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Title: AI Planning for Transportation Logistics
Author(s): I. Refanidis, N. Bassiliades, I. Vlahavas.
Keywords: Transportation Logistics, Artificial Intelligence, Planning, Heuristic Search, Multiple Criteria.
Appeared in: Proc. 17th International Logistics Conference, pp. 241-248, Thessaloniki, October 2001, 2001.
Abstract: In the last decade the efficiency of the Artificial Intelligence Planning Systems has been increased significantly. New systems appeared that are able to cope with planning problems being orders of magnitude more complex than the ones solvable in early 90's. This vast improvement increase was made possible mainly by three new approaches in plan generation: planning graphs, satisfiability planning and heuristic state-space planning. The latter approach, which is the most powerful one, derives a heuristic function from the specification of a planning problem, independently of its domain, and uses it for guiding the search through the space of the states. During the last years appeared many heuristic state-space planners, such as ASP, HSP, GRT and FF, which were able to solve large transportation logistics problems, with numerous locations, trucks and objects that have to be transferred, very efficiently, as it has been shown in the recent international planning competitions. This paper briefly presents the current status in domain-independent heuristic state-space planning and concentrates on the GRT and MO-GRT planners, where the latter is a recent extension of GRT being able to consider multiple criteria in the plan generation and evaluation process. Finally, the paper outlines results of running MO-GRT in some transportation logistics problems and poses directions for future research.
See also :

        This paper has been cited by the following:

1 Jiangzhihua Jiangyunfei, "Bring to a value of planning algorithm: A weighted algorithm of Graphplan", Computer applications and software, Vol. 22, No. 5, pp. 16-18, 2005.
2 Don Perugini, "Agents for Logistics: A Provisional Agreement Approach", PhD Thesis, Department of Computer Science and Software Engineering, The University of Melbourne, Victoria, Australia, February 2006