Contention is no obstacle to shared-memory multiprocessing
Communications of the ACM - Special issue on parallelism
The auction algorithm: a distributed relaxation method for the assignment problem
Annals of Operations Research - Special Issue: Parallel Optimization on Novel Computer Architectures
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Benefit-Cost Analysis of Coding Techniques for the Primal Transportation Algorithm
Journal of the ACM (JACM)
Algorithms for Network Programming
Algorithms for Network Programming
Results from a parallel branch and bound algorithm for the asymmetric traveling salesman problem
Operations Research Letters
Results from a parallel branch and bound algorithm for the asymmetric traveling salesman problem
Operations Research Letters
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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 x n transportation problem was proportional to n^a where a varied between 2.0 and 2.2 with increasing shipping amounts.