehicles technology allows vehicles and traffic infrastructure to exchange information. This information can be used to provide vehicles with optimal speed profiles which reduce emissions and fuel consumption. However for vehicles with different characteristics a single speed may not be optimal for all the vehicles while maintaining the traffic flow. This research aims to generate speed profiles which may be sub-optimal for a single vehicle, but result in lower emissions and fuel consumption at the system level for a group of vehicles. 3 - Non-Connected Vehicle Detection using Connected Vehicles Yong Hoon Kim, Purdue University, 2335 Hokins Dr., West Lafayette, IN, 47907, United States, kim523@purdue.edu, Lin Liu, Srinivas Peeta This study proposes a hidden Markov model, which is a probabilistic inference approach, to identify non-connected vehicle location/trajectory using connected vehicle trajectories under mixed traffic streams of connected vehicles. This methodology is integrated with a cooperative-situation awareness framework. The proposed model is analyzed using real-world vehicle trajectory data to aid the situational awareness of connected vehicles under low market penetration rates. 4 - Constrained Model Predictive Control and Distributed Computation based Car-Following Control for a Platoon mixed with CAVs and Human-Drive Vehicles Siyuan Gong, Illinois Institute of Technology, Chicago, IL, 60616, United States, sgong1@hawk.iit.edu, Lili Du The study developed a car-following control mechanism for a platoon mixed with human-drive vehicles and CAVs on a straight highway. Specifically, the movement of human-drive vehicles is modeled by the Newell car-following model and the aggregated response delay anticipated by a machine learning algorithm. By utilizing the connectivity between CAVs, a constrained MPC combined a distributed computation approach is developed to generate optimal control law for individual CAVs so that the desired traffic smoothness and stability are ensured in the system level. Last, the performance of our approaches is validated by simulation experiments.

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361E Network Design Sponsored: TSL, Facility Logistics Sponsored Session Chair: Mohammad Marufuzzaman, Mississippi State University, Dept of Industrial & Systems Engineering, P.O. Box 9542, Starkville, MS, 39762, United States, maruf237@gmail.com 1 - Optimizing Electric Vehicle Charging Station Expansion Decisions with an Integration of Renewable Energy and Vehicle-to-grid Sources MD Abdul Quddus, Mississippi State University, 319 North Jackson Street, Apt 3E, Starkville, MS, 39759, United States, mq90@msstate.edu, Mohannad Kabli, Mohammad Marufuzzaman This study proposes a novel formulation for designing and managing electric vehicle charging stations, considering both long-term planning decisions and short-term hourly operational decisions over a pre-specified planning horizon and under stochastic power demand. We propose a highly customized hybrid algorithm that combines Sample Average Approximation (SAA) with an enhanced Progressive Hedging Algorithm (PHA). A case study based on a road network around Washington, D.C. is present to visualize and validate the modeling results. Our computational results provide a number of managerial insights to the decision makers while indicating the robustness of the proposed algorithm. 2 - The Location Decision Revisited: Empirical Findings from the Automotive Sector Ioannis Siskos, Kuhne Logistics University, Grosser Grasbrook 17, Hamburg, 20457, Germany, ioannis.siskos@the-klu.org Ioannis Siskos, Vlerick Business School, Reep 1, Gent, 9000, Belgium, ioannis.siskos@the-klu.org, Ann Vereecke, Luk N. Van Wassenhove, Matthias Holweg In this paper we empirically test location factors in the automotive context, using de facto location decisions car firms have taken, i.e Vehicle Assembly Plant openings and closures. The main research question here is whether the location factors identified in the literature have the same effect in both openings and closures of Vehicle Assembly Plants. 3 - Optimal Planning of Swapping Station Network with Customer Satisfaction Fang Guo, Huazhong University of Science and Technology, Hongshan District, 1037 Luoyu Road, WuHan, 430074, China, fang_guo@hust.edu.cn, Jun Yang Key to the mass adoption of electric vehicles is the establishment of a sufficient battery service infrastructure network on the basis of customer behavior and psychology. Motivated by EV service infrastructure network design under the battery leasing/electric car sharing service business models,we present an electric vehicle battery service network design problem considering a customer’s satisfaction related to “range anxiety” and “loss anxiety”. The problem is formulated as a linear integer programming model under deterministic and fuzzy scenarios. A Tabu Search heuristic is proposed to solve the problem. Finally, we conduct parametric analysis on real-world road networks. 4 - Strategic and Tactical Planning in Dimension Stone Industry Gangaraju Vanteddu, Associate Professor, Southeast Missouri State University, Harrison College of Business, One University Plaza, MS.5815, Cape Girardeau, MO, 63701, United States, gvanteddu@semo.edu Supply Chain network design and tactical planning in dimension stone industry present certain unique challenges, in the presence of highly variable demand and material availability related constraints. In this research, generic linear programming based models are proposed for the strategic and tactical level planning to optimize the cost of developing a supply chain network and for medium term production planning.

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