Vehicle routing problems (VRP) have been widely studied in literature. Heuristics as well as exact algorithms have been applied to solve this kind of problems. In this study we approximately solve the VRP with simultaneous pickup and delivery and time windows by means of two well-known heuristics namely Tabu Search and Simulated Annealing. We compare the obtained results and then propose a restoration technique that allows both Tabu Search and Simulated Annealing to better explore the solution space. Results show that the proposed restoration technique allows both heuristic algorithms to obtain better results.

Abstract:A common problem when using (meta)-heuristic techniques to solve complex combinatorial optimization problems is related to parameters tuning. Finding "the right" parameter values can lead to significant improvements in terms of best solution objective value found by the heuristic, heuristic reliability and heuristic convergence, among others. Unfortunately, this is usually a tedious and complicated task if done manually. In this paper, we propose a framework that is based on Genetic Programming to fine-tune a key parameter of the well-known Simulated Annealing (SA) algorithm. Experiments on a set of small instances of the Facility Location Problem with capacity constraints are performed. Results show that automatically adjusting a key parameter in SA by means of Genetic Programming leads to an average value of the obtained solution that is closer to the optimal solution than the average value obtained by the simple SA algorithm with a priori selected values. More important, standard deviation of the algorithm is greatly improved by our approach which makes it much more reliable if time limitations are imposed.