Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
The traveling salesman: computational solutions for TSP applications
The traveling salesman: computational solutions for TSP applications
Hybrid metaheuristics in combinatorial optimization: A survey
Applied Soft Computing
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The problem of allocating a set of facilities in order to maximise the sum of the demands of the covered clients is known as the maximal covering location problem. In this work we tackle this problem by means of iterated greedy algorithms. These algorithms iteratively refine a solution by partial destruction and reconstruction, using a greedy constructive procedure. Iterated greedy algorithms have been applied successfully to solve a considerable number of problems. With the aim of providing additional results and insights along this line of research, this paper proposes two new iterated greedy algorithms that incorporate two innovative components: a population of solutions optimised in parallel by the iterated greedy algorithm, and an improvement procedure that explores a large neighbourhood by means of an exact solver. The benefits of the proposal in comparison to a recently proposed decomposition heuristic and a standalone exact solver are experimentally shown.