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Reseach Article

Simulated Annealing and Direct Search based Optimization Models for Facility Location in Logistic Network Design

by Shaju Varughese, Gladston Raj S.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 132 - Number 8
Year of Publication: 2015
Authors: Shaju Varughese, Gladston Raj S.
10.5120/ijca2015907508

Shaju Varughese, Gladston Raj S. . Simulated Annealing and Direct Search based Optimization Models for Facility Location in Logistic Network Design. International Journal of Computer Applications. 132, 8 ( December 2015), 31-37. DOI=10.5120/ijca2015907508

@article{ 10.5120/ijca2015907508,
author = { Shaju Varughese, Gladston Raj S. },
title = { Simulated Annealing and Direct Search based Optimization Models for Facility Location in Logistic Network Design },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 132 },
number = { 8 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 31-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume132/number8/23616-2015907508/ },
doi = { 10.5120/ijca2015907508 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:29:49.566687+05:30
%A Shaju Varughese
%A Gladston Raj S.
%T Simulated Annealing and Direct Search based Optimization Models for Facility Location in Logistic Network Design
%J International Journal of Computer Applications
%@ 0975-8887
%V 132
%N 8
%P 31-37
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Finding the optimum location of facilities is an important problem related in logistics. Locations of Distribution Centers (DCs) can be at the proximity of highways and closer to populated areas in order to speedup package deliveries and minimize the overall transport cost and time. Deciding the optimum number of DCs as well as the optimum location of DCs are the most important aspects of the design of any logistics network. Deciding the number of DCs will depend upon the budget and optimum locations of DCs will reduce the overall transport cost. As operations globalize, location decisions become more complex. A powerful approach to analyzing these problems is the transportation method of linear programming. The linear programming based methods will take much time to attain a solution in such a wide problem space. But for dealing with such wide problem space, soft computing based approaches are well suited and can find a meaningful solution in finite time. This work generate the Simulated Annealing and Direct Search based model for facility location in logistics and evaluate its performance. Also, compare the performance of this model with a k-mean clustering based model.

References
  1. Erlenkotter, D., "A Dual-Based Procedure for the Uncapacitated Facility Location." Operations Research, Vol 26(6), pg. 992-1009, 1978.
  2. Francis, R.L., McGinnis, F.L., Jr., White, J.A., "Facility Layout and Location: An Analytical Approach." Prentice Hall, 2nd Ed., 1974.
  3. Juliana Karakaneva, "A LOCATION PROBLEM MODELING AND SOLVING", Trakia Journal of Sciences, Vol 1, No 4, pp 1-7, 2003,ISSN 1312-1723, Copyright © 2003 Trakia University
  4. HK Smith, G Laporte and PR Harper, "Locational analysis: highlights of growth to maturity", http://eprints.soton.ac.uk/68930/1/Locational_Analysis_-_Smith,_Laporte_and_Harper.doc
  5. Michael J. Bucci, Michael G. Kay, Donald P. Warsing†, Jeffrey A. Joines, "Metaheuristics for Facility Location Problems with Economies of Scale", IIE Transactions
  6. Michael J. Bucci, Ryan Woolard, Jeffrey Joines, Kristin Thoney, Russell E. King, "An Application of Heuristics Incorporating Economies of Scale to Facility Location Problems in Carpet Recycling”
  7. .Biehl, M., Prater, E., Realff, M.J., 2007, Assessing performance and uncertainty in developing carpet reverse logistics systems, Computers and Operations Research, 34, 443-463.
  8. .Brimberg, J.,Hansen, P., Mladenovic, N.,Taillard, E.D. ,2000, Improvements and Comparison of Heuristics for Solving the Uncapacitated Multisource Weber Problem, Operations Research, 48, 444-460.
  9. .Bucci, M.J., Kay, M.G., Warsing, D.P., Joines, J.A., 2009, Metaheuristics for Facility Location with Economies of Scale, Fitts Department of Industrial and Systems Engineering working paper, North Carolina State University, Raleigh NC.
  10. Cooper, L., 1963, Location-allocation problems, Operations Research, 11, 331-343.
  11. CARE (Carpet America Recovery Effort) annual report, 2007, http://www.carpetrecovery.org/pdf/annual_report/07_CARE-annual-rpt.pdf
  12. CARE (Carpet America Recovery Effort) network website,2009, http://www.carpetrecovery.org/pdf/reclamation_centers/Carpet_Reclamation_Center s.pdf
  13. Daskin, M.S., 1995, Network and discrete location: models, algorithms, and applications, John Wiley and Sons, New York.
  14. De Brito, M.P., Dekker, R., Flapper, S.D.P., 2003, Reverse Logistics - a review of case studies, ERIM Report Series.
  15. Fleishchmann, M., Krikke, H.R., Dekker, R., Flapper, S.D.P., 2000, A characterization of logistics networks for product recovery, Omega, 28, 653-666.
  16. Louwers, D., Kip, B.J., Peters, E., Souren, F., Flapper, S.D.P., 1999, A facility location allocation model for reusing carpet, Computers and Industrial Engineering,
  17. Mirchandani, P.B., Francis, R.L., 1990, Discrete Location Theory, Wiley, NewYork.
  18. Realff, M.J., Ammons, J.C., Newton, D., 1999, Carpet Recycling: Determining the 38:3, 547-567.
  19. Realff, M., Systems Planning for Carpet Recycling. 2006, Recycling in Textiles, Editor Youjiang Wang, CRC Press.
  20. Shaju Varghese, Gladston Raj S, “A Genetic Algorithm Based Optimization Model for Facility Location in Logistic Network Design”, International Journal of Applied Engineering Research” Vol 10, No. 69, pp-338-344, 2005, ISSN 0973-4562.
  21. AUTHORS PROFILE
  22. Mr. Shaju Varghese received his M.Sc. (Maths), M.C.A., and M.Phil. in computer Science. Now working as Head of the Department of Computer Science at Baselios Poulose II Catholicos (B. P. C ) College, Piravom, Kerala, India. He was the Principal Investigator of the Minor Research Project "Computerized Facility Location Analysis In Rural Area Using Clustering", 2010, funded by Universities Grant Commission, India. His research interest includes Data Mining, Facility Location Problem, and Cyber Cri
  23. Dr. Gladston Raj S. received his M.Sc (CS), M.Tech (Image Computing) and PhD in Computer Science from University of Kerala and Completed UGC-NET from University of Kerala and PGDCH (Computer hardware) from MicroCode, He is Now working as Head of the Department of Computer Science at Govt. College Nedumangad, Kerala, India. His area of interest includes Image Processing, Signal Processing, Datamining. He is providing research guidance for Ph.D scholars from different areas of research and has pre
Index Terms

Computer Science
Information Sciences

Keywords

Optimization clustering logistics Euclidean distance annealing K-means Direct search maximizing minimizing facility location