2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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3 - Modeling Plug-in Electric Vehicles Driving And Charging Behavior
Using Real-world Connected Vehicles Data
Kuilin Zhang, Assistant Professor, Michigan Technological
University, Houghton, MI, 49931, United States,
Shuaidong Zhao
We propose to investigate driving and charging behavior of Plug-in Electric
Vehicles using real-world connected vehicles data. We use a data-driven approach
to estimating 24-hour activity-travel dynamics of individual drivers from
connected vehicles data collected in real-world. Based on this real-world driver’s
activity and mobility pattern, we formulate an optimization model to address
driving and charging behavior of Plug-in Electric Vehicles to better understand the
battery performance of electric vehicles under real-world conditions.
4 - Operation Of Electricity And Transportation Networks With
Ev Wireless Charging
Mohammad Khodayar, Southern Methodist University,
Saeed D Manshadi, Khaled Abdelghany,
Halit Uster
This research presents the coordinated operation of wireless electric vehicle
charging stations (WECS) in electricity and transportation networks. The traffic
flow pattern in transportation network is assumed to follow the user equilibrium
(UE) traffic assignment, where the cost of utilized electricity is incorporated in the
total traveling cost. The presented formulation leverages consensus optimization
to address the unit commitment in the electricity network as well as user
equilibrium traffic assignment in the transportation network.
SA60
Cumberland 2- Omni
Topics on Shared Public Transportation Systems
Sponsored: TSL, Urban Transportation
Sponsored Session
Chair: Hai Wang, Singapore Management University, Singapore
Management University, Singapore, Singapore, Singapore,
1 - Matching Problem For A Stochastic And Dynamic Online Vehicle
Sharing System
Hai Wang, Singapore Management University,
Chiwei Yan
We study a stochastic and dynamic matching problem for online vehicle sharing
platform: match the spatial and temporal changing demand (ride request) with
supply (vehicle). We propose an algorithm to determine the pairings of drivers to
riders’ requests. At any decision epoch, we consider the set of known available
drivers and potential available drivers, as well as the set of known existing
passengers and potential passengers. We use an iterative procedure which calls a
static and deterministic matching problem as a sub-routine. The objective is to
minimize the average waiting time until picked-up for ride requests. We
demonstrate the advantages of our algorithm by testing in real world data sets.
2 - Estimating Primary Demand In One-way Vehicle Sharing Systems
Chiwei Yan, Massachusetts Institute of Technology, Cambridge,
MA, United States, Chong Yang Goh
Observed trip data for one-way vehicle sharing systems do not always correspond
to true demands for the service due to varying vehicle and parking availability.
For example, in bike sharing systems, passengers arriving at an empty pickup
station may either leave the system or spill over to nearby stations. We propose
efficient methods to estimate the true origin-destination demands in a one-way
vehicle sharing system using observed trip data. Our approach models a
customer’s station substitution behavior based on a ranking-based choice model.
We demonstrate the effectiveness of our approach using data from a bike-sharing
system in Boston.
3 - The Learning Curve Of Taxi Drivers In an Urban Area:
An Empirical Analysis
Youngsoo Kim, Singapore Management University, Singapore,
Singapore,
This study aims to better understand the dynamic change of individual taxi
drivers’ performance on both an aggregated output level (e.g., revenue and trips)
and process level (e.g., occupancy rate and zone selection decision). We also
conduct counterfactual policy experiments that capture the change derived
through knowledge sharing of demanding zone on both individual and company
levels. The implications of our findings for both theory and practice are discussed.
4 - Stochastic Ride-matching In Peer-to-peer Ridesharing Systems
Neda Masoud, University of Michigan, 2350 Hayward St.,
2124 GG Brown Bldg., Ann Arbor, MI, 48109, United States,
, R. Jayakrishnan
We formulate the multi-hop peer-to-peer stochastic ride-matching problem as a
binary program, and propose an efficient algorithm to solve the problem. We use
a forecast of passenger arrivals, and take into consideration the possible future
states of the ridesharing system when routing drivers. The multi-hop property of
the system allows passengers to transfer between different vehicles/modes of
transportation.systems.
SA61
Cumberland 3- Omni
RAS Student Paper Award
Sponsored Session
Chair: Steven Harrod, Technical University of Denmark, KGS. Lyngby,
Denmark,
Rail Applications Section (RAS) sponsored a student research paper contest on
analytics and decision making in railway applications. Papers must advance the
application or theory of OR/MS for improvement of freight or passenger railway
transportation, and it must represent original research that has not been
published elsewhere by the time it is submitted. Authors of the First, Second and
Third Place award winning papers will present their papers in this session.
SA62
Cumberland 4- Omni
Aviation Applications Section: Best Student
Presentation Competition
Sponsored: Aviation Applications
Sponsored Session
Chair: Lavanya Marla, University of Illinois,
SA63
Cumberland 5- Omni
Facility Logistics
Sponsored: TSL, Facility Logistics
Sponsored Session
Chair: Jennifer A Pazour, Rensselaer Polytechnic Institute, 110 8th
street, CII 5217, Troy, NY, 12180, United States,
1 - Facility-level Item Allocation Problem In
Ship-from-Store Environment
Seyed Shahab Mofidi, Rensselaer Polytechnic Institute,
110 8th Street, RPI ISE Department, CII 5015, Troy, NY, 12180,
United States, , Jennifer A Pazour
Leading retailers are using their brick-and-mortar stores to fulfill online order
requests, which results in ambidextrous stores that use inventory to serve both
in-store and on-line shoppers. We develop a novel multi-product optimization
model that captures the tradeoff between applying resources in advance when the
demand is unknown or applying resources after the demand realizes. A case study
illustrates how our model can be used to recommend item allocation policies for
omni-channel supply chains.
2 - Parallel Algorithms For Large Assignment Problems On Graphics
Processing Unit Clusters
Rakesh Nagi, University of Illinois, Urbana-Champai, 117
Transportation Building, MC-238, 104 South Mathews Avenue,
Urbana, IL, 61801, United States, , Ketan Date
We discuss efficient parallel algorithms for solving large instances of the Linear
Assignment Problem (LAP) and the Quadratic Assignment Problem (QAP). Our
parallel architecture is comprised of CUDA enabled NVIDIA Graphics Processing
Units (GPUs) on a computational cluster. We propose novel parallelization of the
Hungarian algorithm on the GPUs, which shows excellent parallel speedup for
large LAPs. We also propose a novel parallel Dual Ascent algorithm on the GPUs,
which is used for solving the RLT2 linearization of the QAP, which also utilizes
our parallel Hungarian algorithm. We show that this GPU-accelerated approach is
extremely valuable in a branch-and-bound scheme.
SA60