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Efficient metaheuristics to solve the intermodal terminal location problem


  • SÖRENSEN, Kenneth
  • VANOVERMEIRE, Christine
  • BUSSCHAERT, Sylvie


Determining the optimal number and location of intermodal transshipment terminals is a decision that strongly influences the viability of the intermodal transportation alternative. In this paper, we develop a model and an optimization method that provides policy makers with a tool to help them take these decisions. The objective of the terminal location problem described in this paper is to determine which of a set of potential terminal locations to use and which not and how to route the supply and demand of a set of customers (representing zones of supply and demand) through the network (by both uni- and intermodal transport) so as to minimize the total cost. We develop two different metaheuristic procedures that both consist of two phases: a solution construction phase and a solution improvement phase. The first metaheuristic constructs solutions using a GRASP procedure, the second one uses the relatively unknown attribute based hill climber (ABHC) heuristic. Innovative in our approach is the integration of a fast heuristic procedure to approximate the total cost given the set of open terminals. Both metaheuristics are compared to the results of an MIP solver. A thorough performance assessment uncovers that both metaheuristics generate close-to-optimal solutions in very short computing times. An argument in favor of the ABHC approach is that it is parameter-free and hence more transparent and likely to be accepted in a business or policy environment.

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  • SÖRENSEN, Kenneth & VANOVERMEIRE, Christine & BUSSCHAERT, Sylvie, 2012. "Efficient metaheuristics to solve the intermodal terminal location problem," Working Papers 2012001, University of Antwerp, Faculty of Applied Economics.
  • Handle: RePEc:ant:wpaper:2012001

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    References listed on IDEAS

    1. MACHARIA, Harrison & GOOS, Peter, 2010. "D-optimal and D-efficient equivalent-estimation second-order split-plot designs," Working Papers 2010011, University of Antwerp, Faculty of Applied Economics.
    2. SCHOEN, Eric D. & JONES, Bradley & GOOS, Peter, 2010. "Split-plot experiments with factor-dependent whole-plot sizes," Working Papers 2010001, University of Antwerp, Faculty of Applied Economics.
    3. Arnouts, Heidi & Goos, Peter, 2010. "Update formulas for split-plot and block designs," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3381-3391, December.
    4. Bradley Jones & Peter Goos, 2007. "A candidate-set-free algorithm for generating "D"-optimal split-plot designs," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(3), pages 347-364.
    5. Peter Goos, 2006. "Optimal versus orthogonal and equivalent-estimation design of blocked and split-plot experiments," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 60(3), pages 361-378.
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    1. repec:kap:netspa:v:17:y:2017:i:2:d:10.1007_s11067-016-9331-0 is not listed on IDEAS

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    Intermodal transportation; Terminal location; Metaheuristics;

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