A cross decomposition algorithm for capacitated facility location
Operations Research
Analysis of a local search heuristic for facility location problems
Journal of Algorithms
Core Problems in Knapsack Algorithms
Operations Research
Improved approximation algorithms for capacitated facility location problems
Mathematical Programming: Series A and B
A cutting plane algorithm for the capacitated facility location problem
Computational Optimization and Applications
An effective heuristic for large-scale capacitated facility location problems
Journal of Heuristics
Kernel search: A general heuristic for the multi-dimensional knapsack problem
Computers and Operations Research
Kernel Search: a new heuristic framework for portfolio selection
Computational Optimization and Applications
A tabu search heuristic procedure for the capacitated facility location problem
Journal of Heuristics
Near-optimal solutions to large-scale facility location problems
Discrete Optimization
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The Capacitated Facility Location Problem (CFLP) is among the most studied problems in the OR literature. Each customer demand has to be supplied by one or more facilities. Each facility cannot supply more than a given amount of product. The goal is to minimize the total cost to open the facilities and to serve all the customers. The problem is $\mathcal{NP}$ -hard. The Kernel Search is a heuristic framework based on the idea of identifying subsets of variables and in solving a sequence of MILP problems, each problem restricted to one of the identified subsets of variables. In this paper we enhance the Kernel Search and apply it to the solution of the CFLP. The heuristic is tested on a very large set of benchmark instances and the computational results confirm the effectiveness of the Kernel Search framework. The optimal solution has been found for all the instances whose optimal solution is known. Most of the best known solutions have been improved for those instances whose optimal solution is still unknown.