Technical Sessions – Monday July 11

FB-11 IFORS 2011 - Melbourne
Wright-Paterson AFB, OH, United States,
marlin.thomas@afit.edu
Markov chains are fundamental in modeling operational systems due to the rational
appeal and computational convenience gained by the Markov property.
Under certain lumpability conditions the state space of a Markov chain can be
partitioned into selected sets of states to form a smaller chain that retains the
Markov property. This can simplify computations and sometimes provide desirable
structural properties such as special subclasses of states that are peculiar
to manpower planning models, selected DNA markers, and supply chain networks.
Methods exist for examining alternative lumping options for given transition
probabilities but identifying these alternatives can be quite difficult. With
data however, one can postulate desired partitions and examine their statistical
validity using standard inference procedures. The procedure for applying this
method will be summarized along with examples.
� FB-11
Friday, 13:15-14:45
Meeting Room 112
Network Design
Stream: Integer Programming
Invited session
Chair: Haifei Yu, Department of Management Science and
Engineering, Northeastern University, No.11, Lane 3, Wen Hua Road,
He Ping District, 110819, Shenyang, Liaoning, China,
yuhaifei@gmail.com
1 - Solving a Network Flow Model using an Extended Tree
Knapsack Approach
Hennie Kruger, School of Computer, Statistical and
Mathematical Sciences, North-West University, Private Bag
X6001, 2520, Potchefstroom, South Africa,
Hennie.Kruger@nwu.ac.za, Giel Hattingh, Tumo Baitshenyetsi
There are many practical decision problems that fall into the category of network
flow problems and numerous examples of applications can be found in areas
such as telecommunications, logistics, engineering, computer science etc.
In this paper, the feasibility of representing a network flow model in a tree
network model and subsequently solving it using an extended tree knapsack
approach is investigated. To compare and validate the proposed technique, a
specific case study (an oil pipeline design problem) was chosen from the literature
that can be used as a basis for the research project.
2 - Single Allocation Problem in Hub-and-Spoke Networks
on 2D Plane
Ryuta Ando, Faculty of Science and Engineering, Department of
Information and System Engineering, Chuo University, Kasuga,
Bunkyo-ku„ 112-8551, Tokyo, Japan,
r.ando.1201+lab@gmail.com, Tomomi Matsui
Hub-and-spoke network arises in the airline industry and postal delivery systems.
The hub-and-spoke structure is based on the situation when some nodes,
called non-hub nodes, can interact only via a set of completely interconnected
nodes, called hub nodes. We consider a single allocation problem defined by
nodes on 2-dimensional plane, which allocates each non-hub node to one of hub
nodes, and minimizes the total transportation cost. We formulate the problem
to a mixed integer programming problem and propose 1.6367-approximation
algorithm based on randomized rounding technique.
3 - Optimizing the Deployment of a Multilevel Optical FTTH
Network
Faye Alain, CEDRIC - ENSIIE, 91025, Evry, France,
alain.faye@ensiie.fr, Matthieu Chardy, Marie-Christine Costa,
Mathieu Trampont
Due to the emergence of bandwidth-requiring services, telecommunication operators
are being compelled to renew their fix access network, most of them favoring
the Fiber To The Home (FTTH) technology. This presentation focuses
on the optimization of FTTH deployment, which is of prime importance due
to the economic stakes. First we propose a mixed integer formulation for this
decision problem. Then, valid inequalities and problem size reduction schemes
are presented. Finally efficiency of solving approaches is assessed through extensive
numerical tests performed on Orange real-life data.
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4 - Optimization of Tree-structured Gas Distribution Network
using Ant Colony Optimization: A Case Study
Amir Mohajeri, Industrial Engineering, Mazandaran University
of Science and Technology, Tabarsi Street, 4716698563, Babol,
Mazandaran, Iran, Islamic Republic Of,
mohajeri.amir@gmail.com, Iraj Mahdavi, Nezam
Mahdavi-Amiri
Here, a mixed integer programming model is formulated to minimize the total
cost in the gas network. The aim is to optimize pipe diameter sizes so that
the location-allocation cost is minimized. We apply the Minimum Spanning
Tree technique to obtain a network with no cycles, spanning all the nodes. The
problem being NP- hard, we propose an ant colony optimization algorithm and
compare its performance with an exact method. A case study in Mazandaran
gas company in Iran is conducted to illustrate the validity and effectiveness of
the proposed model and the ant colony algorithm.
� FB-12
Friday, 13:15-14:45
Meeting Room 205
Robust Optimization, Planning and Control
Stream: Continuous and Non-Smooth Optimization
Invited session
Chair: Christian Gahm, Chair Of Business Administration,
Production & Supply Chain Management, Augsburg University,
Universitätsstraße 16, 86159, Augsburg, Germany,
christian.gahm@wiwi.uni-augsburg.de
1 - A Robust Optimization Approach to the Optimization of
Airline Employee Scheduling
Yi Gao, Faculty of Engineering and Industrial Sciences,
Swinburne University of Technology, PO Box 218, 3122,
Hawthorn, Victoria, Australia, ygao@swin.edu.au
Successful manpower scheduling is vital for airport operations. Stochastic
flight delays make airline employees scheduling challenging. The study aims
to mitigate the impact of flight delays. With the simulated manpower demand
distribution as inputs, the study used mixed integer programming and robust optimization
to generate working schedules for the employees. The comparison
between the robust scheduling model proposed in this study and the traditional
model used by a major US airline suggested that the robust model saved more
on the overall cost, and had less manpower demand violations.
2 - Using Supervised Learning to Improve Monte Carlo Integral
Estimation
Brendan Tracey, Aeronautics and Astronautics, Stanford
University, Durand Building, 496 Lomita Mall, 94305, Stanford,
CA, United States, btracey@stanford.edu, David Wolpert, Juan
Alonso
In uncertainty quantification and robust optimization calculating expected values
is important. We present Stacked Monte Carlo (StackMC), a new method
for post-processing a given set of any Monte-Carlo-generated samples (simple
sampling, importance sampling, etc.) to improve the integral estimate, and in
theory StackMC reduces variance without adding bias. We present a set of experiments
confirming that the StackMC estimate of an integral is more accurate
than both the associated pre-processing Monte Carlo estimate and an estimate
based on a functional fit to the MC samples.
3 - Robust Models for Dynamic Multilevel Capacitated Facility
Location
Marina Gebhard, Chair of Business Administration and
Logistics, University Erlangen-Nuremberg, Lange Gasse 20,
90403, Nürnberg, Germany,
marina.gebhard@wiso.uni-erlangen.de, Vincenzo De Rosa, Jens
Wollenweber
We study a strategic facility location problem for distribution systems, where
demand is served by a network of multiple supply stages. We show a new
formulation for the robust multi-level capacitated facility location model that
minimizes the expectation of the relative regrets over a multi-period planning
horizon. Uncertainty in future demand and transportation costs is modeled by
a set of scenarios. We compare our robust model to deterministic and alphareliable
mean-excess model formulations and analyze the results for differences
in total cost, sites, robustness and effectiveness.