Open Access Open Access  Restricted Access Subscription Access

Facility Location in Logistic Network Design Using Soft Computing Optimization Models


Affiliations
1 Bharathiar University, Coimbatore, Tamilnadu, India
2 Govt. College, Nedumangadu, Thiruvananthapuram, Kerala, India
 

Discovery of the optimal best possibility of location for facilities is the central problem associated in logistics management. The optimal places for the distribution centres (DCs) can be based on the selected attributes that are crucial to locate the best possible locations to increase the speed of the facility service and thus reduce the overall transport cost and time and to provide best service. The major task is to identifying and locating the required number of DCs and its optimum locations are considered as the important goals for the design of any logistics network. The number of DCs will clearly depends upon many factors like population, capacity of the facility, type of facility etc. but locating the optimum locations of DCs will reduce the overall cost. But, for solving such a wide problem space, the powerful tools are the soft computing based approaches and that are well suited and find a meaningful solution in finite time. In this work, we are going to find the optimum locations of DCs for logistics using various soft computing methods.

Keywords

Logistic, Heuristic, Hybrid, Inbounded, Crossover, Mutation, Simulated, Annealing, Direct Search.
User
Notifications
Font Size

  • Erlenkotter, D., "A Dual-Based Procedure for the Uncapacitated Facility Location." Operations Research, Vol 26(6), pg. 992-1009, 1978.
  • Francis, R.L., McGinnis, F.L., Jr., White, J.A., "Facility Layout and Location: An Analytical Approach." Prentice Hall, 2nd Ed., 1974.
  • 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
  • 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
  • Michael J. Bucci, Michael G. Kay, Donald P. Warsing†, Jeffrey A. Joines, "Metaheuristics for Facility Location Problems with Economies of Scale", IIE Transactions
  • 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”
  • 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.
  • . 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, 444460.
  • 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.
  • Cooper, L., 1963, Location-allocation problems, Operations Research, 11, 331-343.
  • CARE (Carpet America Recovery Effort) annual report, 2007, http://www.carpetrecovery.org/pdf/annual_report/07_CARE-annual-rpt.pdf
  • CARE(Carpet America Recovery Effort) network website, 2009, http://www.carpetrecovery.org/pdf/reclamation_centers/Carpet_Reclamation_Center s.pdf
  • Daskin, M.S.,1995, Network and discrete location: models, algorithms, and applications, John Wiley and Sons, New York.
  • de Brito, M.P., Dekker, R., Flapper, S.D.P., 2003, Reverse Logistics - a review of case studies, ERIM Report Series.
  • Fleishchmann, M., Krikke, H.R., Dekker, R., Flapper, S.D.P., 2000, A characterization of logistics networks for product recovery, Omega, 28, 653-666.
  • 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,
  • Mirchandani, P.B., Francis, R.L., 1990, Discrete Location Theory, Wiley, NewYork.
  • Realff, M.J., Ammons, J.C., Newton, D., 1999, Carpet Recycling: Determining the 38:3, 547-567.
  • Realff, M., Systems Planning for Carpet Recycling. 2006, Recycling in Textiles, Editor Youjiang Wang, CRC Press.
  • Repa, E.W., 2005, NSWMA's 2005 Tip Fee Survey, National Solid Wastes Management Association Website,://wastec.isproductions.net/webmodules/webarticles/articlefiles/478Tipping%20Fee%20Bulletin%202005.pdf
  • 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, 2015, ISSN 0973-4562.
  • Shaju Varghese, Gladston Raj S, “Simulated Annealing and Direct Search Based Optimization Models for Facility Location in Logistic Network Design” International Journal of Computer Applications, Vol 132, pp- 31-37, December 2015, ISSN: 0975-8887

Abstract Views: 337

PDF Views: 153




  • Facility Location in Logistic Network Design Using Soft Computing Optimization Models

Abstract Views: 337  |  PDF Views: 153

Authors

Shaju Varughese
Bharathiar University, Coimbatore, Tamilnadu, India
S. Gladston Raj
Govt. College, Nedumangadu, Thiruvananthapuram, Kerala, India

Abstract


Discovery of the optimal best possibility of location for facilities is the central problem associated in logistics management. The optimal places for the distribution centres (DCs) can be based on the selected attributes that are crucial to locate the best possible locations to increase the speed of the facility service and thus reduce the overall transport cost and time and to provide best service. The major task is to identifying and locating the required number of DCs and its optimum locations are considered as the important goals for the design of any logistics network. The number of DCs will clearly depends upon many factors like population, capacity of the facility, type of facility etc. but locating the optimum locations of DCs will reduce the overall cost. But, for solving such a wide problem space, the powerful tools are the soft computing based approaches and that are well suited and find a meaningful solution in finite time. In this work, we are going to find the optimum locations of DCs for logistics using various soft computing methods.

Keywords


Logistic, Heuristic, Hybrid, Inbounded, Crossover, Mutation, Simulated, Annealing, Direct Search.

References