Algorithms for clustering data
Algorithms for clustering data
Exploiting meta-level information in a distributed scheduling system
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
A decision-theoretic model for cooperative transportation scheduling
MAAMAW '96 Proceedings of the 7th European workshop on Modelling autonomous agents in a multi-agent world : agents breaking away: agents breaking away
Resource Allocation in Distributed Factory Scheduling
IEEE Expert: Intelligent Systems and Their Applications
Pickup and Delivery with Time Windows: Algorithms and Test Case Generation
ICTAI '01 Proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence
MACS-VRPTW: A Multiple Ant Colony System for Vehicle Routing Problems with Time Windows
MACS-VRPTW: A Multiple Ant Colony System for Vehicle Routing Problems with Time Windows
The focussed D* algorithm for real-time replanning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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In this paper, we propose a practical method for solving the delivery-scheduling problem and discuss its implementation. The method is based on the cooperative problem solving with multiple agents. In the truck delivery scheduling method, the covered region is partitioned into multiple subregions and each sub-region is assigned a sub-problem solving agent. For integrating those sub-problem solving agents, an integration-and-evaluation agent solves the total problem. We also discuss the functions for building cooperative decision support system in a mobile environment in delivery scheduling domain. We consider a delivery center with function, i.e., generating and integrating delivery schedule, acquiring and managing the information shared commonly by all delivery persons, and dispatching the selected information to delivery persons, and the mobile terminal that a delivery person uses for exchanging information with the center. By employing the multi-agent problem solving framework for the delivery scheduling problem, we achieved an easy incorporation of various evaluation parameters in the process of scheduling, efficient use and management of scheduling knowledge of various levels.