Minimizing total tardiness on one machine is NP-hard
Mathematics of Operations Research
Performance Evaluation of a Firewall-Compliant Globus-Based Wide-Area Cluster System
HPDC '00 Proceedings of the 9th IEEE International Symposium on High Performance Distributed Computing
A Problem-Specific Fault-Tolerance Mechanism for Asynchronous, Distributed Systems
ICPP '00 Proceedings of the Proceedings of the 2000 International Conference on Parallel Processing
High-Performance Parallel and Distributed Computing for the BMI Eigenvalue Problem
IPDPS '02 Proceedings of the 16th International Symposium on Parallel and Distributed Processing
A Case Study in Running a Parallel Branch and Bound Application on the Grid
SAINT '05 Proceedings of the The 2005 Symposium on Applications and the Internet
Multicriteria Scheduling: Theory, Models and Algorithms
Multicriteria Scheduling: Theory, Models and Algorithms
Parallel Approaches for Multiobjective Optimization
Multiobjective Optimization
A new step toward load balancing based on competency rank and transitional phases in Grid networks
Future Generation Computer Systems
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The most popular parallelization approach of the branch and bound algorithm consists in building and exploring in parallel the search tree representing the problem being tackled. The deployment of such parallel model on a grid rises the crucial issue of dynamic load balancing. The major question is how to efficiently distribute the nodes of an irregular search tree among a large set of heterogeneous and volatile processors. In this paper, we propose a new dynamic load balancing approach for the parallel branch and bound algorithm on the computational grid. The approach is based on a particular numbering of the tree nodes allowing a very simple description of the work units distributed during the exploration. Such description optimizes the communications involved by the huge amount of load balancing operations. The approach has been applied to one instance of the bi-objective flow-shop scheduling problem. The application has been experimented on a computational pool of more than 1000 processors belonging to seven Nation-wide clusters. The optimal solution has been generated within almost 6 days with a parallel efficiency of 98%.