2015 Informs Annual Meeting

TA72

INFORMS Philadelphia – 2015

TA69 69-Room 201C, CC Maritime Logistics Sponsor: Transportation, Science and Logistics Sponsored Session Chair: Irina Benedyk, United States of America, birina@purdue.edu 1 - Solving the Pre-Marshalling Problem to Optimality Kevin Tierney, Assistant Professor, University of Paderborn, The pre-marshalling problem is a key problem at container terminals. The goal is to find a minimal sequence of re-shuffling containers in a set of stacks such that they are arranged according to the time each container must leave the stacks. We present a novel algorithm using A* and IDA* combined with several novel branching and symmetry breaking rules. We solve over 500 previously unsolved benchmark instances to optimality clearly outperforming current state-of-the-art methods. 2 - A Genetic Algorithms Based Approach to Develop Cost-Effective Annual LNG Delivery Program Fatih Mutlu, Asst. Professor, Qatar University, Doha, Doha, Qatar, fatihmutlu@qu.edu.qa Developing a cost effective annual delivery program for liquefied natural gas suppliers is known to be among the most challenging integrated inventory, production, and maritime delivery routing problems. We use a genetic algorithms based approach to solve this problem. We produce alternative routes for the vessels, each of which represents a chromosome. Our method performs better than the exact solution method in all of the problem instances we solved. 3 - A Mathematical Model for the Ship Scheduling and Cargo Assignment Problem Salomon Wollenstein Betech, Student, Instituto Tecnologico de Estudios Superiores de Monterrey, Av Carlos Lazo 100, Alvaro Obregón, DF, 01389, Mexico, s.wollenstein@gmail.com Middle-size companies with maritime shipping face a scheduling and cargo- assignment problem. Given a set of demands, suppliers, contracts, and ships, the company must design their operations to minimize cost. A mathematical model is proposed that simultaneously solves the ship scheduling and cargo assignment problem for a period of a year, discretizing time in days. The algorithm is capable of solving the problem at a rate of five ships and ports in ten minutes. 4 - A Bivariate Probit Model to Analyze Perspectives for Container Shipping on the Northern Sea Route Irina Benedyk, United States of America, birina@purdue.edu, Srinivas Peeta This study seeks to explore opportunities and barriers for container freight shippers to use the Northern Sea Route. A stated preference survey of freight shippers in East Asia and Europe is conducted. A Bivariate Probit Model is used to investigate attitudes towards the usage of the North Sea Route, and identify key factors that influence them. TA70 70-Room 202A, CC Advanced Analytics in Tactical Decision Making Sponsor: Railway Applications Sponsored Session Chair: Krishna Jha, Vice President Research And Development, Optym, 7600 NW 5th Place, Gainesville, FL, 32607, United States of America, krishna.jha@optym.com 1 - Forecast Locomotive Surplus and Deficit to Balance the Terminals and Shops Warburger Strafle 100, Paderborn, 33098, Germany, kevin.tierney@upb.de, Stefan Voss, Dario Pacino

2 - Failure Prediction and Sensor Spacing Optimization Along Track Corridors Yanfeng Ouyang, Univ. Of Illinois, 205 N. Mathews Ave, Urbana, United States of America, yfouyang@illinois.edu, Zhaodong Wang This talk describes a machine-learning based framework for determining sensor deployment to ensure optimal reporting of potential incident-prone failures of the passing traffic. A simulation-based optimization model is used to find the optimal sensor spacing. 3 - Development and Application of Line-of-road Emulator Tool in CSX Yu Wang, Manager Operations Research, CSX Transportation Inc., 500 Water Street, Jacksonville, FL, 32202, United States of America, Yu_Wang@csx.com, Eric Pachman Line-of-Road Emulator is a web-based tool to visualize train movements in a GIS view. The tool can highlight slow-moving and/or long-dwell trains with different styles of bubbles, which provides informative insights to help railroad managers understand the situation and investigate the reasons causing congestions. The tool was used to create an illustration video about the congestion happened on the northern tier of CSX network in 2014 winter, and has received high evaluation from the users. 4 - Optimization Algorithms for Hump Yard Decision Support System Alexey Sorokin, Senior Systems Engineer, Optym, 7600 NW 5th Rail cars are classified to their appropriate outbound trains in yards. Important decisions made by yardmasters include the order in which trains should be humped and classification track on which a block should be built at any point in time. We developed optimization modules for a real-time decision support system that can assist yardmasters with these decision. Benefits of the optimization algorithms were computed using a hump-yard simulation system previously developed by Optym. TA72 72-Room 203A, CC DDDAS for Industrial and System Engineering Applications I Sponsor: Quality, Statistics and Reliability Sponsored Session Chair: Shiyu Zhou, Professor, University of Wisconsin-Madison, Department of Industrial and Systems Eng, 1513 University Avenue, Madison, WI, 53706, United States of America, shiyuzhou@wisc.edu Co-Chair: Yu Ding, Professor, Texas A&M University, ETB 4016, MS 3131, College Station, TX, United States of America, yuding@iemail.tamu.edu 1 - Dynamic Data Driven Applications Systems DDDAS): New Capabilities in Data Analytics Frederica Darema, Program Director, Air Force Office of Scientific Research, United States of America, frederica.darema@us.af.mil This talk provides an overview of future directions enabling in new methodologies for analytics through the DDDAS (Dynamic Data Driven Applications Systems) paradigm. We will discuss how DDDAS allows new capabilities in data analytics to enable optimized and fault tolerant systems management, improved analysis and prediction of system conditions, in a diverse set of application areas ranging from aerospace applications to smart cities, to manufacturing planning and control, and cybersecurity. 2 - Offline Learning for Dynamic Data-driven Capability Estimation for Self-aware Aerospace Vehicles Douglas Allaire, Assistant Professor, Texas A&M University, 425 MEOB, 3123 TAMU, College Station, TX, 77843, United States of America, dallaire@tamu.edu, Benson Isaac A self-aware aerospace vehicle can dynamically adapt the way it performs missions by gathering information about itself and its surroundings and responding intelligently. We present an information-theoretic approach to offline learning via the optimization of libraries of strain, capability, and maneuver loading using physics-based computational models. Online capability estimation is then achieved using by a Bayesian classification process that fuses dynamic, sensed data. Place, Gainesville, FL, 32607, United States of America, alexey.sorokin@optym.com, Ravindra Ahuja, Krishna Jha

Kamalesh Somani, CSX Transportation, 500 Water St, Jacksonville, FL, 32202, United States of America, Kamalesh_Somani@CSX.com, Shankara Kuppa, Artyom Nahapetyan

Number of locomotives coming into a terminal may not be same as number of locomotives going out. This creates imbalance where some terminals are in constant need for locomotives and some other terminals usually have spare locomotives. Similarly a shop may receive more locomotive than its capacity and at the same time another shop may not be used to its full capacity. We developed advance analytics tools which help to minimize network balancing cost and any train delay because of locomotives.

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