2015 Informs Annual Meeting

WC68

INFORMS Philadelphia – 2015

WC66 66-Room 113C, CC Airline Operations Sponsor: Aviation Applications Sponsored Session Chair: Cheng-lung Wu, Senior Lecturer, UNSW Australia, School of Aviation, UNSW Australia, Kensington, NS, 2052, Australia, c.l.wu@unsw.edu.au 1 - Enhanced Delay Propagation Tree Model with Bayesian Network for Modelling Flight Delay Propagation Cheng-lung Wu, Senior Lecturer, UNSW Australia, School of Aviation, UNSW Australia, Kensington, NS, 2052, Australia, c.l.wu@unsw.edu.au, Weiwei Wu This paper developed an enhanced Delay Propagation Tree model with Bayesian Network (DPT-BN) to model delay propagation and interdependencies between flights. Results showed that flights have non-homogeneous delay propagation with non-IID delay profiles. The DPT-BN model was used to infer posterior delay profiles with different delay scenarios. We also demonstrated how robust airline scheduling methodologies can benefit from this probability-based delay model. 2 - Integer Programming Based Pairing Generation with Deviation Penalty in Airline Crew Recovery Hyunsuk Lee, Ph.d Student, McCombs School of Business, The University of Texas at Austin, 2110 Speedway Stop B6500, CBA 5.202, Austin, TX, 78712, United States of America, hyunsuk.lee@utexas.edu, Douglas Fearing In this paper, we will explore how to model airline scheduling problems (flight/crew scheduling and recovery), and apply IP based pairing generation technique to crew recovery with deviation penalty. In particular, we will control IP based pairing generator by restricting the number of different flights in new pairing. Such a study should hopefully give insights to minimizing deviations from original schedule. 3 - U.S. Commercial Aviation Demand Forecasting with a Panel Data: The Role of Individual Heterogeneity Mei Liu, Economist, FAA, 6712 Tildenwood Lane, Rockville, MD, 20852, United States of America, chia-mei.liu@faa.gov, Dipasis Bhadra Studies in aviation demand forecasting have long relied on time series approaches, ignoring the individual heterogeneity in a panel data. Heterogeneity is most evident in the U.S. aviation sector where network effect is prevalent. This paper identifies the route-specific effects from 2000 through 2010 and takes it forward to perform a 4-year-ahead forecasting. We evaluate whether the inclusion of individual heterogeneity reduces forecast errors. Chair: Choungryeol Lee, Purdue University, United States of America, lee1210@purdue.edu 1 - Downside Risk Analysis for Planning Intermodal Facility Investments Irina Benedyk, United States of America, birina@purdue.edu, Hong Zheng, Yuntao Guo, Srinivas Peeta, Ananth Iyer We apply Down Side Risk analysis to plan intermodal facility investment decisions. The model accounts for factors such as the future global commodity flow changes and demand uncertainty. Experiment results show that tighter downside risk constraints lead to the inland intermodal facilities being preferred for investment compared to the ports. They also suggest that low downside risk values increase the total cost but reduce its variation. 2 - Procuring and Transporting Commodities: Hedging against Price, Demand and Freight Rate Risk with Options Arun Chockalingam, Assistant Professor, Eindhoven University of Technology, Den Dolech 2, Eindhoven, 5612AZ, Netherlands, A.Chockalingam@tue.nl, Taimaz Soltani, Jan Fransoo We consider a firm that procures and transports a commodity via ocean freight to its production plant where the commodity is converted to a final product to meet customer demand. Transportation of commodities via ocean freight has increased significantly in recent years leading to increasing volatility in the cost of freight transportation. We study how a firm can reduce its procurement and transportation costs using options on procuring the commodity and freight space in a newsvendor setting. WC67 67-Room 201A, CC Risk in Freight Transport and Logistics Sponsor: TSL/Freight Transportation & Logistics Sponsored Session

3 - Risk Management Strategies in Transportation Capacity Decisions: An Analytical Approach Jiho Yoon, Michigan State University, N468 North Business Complex, Michigan State University, East Lansing, MI, 48824- 1121, United States of America, yoon@broad.msu.edu, Hakan Yildiz, Sri Talluri In recent years, access to freight transportation capacity has become a constant issue in the minds of logistics managers due to capacity shortages. In a buyer- seller relationship, reliable, timely, and cost-effective access to transportation is critical to the success of such partnerships. Given this, guaranteed capacity contracts with 3PLs may be appealing to shippers to increase their access to capacity and respond effectively to customer requirements. With this new opportunity, 3PLs must focus on approaches that can assist them in analyzing their options as they promise guaranteed capacity to shippers when faced with uncertain demand and related risks in transportation. In this paper, we analytically analyze three capacity-based risk mitigation strategies and the mixed use of these individual strategies using industry based data to provide insights on which strategy is preferable to the 3PL and under what conditions. We posit that the selection of a strategy is contingent on several conditions faced by both the shipper and the carrier. Although our approach is analytical in nature, it has a high degree of practical utility in that a 3PL can utilize our decision models to effectively analyze and visualize the trade-offs between the different strategies by considering appropriate cost and demand data. 4 - Freight Option-Based Mechanism for Multiple Carrier Collaborative Less-Than-Truckload Logistics Choungryeol Lee, Purdue University, United States of America, lee1210@purdue.edu, Srinivas Peeta We propose option-based mechanisms for LTL carrier-to-carrier collaboration to alleviate operational and financial risks resulting from stochastic demand in the operational horizon. It aims to enhance the utilization of fleet capacity and reduce the induced costs of handling demand variability. Numerical experiments illustrate the feasibility and provide useful insights of implementing option-based multiple carrier collaborative LTL logistics. Chair: Indrajit Chatterjee, University of Minnesota, Twin Cities, 500 Pillsbury Drive SE, Minneapolis, mn, 55455, United States of America, chat0123@umn.edu 1 - Computationally Efficient Algorithms for the Calibration of High- resolution Stochastic Traffic Simulators Chao Zhang, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, United States of America,chaoz@mit.edu, Carolina Osorio, Gunnar Flütterüd This work formulates the calibration problem as a simulation-based optimization (SO) problem which is addressed by the metamodel approach. The metamodel combines information from both the simulator and an analytical traffic model that relates the calibration parameters to the simulation-based objective function. The performance of the proposed approach has been tested on a toy network and is currently being evaluated on a large-scale metropolitan network in Berlin, Germany. 2 - Decentralized Traffic Assignment for Multi-level Modeling Ehsan Jafari, University of Texas at Austin, Austin, TX, United States of America, ejafari@utexas.edu, Stephen Boyles Statewide planning model is used for planning projects that will have implications on transportation across the entire state. At the same time, medium-sized cities have their own planning model. The process of updating these models in a way that maintains consistency between them is laborious and time-consuming. In this research, a decentralized bi-level modeling approach, based on the concept of network contraction, is proposed to address these issues. 3 - A Simulation-based Optimization Algorithm for Traffic Responsive Control Linsen Chong, Massachusetts Institute of Technology, Cambridge, MA, United States of America, linsenc@mit.edu, Carolina Osorio We propose a simulation-based optimization (SO) framework to address generally constrained urban traffic responsive control problems. We develop a tractable dynamic traffic model that is inspired from traffic flow theory, transient queueing theory and is parameterized by time-dependent sensor data. We illustrate the performance of the proposed method through a large-scale urban traffic case study. WC68 68-Room 201B, CC Traffic Control Sponsor: Transportation, Science and Logistics Sponsored Session

449