A cross decomposition algorithm for capacitated facility location
Operations Research
Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
Efficient algorithms for the capacitated concentrator location problem
Computers and Operations Research
Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Modern heuristic techniques for combinatorial problems
Improved approximation algorithms for a capacitated facility location problem
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Tabu Search
Peer-to-Peer Membership Management for Gossip-Based Protocols
IEEE Transactions on Computers
Meta-heuristics: The State of the Art
ECAI '00 Proceedings of the Workshop on Local Search for Planning and Scheduling-Revised Papers
Matheuristics: Optimization, Simulation and Control
HM '09 Proceedings of the 6th International Workshop on Hybrid Metaheuristics
Lagrangian heuristic for a class of the generalized assignment problems
Computers & Mathematics with Applications
A Fully Distributed Lagrangean Solution for a Peer-to-Peer Overlay Network Design Problem
INFORMS Journal on Computing
Hybrid metaheuristics in combinatorial optimization: A survey
Applied Soft Computing
Proceedings of the 2010 Summer Computer Simulation Conference
Annals of Mathematics and Artificial Intelligence
Algorithms for nesting with defects
Discrete Applied Mathematics
A Lagrangian heuristic for sprint planning in agile software development
Computers and Operations Research
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Large part of combinatorial optimization research has been devoted to the study of exact methods leading to a number of very diversified solution approaches. Some of those older frameworks can now be revisited in a metaheuristic perspective, as they are quite general frameworks for dealing with optimization problems. In this work, we propose to investigate the possibility of reinterpreting decompositions, with special emphasis on the related Benders and Lagrangean relaxation techniques. We show how these techniques, whose heuristic effectiveness is already testified by a wide literature, can be framed as a "master process that guides and modifies the operations of subordinate heuristics", i.e., as metaheuristics. Obvious advantages arise from these approaches, first of all the runtime evolution of both upper and lower bounds to the optimal solution cost, thus yielding both a high-quality heuristic solution and a runtime quality certificate of that same solution.