2016 INFORMS Annual Meeting Program

TB61

INFORMS Nashville – 2016

2 - Fast Computation Techniques For The Stochastic On-time Arrival Problem Samitha Samaranayake, Cornell University, samitha@cornell.edu We present a new technique for solving the path-based stochastic on-time arrival (SOTA) problem. Our approach uses the solution to the policy-based SOTA problem - which is of pseudo-polynomial-time complexity in the time budget of the journey - as an efficient search heuristic for the optimal path. We also demonstrate how path preprocessing techniques can be used for further speedups. To the best of our knowledge, these techniques provide the most efficient computation strategy for the path-based SOTA problem for general probability distributions, both with and without preprocessing. 3 - Urban Freight Microsimulation: Evaluating Freight Parking Behavior In New York City Trilce Marie Encarnacion, Rensselaer Polytechnic Institute, encart@rpi.edu, Jose Holguin-Veras, Johanna Amaya As urban centers continue having increased demand for consumer goods and services, the amount of freight trafficc and the associated negative externalities increase. Previous studies have shown that carriers have to either cruise until they find a parking space or illegally park in order to make their deliveries. Using data collected from in-depth interviews of carriers as well as current traffic conditions, a discrete simulation framework was developed to replicate the parking behavior of trucks making deliveries to a case study area in Midtown Manhattan. The goal is to provide insight into optimal freight parking policies to Ling Zhang, North Carolina State University, Raleigh, NC, United States, lzhang42@ncsu.edu, Yunan Liu, Yang Liu, Shuangchi He Balancing supply and demand across different areas is a critical issue in one-way car sharing networks. We study dynamic pricing in order to maximize the profit of a car sharing network. Since the stochastic network model is analytically intractable, we propose a fluid approximation to represent the supply and demand of vehicles. In contrast to conventional transportation fluid models that assume deterministic processing times, general rental time distributions are built into our fluid model. Moreover, our model allows for time-varying demand rates and rental time distributions. Under this formulation, dynamic pricing is reduced to a convex optimization problem that is efficiently solvable. TB61 Cumberland 3- Omni Online Delivery Routing Sponsored: TSL, Freight Transportation & Logistics Sponsored Session Chair: Jan Fabian Ehmke, Freie Universität Berlin, Garystr. 21, Berlin, 14195, Germany, janfabian.ehmke@fu-berlin.de 1 - Taking Advantage Of In-store Customers To Deliver Online Orders Same-day delivery of online orders is becoming an indispensable service for large retailers. We explore a novel environment in which in-store customers may take over the task of delivering online orders on their way back home. Additionally, a fleet of company-employed drivers is available to cover any unserved online orders. This context represents a highly dynamic and stochastic environment for which we explore and compare two rolling horizon approaches: one that ignores any information about future arrivals of online orders and in-store customers, and one that incorporates such information by means of sampled scenarios. Our results demonstrate the superiority of scenario-based planning. 2 - An Online Cost Allocation Model For Horizontal Supply Chains Han Zou, University of Southern California, Los Angeles, CA, United States, hanzou@usc.edu, Maged M Dessouky, John Gunnar Carlsson This research addresses the cost allocation problem in a real-time cost sharing transportation system, which results from horizontal cooperation among multiple suppliers. We formulate the cost allocation problem for the dynamic vehicle routing environment, where only part of the customers are known in advance, and the rest become known in real time. We propose an online cost-sharing mechanism coupled with a look-ahead dynamic vehicle routing framework that explicitly forecasts future customer requests. improve freight systems performance in dense urban areas. 4 - Dynamic Pricing In One-way Car Sharing Networks: A Distributional Fluid Approximation Approach Iman Dayarian, Georgia Institute of Technology, 765 Ferst Dr NW, Atlanta, GA, 30318, United States, iman.dayarian@isye.gatech.edu, Martin W P Savelsbergh

3 - A Branch-and-price Approach For The Vehicle Routing Problem With Roaming Delivery Locations Gizem Ozbaygin, Bilkent University, Ankara, Turkey, ozbaygin@bilkent.edu.tr, Martin W P Savelsbergh, Hande Yaman, Oya Ekin Karasan We study the vehicle routing problem with roaming delivery locations in which each customer is associated with multiple locations and time windows. Exactly one location per customer should be included in the delivery plan respecting the time windows. We devise a branch-and-price algorithm to solve the problem and perform a computational analysis. 4 - E-fulfillment For Attended Last-mile Delivery Services In Metropolitan Areas We consider service time windows as a scarce resource and combine concepts of revenue management and vehicle routing to improve e-fulfillment of last-mile delivery services. As the customer has to be present for attended deliveries such as groceries, a service time window has to be agreed upon already when the order is accepted. We will focus on the factors impacting the success for e-fulfillment in metropolitan areas, considering uncertain demand and traffic conditions. To this end, we analyse historical order data and extend time-dependent vehicle routing techniques. Jan Fabian Ehmke, Freie Universität Berlin, Garystr. 21, Berlin, 14195, Germany, janfabian.ehmke@fu-berlin.de, Catherine Cleophas, Charlotte Köhler, Magdalena Lang TB62 Cumberland 4- Omni Data Mining and Optimization for Improved Airport Operations Sponsored: Aviation Applications Sponsored Session Chair: Heng Chen, University of Nebraska–Lincoln, Supply Chain Management and Analytics, Lincoln, NE, 68588, United States, heng@unl.edu 1 - Airport Capacity Estimation For Decision Support Sreeta Gorripaty, University of California Berkeley, gorripaty@berkeley.edu, Mark M Hansen Capacity is a critical component of airport performance and air traffic decision- making. Capacity of an airport is the throughput observed at sufficiently high demand and is thus demand censored. The demand that is observed at the airport is a result of strategic and tactical decisions made to avoid buildups of unmet demand, thus making it challenging to estimate the capacity of an airport. We demonstrate that Random Survival Forest (RSF) model can be used to capture the censored nature of capacity data and model hourly capacity. The RSF capacity model is further used in decision support algorithms to represent airport capacity. 2 - Parameter Fixing Method For Improving The Rate Of Convergence Of A Hybrid Particle Swarm Optimization Giuseppe Sirigu, Georgia Institute of Technology, giuseppe.sirigu@aerospace.gatech.edu A new solution is proposed to perform just in time taxi operations using autonomous electric towbarless tractors, thereby to minimize the overall cost and the environmental impact of the ground operations. An algorithm for a tool that provides conflict-free schedules for the tractor autopilots was developed, which is based on a hybrid particle swarm optimization (HPSO), hybridized with a hill climbing meta-heuristic. In order to improve the rate of convergence of the algorithm, we developed a parameter fixing method. 3 - Machine Learning Techniques For Airport Passenger Flow Management Xiaojia Guo, University College London, London, United Kingdom, x.guo.11@ucl.ac.uk, Yael S Grushka-Cockayne, Casey Lichtendahl, Frederick Tasker, Neville Coss, Tom Garside, Bert De Reyck Passengers missing their connection at an airport can have a major impact on passenger satisfaction and airline delays. We develop a predictive model of passengers’ connecting time using machine learning techniques, and provide both point forecasts and probabilistic forecasts using historical and real-time data. Based on these forecasts, we are developing a dynamic planning tool for London’s Heathrow Airport to support airport operations.

284