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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
The results on the set of problems show that the modified algorithm produces good solutions and time saving in computation of large-scale network.\r\n
📌 3 Key Takeaways
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Objective & Hypothesis:\r\n This paper considers the problem of finding the shortest path in a static network, where the costs are constant.
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Key Findings & Data:The CE Algorithm based strategy that is presented by Rubinstein to solving rare event and combinatorial optimization problem is modified to finding shortest path in this research.
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Conclusion & Impact:The results on the set of problems show that the modified algorithm produces good solutions and time saving in computation of large-scale network.\r\n
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Shortest Path · CE Algorithm · Rare Event
Özet
\r\n This paper considers the problem of finding the shortest path in a static network, where the costs are constant. The CE Algorithm based strategy that is presented by Rubinstein to solving rare event and combinatorial optimization problem is modified to finding shortest path in this research. To analyze the efficiency of the used algorithm three sets of small, medium and large sized problems that generated randomly are solved. The results on the set of problems show that the modified algorithm produces good solutions and time saving in computation of large-scale network.\r\n
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Cite This Article
ABBASI, S., MOOSAVI, F. (2020). Finding shortest path in static networks: using a modified algorithm. International Journal of Finance and Banking Studies, Cilt: 1 Sayı: 1. https://ajindex.com/articles/1040141