Human Drivers Knowledge Integration in a Logistics Decision Support Tool
The logistics sector is a very dynamic and complex domain with large impact in our day-to-day life. It is also very sensitive to the market changes, so tools that can improve their processes are needed. This paper describes a decision support tool for a Spanish Logistics Company. The tool takes the knowlege from the human drivers, i.e. the address used to deliver a package, integrates and exploits it into the decision process. However, this is precisely the data that is often wrongly introduced by the drivers. The tool analyses and detects different mistakes (i.e. misspellings) in the addresses introduced, groups by zones the packages that have to be delivered, and proposes the routes that the drivers should follow. To achieve this, the tool combines three different techniques from Artificial Intelligence. First, Data Mining techniques are used to detect and correct addresses inconsistencies. Second, Case-Based Reasoning techniques that are employed to separate and learn the most frequent areas or zones that the experienced drivers do. And finally, we use Evolutionary Computation (EC) to plan optimal routes (using the human drivers past experiences) from the learned areas, and evaluate those plans against the original route executed by the human driver. Results show that the tool can automatically correct most of the misspelling in the data.
KeywordsGeographic Information System Travel Salesman Problem Optimal Route Case Base Reasoning Data Mining Technique
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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.Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2009)Google Scholar
- 3.Fellegi, I.P., Sunter, A.B.: A theory for record linkage. Journal of the American Statistical Association (1969)Google Scholar
- 4.Holland, J.H. (ed.): Adaptation in natural and artificial systems. MIT Press, Cambridge (1992)Google Scholar
- 6.Levenshtein, V.: Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady (1966)Google Scholar
- 7.Monge, A., Elkan, C.: An efficient domain-independent algorithm for detecting approximately duplicate database records. In: Proceedings of the SIGMOD Workshop Data Mining and Knowledge Discovery, pp. 267–270. ACM, New York (1997)Google Scholar
- 8.Newcombe, H., Kennedy, J., Axford, S.J., James, A.P.: Automatic linkage of vital records. Science (1959)Google Scholar
- 13.Winkler, W.: The state of record linkage and current research problems. Statistics of Income Division, Internal Revenue Service (1999)Google Scholar