Emergent cooperative goal-satisfaction in large-scale automated-agent systems
Artificial Intelligence
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
Solving a Supply Chain Optimization Problem Collaboratively
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Integrating local search and network flow to solve the inventory routing problem
Eighteenth national conference on Artificial intelligence
An asynchronous complete method for distributed constraint optimization
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Multiagent diffusion and distributed optimization
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Performance models for large scale multiagent systems: using distributed POMDP building blocks
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Coalition Formation for Large-Scale Electronic Markets
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Journal of Global Optimization
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In this paper, we propose a multi-agent approach for solving a class of optimization problems involving expensive resources, where monolithic local search schemes perform miserably. More specifically, we study the class of bin-packing problems. Under our proposed Fine-Grained Agent System scheme, rational agents work both collaboratively and selfishly based on local search and mimic physics-motivated systems. We apply our approach to a generalization of bin-packing - the Inventory Routing Problem with Time Windows - which is an important logistics problem, and demonstrate the efficiency and effectiveness of our approach.