Technical Sessions – Monday July 11

MA-05 IFORS 2011 - Melbourne
3 - A Distribution Inventory Model with Transshipments
from a Support Warehouse
Christian Howard, Industrial Management & Logistics, Lund
University, Ole römers väg 1, Box 118, 22100, Lund, Sweden,
christian.howard@iml.lth.se, Sven Axsäter, Johan Marklund
Motivated by collaboration with a global spare parts service provider, we consider
an inventory system consisting of N retailers and a regional support warehouse.
All stock points apply standard (R,Q) replenishment policies. In case of
stock-outs, the retailers receive transshipments from the support warehouse at
an extra cost. We present a model for cost evaluation and optimization of the
reorder points in the system under fill rate constraints. The solution method is
designed to handle large-scale systems and is fast enough to be directly implemented
in practice.
� MA-05
Monday, 11:30-13:00
Meeting Room 104
New Mathematical Paradigms for Service
Science
Stream: Service Science and Sustainability
Invited session
Chair: Eizo Kinoshita, Urban Science Department, Meijo University,
4-3-3 Nijigaoka, 509-0261, Kani, Gifu, Japan,
kinoshit@urban.meijo-u.ac.jp
1 - Why Bubble Economy Occurs and Crashes? –Service
Science for Next Paradigm
Eizo Kinoshita, Urban Science Department, Meijo University,
4-3-3 Nijigaoka, 509-0261, Kani, Gifu, Japan,
kinoshit@urban.meijo-u.ac.jp
This paper shows that there are two different phases in economics.These are
the primal and dual problems.This paper defines the concept of Economic
Growth,Bubble Economy and Destruction of Bubble Economy.And this paper
describes that why bubble economy occurs and bubble economy crashes.In
the process,this paper shows that Primal Economy exists before Bubble Economy
and Dual Economy exists after Destruction of Bubble Economy.And,the
authors proposes that new Primal Economy have new Paradigm which is "Service
Science".
2 - Cloud Computing Service Value Measured Using the
AHP
Norikumo Shunei, Osaka University of Commerce, General
Management Department, 4-1-10, Mikuriyasakae-machi,
577-8505, Higashiosaka, Osaka, Japan,
shunei.norikumo@gmail.com
This study, the cloud is an emerging technology IT investments value measurement
of service delivery from the side of advantages and disadvantages of
cloud. If the company operated in house corporate information assets, and if
the commission cloud. Were analyzed by two layers of cloud are configured.
The first is providing software to SaaS(Software as a Service), services and infrastructure
development and operation of such a PaaS(Platform as a Service).
3 - Improvement of Productivity in Japanese Service Industry
Norihiko Saiga, Urban Science Dept., Meijo University,
Nijigaoka, 4-3-3, 509-0261, Kani, Gihu, Japan,
nsaiga@urban.meijo-u.ac.jp
It is said that the productivity of the service industry of Japan is low, and causes
the global competitiveness decrease in Japan. Therefore, it is a pressing issue
to improve the productivity of the service industry of Japan. I think that it is a
big cause that the current Japanese Government makes efforts to the technology
strengthening of manufacturing, and there were little assistance measures
in the service industry.
4
� MA-06
Monday, 11:30-13:00
Meeting Room 105
Sparse Optimization and Applications
Stream: Non-linear Optimisation
Invited session
Chair: Wotao Yin, Computational and Applied Mathematics Dept.,
Rice University, 6100 Main St, MS-134, 77005, Houston, TX, United
States, wotao.yin@rice.edu
Chair: Yin Zhang, CAAM, Rice University, MS 134, 6100 Main,
77005, Houston, Texas, United States, yzhang@rice.edu
1 - S-Goodness and G-numbers of Linear Transformation
in Low-rank Matrix Recovery
Naihua Xiu, Northern Jiaotong University, Applied Mathematics
Dept., 100044, Beijing, China, naihua_xiu@126.com, Lingchen
Kong, Levent Tencel
In this paper, we extend and characterize the concept of s-goodness for a sensing
matrix in sparse signal recovery to a linear transformation in the low-rank
matrix recovery (LMR). Using two G-numbers of a linear transformation, we
not only show necessary and sufficient conditions for the linear transformation
to be s-good, but also provide sufficient conditions for exact and stable LMR
from the nuclear norm minimization. Moreover, we give computable upper
bounds of G-number. Finally, we establish the connection between restricted
isometry property and s-goodness, and give new bounds for restricted isometry
constant in LMR.
The work was supported in part by the National Natural Science Foundation
of China (10831006) and the National Basic Research Program of China
(2010CB732501), and a Discovery Grant from NSERC.
2 - A Subspace Algorithm for L1 Minimization
Ya-Xiang Yuan, Institute of Computational Mathematics,
Chinese Academy of Sciences, Academy of Mathematics and
Systems Science, Zhong Guan Cun Donglu 55, 100190, Beijing,
China, yyx@lsec.cc.ac.cn
In this talk, a subspace algorithm for L1 minimizaiton is presented. In each
iteration of the algorithm, the new iterate point is found by minimizing a subproblem
defined in a low dimensional subspace, therefore the subspace subproblem
is significantly smaller than the original problem in scale, particularly
for large scale problems. Convergence properties of the new algorithm is given
and some numerical tests are also reported.
3 - Markov Decision Processes under Probability Constraints
Felisa Vazquez-Abad, Computer Science Dept., City University
New York, 695 Park Ave, Room HN1000E, 10065, New York,
United States, felisav@hunter.cuny.edu, Owen Jones, Pierre
Carpentier
We study a controlled Markov process that must be stationary at periodic intervals,
but the stationary failure probability must be small. For example, a battery
controls storage and release of eolian energy every minute, trying to avoid surcharges;
a dam is controlled each day to sell electricity, trying to keep the level
high in Summer for recreational activities. On consecutive days (years) the
battery (dam) state should start with the same distribution. Because the control
and probability constraint act at different time scales, the problem cannot be
solved using existing methods.
4 - Alternating Direction Methods Applied to Sparse Optimization
Problems
Yin Zhang, Dept. of CAAM, Rice University, 6100 Main Street,
Rice University, 77005, Houston, Texas, United States,
yzhang@rice.edu
The classic augmented Lagrangian alternating direction methods (ALADM or
AMD for short) have recently found utilities in solving many sparse optimization
problems arising from signal and image processing including both convex
and non-convex problems. We will introduce such recent applications, and then
report some new convergence results.