JSEA  Vol.5 No.8 , August 2012
Parallelization of a Branch and Bound Algorithm on Multicore Systems
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.

Cite this paper
C. Chung, J. Flynn and J. Sang, "Parallelization of a Branch and Bound Algorithm on Multicore Systems," Journal of Software Engineering and Applications, Vol. 5 No. 8, 2012, pp. 621-629. doi: 10.4236/jsea.2012.58071.
References
[1]   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

[2]   K. R. Baker, “Introduction to Sequencing and Scheduling,” Wiley, New York, 1974.

[3]   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

[4]   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.

[5]   D. Gelenter and T. G. Crainic, “Parallel Branch and Bound Algorithms: Survey and Synthesis,” Operation Research, Vol. 2, 1994, pp. 1042-1066.

[6]   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.

[7]   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

[8]   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

[9]   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

[10]   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.

[11]   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.

[12]   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

[13]   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

[14]   B. Mans, T. Mautor and C. Roucairol, “A Parallel Depth First Search Branch and Bound Algorithm for the Quadratic Assignment Problem,” European Journal of Operational Research, Vol. 81, No. 3, 1995, pp. 617-628. doi:10.1016/0377-2217(93)E0334-T

[15]   J. Framinan, R. Leisten and R. Ruiz-Usano, “Comparison of Heuristics for Flow Time Minimisation in Permutation Flowshops,” Computers & Operations Research, Vol. 32, No. 5, 2005, pp. 1237-1254.

[16]   Y. D. Kim, “Heuristics for Flowshop Scheduling Problems Minimizing Mean Tardiness,” Journal of Operational Research Society, Vol. 44, No. 1, 1993, pp. 19-28.

[17]   C. Reeves, “Genetic Algorithms for the Operations Researcher,” INFORMS Journal of Computing, Vol. 9, No. 3, 1997, pp. 231-250.

[18]   C. Ruiz and C. Maroto, “Comprehensive Review and Evaluation of Permutation Flowshop Heuristics,” European Journal of Operational Research, Vol. 165, 2005, pp. 479-494. doi:10.1016/j.ejor.2004.04.017

[19]   C. N. Potts, “An Adaptive Branching Rule for the Permutation Flow-Shop Problem,” European Journal of Operational Research, Vol. 5, No. 1, 1980, pp. 19-25.

[20]   B. Nichols, D. Buttlar and J. P. Farrel, “Pthreads Programming,” 1st Edition, O’Reilly & Associates, Inc., Sebastopol, 1996.

[21]   K. A. Robbins and S. Robbins, “Practical UNIX Programming,” Prentice-Hall, Englewood Cliffs, 1996.

[22]   E. Taillard, “Benchmarks for Basic Scheduling Problems,” European Journal of Operational Research, Vol. 64, No. 2, 1993, pp. 278-285.

[23]   W. Press, S. Teukolsky, W. Vetterling and B. Flannery, “Numerical Recipes in C: The Art of Scientific Computing,” 2nd Edition, Chapter 7, Cambridge University Press, Cambridge, 1992.

 
 
Top