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Adaptation of the GRASP Algorithm to Solve a Multiobjective Problem Using the Pareto Concept

Luis G. Guzmán, Alix S. Gómez, Carlos J. Ardila, Daladier Jabba


This paper introduces a probabilistic adaptation of the algorithm GRASP (Greedy Randomize Adaptive Search Procedure), in the solution of a bi-objective transportation problem. The purpose of the paper is to minimize the cost of storing and sending products, and maximize the client satisfying level by the existence of some nodes (warehouses) in a network that not only receive but also distribute products. A scenario was created to test an exhaustive searching algorithm with networks of 200, 400 and 600 nodes, to determine the three warehouses that optimize the objectives mentioned before. The proposed algorithm was evaluated with the same scenario observing a better performance in terms of execution time, and qualitative and quantitative solutions closed to the one obtained by the exhaustive searching algorithm. Novelties in this paper are first the adaptation of the GRASP algorithm in a bi-objective problem using the Pareto optimization criteria, and second to apply the proposed algorithm not in the process of searching paths as usually have been used but to optimize others criteria.


GRASP algorithm, multiobjective problem, transportation and storage problem, Pareto optimization criteria, exhaustive searching algorithm.

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