Iterated Greedy Algorithms for the Maximal Covering Location Problem
- 1.4k Downloads
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.
Keywordsiterated greedy algorithm large neighbourhood search maximal covering location problem
Unable to display preview. Download preview PDF.
- 1.IBM ILOG CPLEX optimizer (November 2011), http://www-01.ibm.com/software/integration/optimization/cplex-optimizer/
- 2.Arakaki, R.G.I., Lorena, L.A.N.: A constructive genetic algorithm for the maximal covering location problem. In: Proceedings of the 4th Metaheuristics International Conference (MIC 2001), pp. 13–17 (2001)Google Scholar
- 5.Culberson, J.C., Luo, F.: Exploring the k-colorable landscape with iterated greedy. Dimacs Series in Discrete Mathematics and Theoretical Computer Science, pp. 245–284. American Mathematical Society (1996)Google Scholar
- 8.Lorena, L.A., Pereira, M.A.: A lagrangean/surrogate heuristic for the maximal covering location problem using hillsman’s edition. International Journal of Industrial Engineering 9, 57–67 (2001)Google Scholar
- 10.Reinelt, G.: The traveling salesman: computational solutions for TSP applications. Springer, Heidelberg (1994)Google Scholar
- 13.Senne, E.L.F., Pereira, M.A., Lorena, L.A.N.: A decomposition heuristic for the maximal covering location problem. Advances in Operations Research 2010 (2010)Google Scholar
- 14.Xia, L., Xie, M., Xu, W., Shao, J., Yin, W., Dong, J.: An empirical comparison of five efficient heuristics for maximal covering location problems. In: IEEE/INFORMS International Conference on Service Operations, Logistics and Informatics (SOLI 2009), pp. 747–753 (2009)Google Scholar