A Decision Support System for Logistics Operations
This paper describes an Artificial Intelligence based application for a logistic company that solves the problem of grouping by zones the packages that have to be delivered and propose the routes that the drivers should follow. The tool combines from the one hand, Case-Based Reasoning techniques to separate and learn the most frequent areas or zones that the experienced logistic operators do. These techniques allow the company to separate the daily incidents that generate noise in the routes, from the decision made based on the knowledge of the route. From the other hand, we have used Evolutionary Computation to plan optimal routes from the learning areas and evaluate those routes. The application allows the users to decide under what parameters (i.e. distance, time, etc) the route should be optimized.
KeywordsGenetic Algorithm Geographic Information System Decision Support System Postal Code Evolutionary Computation
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- 1.Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. Artificial Intelligence Communications 7(1), 39–52 (1994)Google Scholar
- 2.Bäck, T., Fogel, D.B., Michalewicz, Z.: Permutations. In: Evolutionary Computation 1. Basic Algorithms and Operators, pp. 139–149. Institute of Physics Publishing (1984)Google Scholar
- 3.Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2009)Google Scholar
- 4.Holland, J.H.: Adaptation in natural and artificial systems. MIT Press, Cambridge (1992)Google Scholar
- 5.Sengoku, H., Yoshihara, I.: A fast tsp solver using ga on java. In: 3rd AROB (1998)Google Scholar