Solving Facility Location Problem on Cluster Computing
Computation of facility location problem for every
location in the country is not easy simultaneously. Solving the
problem is described by using cluster computing. A technique is to
design parallel algorithm by using local search with single swap
method in order to solve that problem on clusters. Parallel
implementation is done by the use of portable parallel programming,
Message Passing Interface (MPI), on Microsoft Windows Compute
Cluster. In this paper, it presents the algorithm that used local search
with single swap method and implementation of the system of a
facility to be opened by using MPI on cluster. If large datasets are
considered, the process of calculating a reasonable cost for a facility
becomes time consuming. The result shows parallel computation of
facility location problem on cluster speedups and scales well as
problem size increases.
[1] Anath Granma, Anshul Gupta, George Karypis and vipin Kumar,
"Introduction to Parallel Computing", 2nd ed.., 2003.
[2] Emir Imamagi, Damir Danijel Žagar, "Cluster Distributions Review," in
Department of Computer Systems,University Computing Centre,
Croatia, p. 1.
[3] Fraigniaud, Anne Mignotte, and Yves Robert, editors, Euro-Par '96
Parallel Processing, volume 1 of Lecture Notes in Computer Science, in
Luc Bouge, Springer Verlag, p 128-130., 1996.
[4] Candace Arai Yano, "On the Equivalence of an Equipment
Replcacement Problem and a Facility Location Problem," in department
of Industrial and Operations Engineering, December, 1984.
[5] Hai Jin, Rajkumar Buyya, Mark Baker, "Cluster Computing Tools,
Applications, and Australian Initiatives for Low Cost Supercomputing,"
in School of Computer Science, University of Portsmouth, Portsmouth,
Hants, UK, 2001, p. 2.
[6] E. Lusk. Programming with MPI on clusters. In 3rd IEEE
International Conference on Cluster Computing (CLUSTER-01),
October 2001.
[7] N. Ruest and D. Ruest, Deploying and Managing Microsoft Windows
Compute Cluster Server 2003, p.5-10, November 2005.
[1] Anath Granma, Anshul Gupta, George Karypis and vipin Kumar,
"Introduction to Parallel Computing", 2nd ed.., 2003.
[2] Emir Imamagi, Damir Danijel Žagar, "Cluster Distributions Review," in
Department of Computer Systems,University Computing Centre,
Croatia, p. 1.
[3] Fraigniaud, Anne Mignotte, and Yves Robert, editors, Euro-Par '96
Parallel Processing, volume 1 of Lecture Notes in Computer Science, in
Luc Bouge, Springer Verlag, p 128-130., 1996.
[4] Candace Arai Yano, "On the Equivalence of an Equipment
Replcacement Problem and a Facility Location Problem," in department
of Industrial and Operations Engineering, December, 1984.
[5] Hai Jin, Rajkumar Buyya, Mark Baker, "Cluster Computing Tools,
Applications, and Australian Initiatives for Low Cost Supercomputing,"
in School of Computer Science, University of Portsmouth, Portsmouth,
Hants, UK, 2001, p. 2.
[6] E. Lusk. Programming with MPI on clusters. In 3rd IEEE
International Conference on Cluster Computing (CLUSTER-01),
October 2001.
[7] N. Ruest and D. Ruest, Deploying and Managing Microsoft Windows
Compute Cluster Server 2003, p.5-10, November 2005.
@article{"International Journal of Information, Control and Computer Sciences:50144", author = "Ei Phyo Wai and Nay Min Tun", title = "Solving Facility Location Problem on Cluster Computing", abstract = "Computation of facility location problem for every
location in the country is not easy simultaneously. Solving the
problem is described by using cluster computing. A technique is to
design parallel algorithm by using local search with single swap
method in order to solve that problem on clusters. Parallel
implementation is done by the use of portable parallel programming,
Message Passing Interface (MPI), on Microsoft Windows Compute
Cluster. In this paper, it presents the algorithm that used local search
with single swap method and implementation of the system of a
facility to be opened by using MPI on cluster. If large datasets are
considered, the process of calculating a reasonable cost for a facility
becomes time consuming. The result shows parallel computation of
facility location problem on cluster speedups and scales well as
problem size increases.", keywords = "cluster, cost, demand, facility location", volume = "5", number = "3", pages = "232-5", }