Challenger: a multi-agent system for distributed resource allocation
AGENTS '97 Proceedings of the first international conference on Autonomous agents
A hybrid heuristic to solve a task allocation problem
Computers and Operations Research
Improved Algorithms for Optimal Winner Determination in Combinatorial Auctions and Generalizations
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
On multi-robot task allocation
On multi-robot task allocation
Traderbots: a new paradigm for robust and efficient multirobot coordination in dynamic environments
Traderbots: a new paradigm for robust and efficient multirobot coordination in dynamic environments
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Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
An auction-based approach to complex task allocation for multirobot teams
An auction-based approach to complex task allocation for multirobot teams
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
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IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
An implementation of the contract net protocol based on marginal cost calculations
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Task allocation in mesh connected processors with local search meta-heuristic algorithms
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Complex Task Allocation in Mobile Surveillance Systems
Journal of Intelligent and Robotic Systems
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This paper presents a comparative study between optimization-based and market-based approaches used for solving the Multirobot task allocation (MRTA) problem that arises in the context of multirobot systems (MRS). The two proposed approaches are used to find the optimal allocation of a number of heterogeneous robots to a number of heterogeneous tasks. The two approaches were extensively tested over a number of test scenarios in order to test their capability of handling complex heavily constrained MRS applications that include extended number of tasks and robots. Finally, a comparative study is implemented between the two approaches and the results show that the optimization-based approach outperforms themarket-based approach in terms of optimal allocation and computational time.