A Hybrid Method for Solving Large-Scale Supply Chain Problems

  • Steffen Wolf
  • Peter Merz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4446)


The strategic supply chain design problem which allows capacity shifts and budget limitations can be formulated as a linear program. Since facilities are allowed to be opened or shut down during the planning horizon, this problem is in fact a mixed integer problem. Choosing the optimal set of facilities to serve the customer demands is an NP-hard combinatorial optimization problem. We present a hybrid method combining an evolutionary algorithm and LP based solvers for solving large-scale supply chain problems, which takes its power from filtering out infeasible solutions. The EA incorporating these filters is shown to be faster than the MIP solver ILOG CPLEX in most of the considered instances. For the remaining instances it finds feasible solutions much faster than the MIP solver.


Evolutionary Algorithm Penalty Cost Facility Location Problem ILOG CPLEX Feasible Combination 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Nemhauser, G.L., Wolsey, L.A.: Integer and Combinatorial Optimization. John Wiley & Sons, New York (1988)zbMATHGoogle Scholar
  2. 2.
    Resende, M.G.C., Werneck, R.F.: A hybrid multistart heuristic for the uncapacitated facility location problem. European Journal of Operational Research 174, 54–68 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Zhang, J.: Approximating the two-level facility location problem via a quasi-greedy approach. In: SODA’04: Proceedings of the 15th annual ACM-SIAM symposium on Discrete algorithms, Philadelphia, PA, USA, pp. 808–817. Society for Industrial and Applied Mathematics (2004)Google Scholar
  4. 4.
    ILOG S.A.: ILOG CPLEX User’s Manual. Gentilli, France (2006),
  5. 5.
    Melo, M.T., Nickel, S., da Gama, F.S.: Dynamic multi-commodity capacitated facility location: A mathematical modeling framework for strategic supply chain planning. Computers & Operations Research 33, 181–208 (2006)zbMATHCrossRefGoogle Scholar
  6. 6.
    Cornuéjols, G.P., Nemhauser, G.L., Wolsey, L.A.: The uncapacitated facility location problem. In: Mirchandani, P.B., Francis, R.L. (eds.) Discrete Location Theory, pp. 119–171. Wiley, New York (1990)Google Scholar
  7. 7.
    Velásquez, R., Melo, M.T.: Solving a large-scale dynamic facility location problem with variable neighbourhood and token ring search. In: Proceedings of the 39th ORSNZ Conference, Auckland, NZ (2004)Google Scholar
  8. 8.
    Melo, M.T., Nickel, S., da Gama, F.S.: Large-scale models for dynamic multi-commodity capacitated facility location. Technical Report 58, Fraunhofer Institut for Industrial Mathematics (ITWM), Kaiserslautern, Germany (2003), Available at

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Steffen Wolf
    • 1
  • Peter Merz
    • 1
  1. 1.Distributed Algorithms Group, University of KaiserslauternGermany

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