Informs 2015

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INFORMS Philadelphia – 2015
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SB69
69-Room 201C, CC
Routing Problems with Uncertainty I
Sponsor: Transportation, Science and Logistics
Sponsored Session
Chair: Shu Zhang, School of Economics and Business Administration,
Chongqing University, No. 174 Shazheng Street, Shapingba,
Chongqing, 400030, China,
1 - Technician Scheduling Problem with Experience-based
Service Times
Xi Chen, The University of Iowa, Iowa City IA 52242
, Michael Hewitt, Barrett Thomas
We study a dynamic technician scheduling problem with experience-based service
times. We use well-established models from the psychology community to model
how gains in experience impact service times. We assume that customer requests
are uncertain. We model the problem as a Markov decision process with the
objective of minimizing the expected total service times over a finite horizon. We
also propose a number of lookahead schemes and present results.
2 - Route Evaluators for the Vehicle Routing Problem with Stochastic
and Correlated Travel Times
Jorge E. Mendoza, Associate Professor, Polytech Tours, 64 Avenue
Jean Portalis, Tours, 37200, France,
Andres Medaglia, Andrés Sarmiento, Carlos Felipe Ruiz,
Raha Akhavan-Tabatabaei
Vehicle routing problems with stochastic travel times (VRPSTTs) consist in
designing transportation routes of minimal expected cost over a network where
times are represented by random variables. In this research we consider the case
of correlated random variables. We present two methodologies, one analytical
and one based on simulation, to efficiently evaluate routes. We present
computational experiments showing the value of considering correlations in
VRPSTTs.
3 - Revenue Driven Supply Chains under Uncertainty
Nizar Zaarour, Northeastern University, Boston, MA, United
States of America, , Emanuel Melachrinoudis,
Marius M. Solomon
We consider the discount pricing of products that are being phased-out in retail
stores. This triggers a time horizon for the retailer to sell the existing inventory or
sell it at a major discount in a reverse logistics move. We develop mathematical
programming models to deal with this issue. The models are tested on real data
provided by a national retailer.
4 - Multi-Period Orienteering with Uncertain Adoption Likelihood and
Waiting at Customers
Shu Zhang, School of Economics and Business Administration,
Chongqing University, No. 174 Shazheng Street, Shapingba,
Chongqing, 400030, China, ,
Barrett Thomas, Jeffrey Ohlmann
We consider a problem in which a traveler visits customers over a multi-period
horizon to influence and observe the chance of customer adoption. Over the
multiperiod horizon, each customer’s likelihood of adopting the traveler’s product
stochastically evolves. We model the problem as a partially observed Markov
decision process with an objective to maximize the expected sales. Each period,
the traveler must decide which customers to schedule and which order to visit,
knowing that due to uncertain wait times the traveler may not be able to meet
customers even if they are on the schedule.
SB70
70-Room 202A, CC
RAS Problem Solving Competition 2015
Sponsor: Railway Applications
Sponsored Session
Chair: Francesco Corman, Delft University Of Technology, Mekelweg 2,
Delft, Netherlands,
1 - Railway Applications Society Problem Solving Competition
Francesco Corman, Delft University Of Technology, Mekelweg 2,
Delft, Netherlands,
This session is reserved for the finalists of the RAS Problem Solving Competition
(PSC). The presenters and their abstracts won’t be determined until we finish the
judging process, which happens around mid-October. The selection committee
will identify the top three teams who will present their results during the session.
PSC asks participants to build a model to predict track failures on a railway
network. Data about recorded traffic over a railway network is given, together
with yellow and red tags. Yellow tags are measured deviations from the railway
regulations for track layout, alignment and condition, which would require
maintenance, but still allow for running traffic. Red tags are failures with require
immediate intervention. The goal of the PSC is to predict when yellow tags will
turn red, to be able to plan optimally maintenance and repair actions.
SB71
71-Room 202B, CC
Traffic State Estimation Methods and Data
Sponsor: TSL/Urban Transportation
Sponsored Session
Chair: Kai Yin, Nomis Solutions, 1111 Bayhill Drive, Suite 230, San
Bruno, CA, 94066, United States of America,
1 - Trip Splitting Approximation to Link Travel Time Estimation on a
Transportation Network
Kai Yin, Nomis Solutions, 1111 Bayhill Drive, Suite 230,
San Bruno, CA, 94066, United States of America,
, Bruce Wang, Wen Wang, Teresa Adams
We study link travel time estimation using time stamps of trips on a
transportation network. Assuming that each link associates with a random travel
time, a statistical inference method, trip splitting approximation, is proposed to
deal with a general link travel time distribution and also address the case that the
routes of some trip observations are unknown. We explore an iterative procedure
analogous to the Expectation-Maximization (EM) algorithm and apply the
Bayesian method for solutions. The properties of solutions and the effect of data
quality are examined.
2 - Map Inference using Probe Vehicle GPS Data with Low
Reporting Frequency
Wen Jin, Tsinghua University, 519A Shunde Building, Beijing,
100084, China, Hai Jiang
The accuracy of road maps is critical to providing quality navigation services. As
rapid urbanization takes place in Chinese cities, there are frequent changes to
road networks due to the expansion of the city to suburban areas as well as urban
redevelopment in developed districts. Most, if not all, existing literature on map
inference problems requires GPS data with high reporting frequency (>= 1 Hz). In
this talk, we present a network optimization based approach to solve the map
inference problem when the reporting frequency is low. Our algorithm is
validated using real data from a leading navigation service provider in China.
3 - Predicting the Status of Traffic Signals using GPS Data from
Probe Vehicles
Xin Qi, Department of Industrial Engineering, Tsinghua
University, Shunde Building 519A,Tsinghua University,
Beijing, China, Hai Jiang
It has been recognized that intersection delay accounts for over 1/3 of the total
travel time for a trip in the city. To provide quality route guidance to drivers, it is
therefore critical to be able to estimate the delays caused by traffic signals. Since
intersection delays are dependent on the status of traffic signals, in this talk we
develop models to estimate key operating parameters of intersection signals: their
cycle lengths as well as the corresponding signal timings. We test our models in
the city of Beijing and results show that satisfactory results can be obtained in a
wide variety of scenarios.
SB72
72-Room 203A, CC
QSR Student Introduction and Interaction and Best
Student Poster Competition
Sponsor: Quality, Statistics and Reliability
Sponsored Session
Chair: Kaibo Wang, Associate Professor, Tsinghua University,
Department of Industrial Engineering, Beijing, China,
Co-Chair: Hui Yang, Associate Professor, Pennsylvania State University,
310 Leonhard Building, Industrial and Manufacturing Eng., State
College, PA, 16801, United States of America,
1 - QSR Student Introduction and Interaction and Best Student
Poster Competition
Kaibo Wang, Associate Professor, Tsinghua University,
Department of Industrial Engineering, Beijing, China,
Student poster exhibition starts at 10:00am in the session room; all are welcomed
to arrive earlier to interact with the students. The Introduction&Interaction
session formally starts at 11:00am. Each of the student members presents a 2-min
elevator speech first; interactions with invited guests are then followed. A lunch
gathering in the same room for student members and invited guests with tickets
starts at 12:30pm. The student poster exhibition continuous until 1:00pm.
SB72