Abstract
In this paper, we develop a multi-item, multi-period, double-random inventory model and demonstrate the application of distributed-memory MIMD parallel computers to solving this model, which, like many other inventory models, provides the basis for decisions in business and manufacturing industries. The parallel algorithm we have constructed in this study, with nearly perfect speedup, can solve for our model with 2000 items simulated for 30 years with an assumption of daily re-order, by using 24 hours of CPU time on a 50-node Paragon. This suggests that modeling realistic inventory problems with several thousand items on a small parallel computer is feasible. As a consequence, an entire new range of economical processes are open to systematic study by parallel processing.
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Wang, Y., Deng, Y. (1996). Multi-Item Stochastic Inventory Models with Constraints and Their Parallel Computation. In: Gilli, M. (eds) Computational Economic Systems. Advances in Computational Economics, vol 5. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8743-3_5
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DOI: https://doi.org/10.1007/978-94-015-8743-3_5
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-4655-0
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