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: firstname.lastname@example.org).
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).