Lagrangean decomposition: A model yielding stronger lagrangean bounds
Mathematical Programming: Series A and B
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
An asynchronous complete method for distributed constraint optimization
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
The distributed breakout algorithms
Artificial Intelligence - Special issue: Distributed constraint satisfaction
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
DisLRPɑ: ɑ-approximation in generalized mutual assignment
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Adaptive price update in distributed Lagrangian relaxation protocol
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
An α-approximation protocol for the generalized mutual assignment problem
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A hybrid solution to collaborative decision-making in a decentralized supply-chain
Journal of Engineering and Technology Management
PRIMA'11 Proceedings of the 14th international conference on Agents in Principle, Agents in Practice
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We present a new formulation of distributed task assignment, called Generalized Mutual Assignment Problem (GMAP), which is derived from an NP-hard combinatorial optimization problem that has been studied for many years in the operations research community. To solve the GMAP, we introduce a novel distributed solution protocol using Lagrangian decomposition and distributed constraint satisfaction, where the agents solve their individual optimization problems and coordinate their locally optimized solutions through a distributed constraint satisfaction technique. Next, to produce quick agreement between the agents on a feasible solution with reasonably good quality, we provide a parameter that controls the range of "noise" mixed with an increment/decrement in a Lagrange multiplier. Our experimental results indicate that the parameter may allow us to control tradeoffs between the quality of a solution and the cost of finding it.