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
The distributed breakout algorithms
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Agent-Based Dantzig-Wolfe Decomposition
KES-AMSTA '09 Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
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We present a new formulation of distributed task assignment, Generalized Mutual Assignment Problem (GMAP), which is derived from a typical NP-hard combinatorial optimization problem that has been studied for many years mainly in the operations research community. Then, to solve the GMAP, we introduce a novel distributed solution protocol using Lagrangean 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 increment/decrement in a Lagrangean multiplier and report our experimental results indicating that the parameter may allow us to control tradeoffs between the quality of a solution and the cost of finding it.