Home > Archive > 2020 > Volume 10 Number 2 (Feb. 2020) >
IJMLC 2020 Vol.10(2): 290-298 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.2.934

Parallel Branch and Bound Algorithm for Product Testing Job Scheduling Problems using MapReduce

Yu Yu

Abstract—This paper analysis the similarities and differences between test scheduling and production scheduling. A job parallelization scheduling model based on characteristics of test scheduling is proposed. Further, the branch and bound search algorithm of job shop scheduling problem is studied. The MR-WFBB algorithm based on cloud computing MapReduce computing model is proposed. This algorithm is a novel job shop scheduling parallelization breadth-first branch and bound algorithm. Based on the actual test scheduling, this paper proposes the constraints of the job parallelization scheduling mode, solves the job parallelization scheduling problem under certain constraints and gives the Gantt chart and the assignment table corresponding to the optimal solution. The optimal solution can provide calibration and comparison for various artificial intelligence scheduling algorithms.

Index Terms—MapReduce, Job shop, parallel algorithm.

Yu Yu is with Institute of Ocean Instruments and Metrology, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, China (e-mail: rainertop@126.com).


Cite: Yu Yu, "Parallel Branch and Bound Algorithm for Product Testing Job Scheduling Problems using MapReduce," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 290-298, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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