Research ArticleOpen AccessGoogle Scholar indexed
Parallelization of a Branch and Bound Algorithm on Multicore Systems
Department of Operations and Supply Chain Management, Cleveland State University, Cleveland, USA
Department of Operations and Supply Chain Management, Cleveland State University, Cleveland, USA
Departmentof Computer and Information Science, Cleveland State University, Cleveland, USA.
- 1 Department of Operations and Supply Chain Management, Cleveland State University, Cleveland, USA
- 2 Department of Operations and Supply Chain Management, Cleveland State University, Cleveland, USA
- 3 Departmentof Computer and Information Science, Cleveland State University, Cleveland, USA.
Journal of Software Engineering and Applications·Volume 05 (2012)·Pages 621–629·Published 28 August 2012·DOI10.4236/jsea.2012.58071
Copy link · social · email
Abstract
The general m-machine permutation flowshop problem with the total flow-time objective is known to be NP-hard for m ≥ 2. The only practical method for finding optimal solutions has been branch-and-bound algorithms. In this paper, we present an improved sequential algorithm which is based on a strict alternation of Generation and Exploration execution modes as well as Depth-First/Best-First hybrid strategies. The experimental results show that the proposed scheme exhibits improved performance compared with the algorithm in [1]. More importantly, our method can be easily extended and implemented with lightweight threads to speed up the execution times. Good speedups can be obtained on shared-memory multicore systems.
KeywordsParallel Branch and BoundMultithreaded ProgrammingMulticore SystemPermutation FlowshopSoftware Reuse
- C. Chung, J. Flynn and O. Kirca, “A Branch and Bound Algorithm to Minimize the Total Flow Time for m-Machine Permutation Flowshop Problems,” International Journal of Production Economics, Vol. 79, No. 3, 2002, pp. 185-196. doi:10.1016/S0925-5273(02)00234-7
- K. R. Baker, “Introduction to Sequencing and Scheduling,” Wiley, New York, 1974.
- J. N. D. Gupta and E. F. Stafford Jr., “Flowshop Scheduling Research after Five Decades,” European Journal of Operational Research, Vol. 169, No. 3, 2006, pp. 699-711. doi:10.1016/j.ejor.2005.02.001
- R. A. Dudek, S. S. Panwalker and M. L. Smith, “The Lessons of Flowshop Scheduling Research,” Operations Research, Vol. 40, No. 1, 1992, pp. 7-13.
- D. Gelenter and T. G. Crainic, “Parallel Branch and Bound Algorithms: Survey and Synthesis,” Operation Research, Vol. 2, 1994, pp. 1042-1066.
- E. Ignall and L. Schrage, “Application of the Branch and Bound Technique to Some Flow-Shop Scheduling Problems,” Operations Research, Vol. 13, No. 3, 1965, pp. 400-412.
- S. P. Bansal, “Minimizing the Sum of Completion Times of n Jobs Over m Machines in a Flowshop—A Branch, Bound Approach,” AIIE Transactions, Vol. 9, 1977, pp. 306-311. doi:10.1080/05695557708975160
- R. H. Ahmadi and U. Bagchi, “Improved Lower Bounds for Minimizing the Sum of Completion Times of n Jobs Over m Machines in a Flow Shop,” European Journal of Operational Research, Vol. 44, 1990, pp. 331-336. doi:10.1016/0377-2217(90)90244-6
- C.-F. Yu and B. W. Wah, “Efficient Branch-and-Bound Algorithms on a Two-Level Memory System,” IEEE Transactions on Software Engineering, Vol. 14, No. 9, 1988, pp. 1342-1356. doi:10.1109/32.6177
- M. O. Neary and P. Cappello, “Advanced eager scheduling for Java-Based Adaptive Parallel Computing,” Concurrency and Computation: Practice and Experience, Vol. 17, No. 7-8, 2005, pp. 797-819.
- K. Yu, J. Zhou, C. Lin and C. Tang, “Efficient Parallel Branch-and-Bound Algorithm for Constructing Minimum Ultrametric Trees,” Journal of Parallel and Distributed Computing, Vol. 69, No. 11, 2009, pp. 905-914.
- V. N. Rao and V. Kumar, “Parallel Depth-First Search on Multiprocessors—Part I: Implementation,” International Journal of Parallel Programming, Vol. 16, No. 6, 1987, pp. 479-499. doi:10.1007/BF01389000
- M. K. Yang and C. R. Das, “Evaluation of a Parallel Branch-and-Bound Algorithm on a Class of Multiprocessors,” IEEE Transactions on Parallel and Distributed Systems, Vol. 5, No. 1, 1994, pp. 74-86. doi:10.1109/71.262590