Abstract
We implemented a version of primal transportation algorithm on a 14 processor BBN Butterfly computer and solved a variety of large, fully dense, randomly generated transportation and assignment problems ranging in size up to m = n = 3000. The algorithm alternates between a search and pivot phase. Processors independently locate possible pivots by concurrently searching the reduced cost matrix. Pivots are then performed sequentially by all processors. This parallelization strategy is justified since we have found that the search phase of the algorithm becomes the dominant activity with increasing problem size. The parallel algorithm has the added advantage of being easy to implement. A speedup of approximately 7 was obtained on large problems. The empirical difficulty of solving an n × n transportation problem was proportional to n a where a varied between 2.0 and 2.2 with increasing shipping amounts.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
