2016 INFORMS Annual Meeting Program

416 / 561

INFORMS Nashville – 2016
416
2 - Efficient And Reliable Package Delivery Path Planning For Drones
Mohannad Kabli, Mississippi State University,
, Sudipta Chowdhury,
Mohammad Marufuzzaman
The development of efficient and reliable path for drones is becoming crucial in
today’s world due to its potential applicability in many commercial purposes. This
research focuses on designing an efficient and reliable path planning for the
delivery of packages by considering power requirement, collision, altitude, and
other related factors into account. The characteristics of the optimal path are
expressed in terms of a multi-objective cost function which we solved by using an
Adaptive Large Neighborhood Search (ALNS) heuristic.
3 - A Continuum-approximation Approach To Optimize Routing
Decisions For Drones Under Extreme Events
Sudipta Chowdhury, Mississippi State University,
, Adindu Emelogu,
Mohammad Marufuzzaman, Linkan Bian
Application of drones in various sectors is becoming common place day by day,
and it has got huge potential in humanitarian logistics. This research pertains to
optimization of logistics management of drones under extreme events. The key
decisions investigated in this study is where to locate the transportation centers,
how to assign demand points at each transportation center, and what should be
the inventory policy such that the total network cost is minimized. Continuous
Approximation (CA) approach is used to solve this problem. As a test bed for
computational experiments, coastal region of Mississippi is selected due to its long
history of getting affected by various natural disasters.
WB60
Cumberland 2- Omni
Routing Optimization Problems
Sponsored: TSL, Freight Transportation & Logistics
Sponsored Session
Chair: Ahmed Ghoniem, Associate Professor, University of
Massachusetts Amherst, 121 Presidents Dr., Amherst, MA, 01002,
United States,
1 - A Branch-and-cut-and-price Algorithm For The Generalized
Vehicle Routing Problem
Mohammad Reihaneh, University of Massachusetts Amherst,
Amherst, MA, 01002, United States, ,
Ahmed Ghoniem
We consider the Generalized Vehicle Routing Problem in which customers are
partitioned into mutually exclusive clusters, each with a specific demand. The
goal is to construct cost minimizing tours such that exactly one customer is visited
in every cluster, subject to vehicle capacity constraints. The proposed specialized
branch-and-cut-and-price algorithm compares favorably against state-of-the-art
exact algorithms in the literature and closes several open benchmark instances.
2 - A Two-level Optimization Approach For Robust Aircraft Routing
And Retiming
Mohamed Haouari, Qatar University,
We address the robust aircraft routing and flight retiming problem, and we
propose a two-level solution strategy that embeds a simulation-optimization
procedure within an evolutionary algorithm. The proposed approach requires
inserting buffer times prior to the flight departure times in order to improve the
robustness of both aircraft and passengers connections. We present the results of
extensive computational experiments that were carried out on a set of real data.
3 - Resource Constrained Arc Routing For Snow Plowing
Joris Kinable, Carnegie Mellon University, Pittsburgh, PA,
United States, , Willem-Jan Van Hoeve,
Stephen F Smith
This work considers a Resource Constrained Arc Routing Problem for snow
plowing, a fundamental problem faced by many cold-weather cities. In RC-ARP,
routes for a heterogeneous set of vehicles must be computed such that they
collectively cover a network of streets, while adhering to various resource (salt)
usage and replenishment constraints. We contrast exact and heuristics
approaches, as well as a decomposition method. The performance is demonstrated
on real-world data from the city of Pittsburgh, PA.
4 - Vehicle Routing Problems With Drone Delivery
Ahmed Ghoniem, University of Massachusetts Amherst,
, Mohamed Haouari,
Mohammad Reihaneh
We study a Vehicle Routing Problem with drone delivery. In this setting, a
customer is directly visited by a vehicle or his/her demand is indirectly delivered
from a neighboring customer using a drone. A mixed-integer formulation is
presented along with a branch-and-price algorithm. Alternative solution
approaches are investigated for the column generation pricing subproblem and
computational results are presented.
WB61
Cumberland 3- Omni
Railway Analytics
Sponsored: Railway Applications
Sponsored Session
Chair: Qing He, SUNY Buffalo, Buffalo, NY, United States,
1 - Estimating The Probability And Impact Of Track Defects In
Railroad Operations And Maintenance Planning
Alexander Lovett, University of Illinois at Urbana-Champaign,
Slow orders and spot maintenance and are used by railroads to mitigate the
impact of track defects and ensure safe train operations between capital
maintenance actions. Individually, slow orders and spot maintenance do not
appear to have a significant cost impact, but in aggregate, they can reduce
efficiency and increase costs over the rail network. This presentation will discuss
probabilistic methods to predict when slow orders and spot maintenance will be
required to allow for more efficient objective track maintenance planning.
2 - Predictive Switch Health
Casey Jen, CSX, Jacksonville, FL, United States,
, Bob Gutman, Aihong Wen
Among all the Communication and Signal (C&S) components, switches cause
biggest number of train delays on line of road. In this study, we leveraged cutting-
edge Big Data and Predictive Analytics techniques and developed a set of
prediction models to assess future switch health. Taking information from
multiple data sources, such as Computer Aided Dispatching System (CADS) event
logs, switch inspection records, switch incidents records, and many others, these
prediction models will enable CSX to proactively maintain our vital assets and
better plan C&S workforce.
3 - Data-driven Optimization Of Railway Track Inspection And
Maintenance Using Markov Decision Process
Qing He, University at Buffalo, SUNY, Buffalo, NY, United States,
Siddhartha Sharma, Yu Cui, Zhiguo Li
This paper develops a data-driven condition-based maintenance policy for track
inspection. This paper will help in maintaining high service level of the railway
tracks which is a difficult task to accomplish. Dataset is two-year track geometry
inspection data which contains a variety of geometry measurements for every
foot. We employ Markov Chain to model track deterioration, and build a Markov
Decision Process for track maintenance decision making and optimize it using
value iteration algorithm. By comparing with existing maintenance policy with
Markov Chain Monte Carlo simulation, the new maintenance policy developed in
this paper can save nearly 10% maintenance costs.
WB62
Cumberland 4- Omni
Aviation Economics Decision-making
Sponsored: Aviation Applications
Sponsored Session
Chair: Ricard Gil, Johns Hopkins Carey Business School,
100 International Drive, Baltimore, MD, 21202, United States,
1 - A Profit Maximizing Integrated Model Of Fleet Assignment
And Aircraft Routing With Considerations Of Flight
Schedule Disturbances
Muhammed Sutcu, Assistant Professor, Abdullah Gul University,
Sumer Campus, Erkilet Bulvari, Kayseri, 38060, Turkey,
, Baris Yildiz, Yeliz Yoldas
Airline schedule disturbances are one of the most critical problems due to the
unpredictable disruptions such as technical failures and severe weather
conditions. In this work, a mixed-integer mathematical model integrating fleet
assignment and aircraft routing is proposed to select from among a set of flights
and to assign the selected flights to appropriate aircraft for a profitable daily
schedule with as minimum delay and idle time in total as possible. The
uncertainties of demand, failures of the aircraft and delays arising from adverse
weather conditions are also integrated to the corresponding model. To propose a
solution methodology for this model is another purpose of this paper.
WB60