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An improved Benders decomposition algorithm for the logistics facility location problem with capacity expansions

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  • Lixin Tang
  • Wei Jiang
  • Georgios Saharidis

Abstract

We investigate a logistics facility location problem to determine whether the existing facilities remain open or not, what the expansion size of the open facilities should be and which potential facilities should be selected. The problem is formulated as a mixed integer linear programming model (MILP) with the objective to minimize the sum of the savings from closing the existing facilities, the expansion costs, the fixed setup costs, the facility operating costs and the transportation costs. The structure of the model motivates us to solve the problem using Benders decomposition algorithm. Three groups of valid inequalities are derived to improve the lower bounds obtained by the Benders master problem. By separating the primal Benders subproblem, different types of disaggregated cuts of the primal Benders cut are constructed in each iteration. A high density Pareto cut generation method is proposed to accelerate the convergence by lifting Pareto-optimal cuts. Computational experiments show that the combination of all the valid inequalities can improve the lower bounds significantly. By alternately applying the high density Pareto cut generation method based on the best disaggregated cuts, the improved Benders decomposition algorithm is advantageous in decreasing the total number of iterations and CPU time when compared to the standard Benders algorithm and optimization solver CPLEX, especially for large-scale instances. Copyright Springer Science+Business Media, LLC 2013

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  • Lixin Tang & Wei Jiang & Georgios Saharidis, 2013. "An improved Benders decomposition algorithm for the logistics facility location problem with capacity expansions," Annals of Operations Research, Springer, vol. 210(1), pages 165-190, November.
  • Handle: RePEc:spr:annopr:v:210:y:2013:i:1:p:165-190:10.1007/s10479-011-1050-9
    DOI: 10.1007/s10479-011-1050-9
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    14. Keyvanshokooh, Esmaeil & Ryan, Sarah M. & Kabir, Elnaz, 2016. "Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition," European Journal of Operational Research, Elsevier, vol. 249(1), pages 76-92.
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    22. M. Jenabi & S. Fatemi Ghomi & S. Torabi & S. Hosseinian, 2015. "Acceleration strategies of Benders decomposition for the security constraints power system expansion planning," Annals of Operations Research, Springer, vol. 235(1), pages 337-369, December.
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    25. N. Beheshti Asl & S. A. MirHassani, 2019. "Accelerating benders decomposition: multiple cuts via multiple solutions," Journal of Combinatorial Optimization, Springer, vol. 37(3), pages 806-826, April.

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