Informs 2015

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INFORMS Philadelphia – 2015
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TA69
69-Room 201C, CC
Maritime Logistics
Sponsor: Transportation, Science and Logistics
Sponsored Session
Chair: Irina Benedyk, United States of America,
1 - Solving the Pre-Marshalling Problem to Optimality
Kevin Tierney, Assistant Professor, University of Paderborn,
Warburger Strafle 100, Paderborn, 33098, Germany,
Stefan Voss, Dario Pacino
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,
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,
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, ,
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,
1 - Forecast Locomotive Surplus and Deficit to Balance the
Terminals and Shops
Kamalesh Somani, CSX Transportation, 500 Water St,
Jacksonville, FL, 32202, United States of America,
, 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.
2 - Failure Prediction and Sensor Spacing Optimization Along
Track Corridors
Yanfeng Ouyang, Univ. Of Illinois, 205 N. Mathews Ave, Urbana,
United States of America, 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, 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
Place, Gainesville, FL, 32607, United States of America,
Ravindra Ahuja, Krishna Jha
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,
Co-Chair: Yu Ding, Professor, Texas A&M University, ETB 4016,
MS 3131, College Station, TX, United States of America,
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,
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, 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.
TA72