A Fault Tolerant and Secured Network Design for File and Application Sharing in a Mid-sized Business Environment

Author(s):  
Eric B. Blancaflor ◽  
Christian Collins C. Chua ◽  
Vince Anthony A. Parallag ◽  
William P. Rey ◽  
Sanami Tamarauoyerimini ◽  
...  
Author(s):  
Baijnath Kaushik ◽  
◽  
Navdeep Kaur ◽  
Amit Kumar Kohli ◽  
◽  
...  

The objective of this paper is to present a novelmethod for achievingmaximumreliability in fault-tolerant optimal network design when networks have variable size. Reliability calculation is a most important and critical component when fault-tolerant optimal network design is required. A network must be supplied with certain parameters that guarantee proper functionality and maintainability in worse-case situations. Many alternative methods for measuring reliability have been stated in the literature for optimal network design. Most of these methods, mentioned in the literature for evaluating reliability, may be analytical and simulation-based. These methods provide significant ways for computing reliability when a network has a limited size. Significant computational effort is also required for growing variable-sized networks. A novel neural network method is therefore presented to achieve significant high reliability in fault-tolerant optimal network design in highly growing variable networks. This paper compares simulation-based analytical methods with improved learning rate gradient descent-based neural network methods. Results show that improved optimal network design with maximum reliability is achievable by a novel neural network at a manageable computational cost.


2015 ◽  
Vol 33 (4) ◽  
pp. 396-404 ◽  
Author(s):  
S. Rajkumar ◽  
Neeraj Kumar Goyal

2013 ◽  
Vol 13 (7) ◽  
pp. 3211-3224 ◽  
Author(s):  
B. Kaushik ◽  
N. Kaur ◽  
A.K. Kohli

2021 ◽  
Vol 13 (5) ◽  
pp. 2596
Author(s):  
Kanokporn Kungwalsong ◽  
Chen-Yang Cheng ◽  
Chumpol Yuangyai ◽  
Udom Janjarassuk

A supply chain disruption is an unanticipated event that disrupts the flow of materials in a supply chain. Any given supply chain disruption could have a significant negative impact on the entire supply chain. Supply chain network designs usually consider two stage of decision process in a business environment. The first stage deals with strategic levels, such as to determine facility locations and their capacity, while the second stage considers in a tactical level, such as production quantity, delivery routing. Each stage’s decision could affect the other stage’s result, and it could not be determined individual. However, supply chain network designs often fail to account for supply chain disruptions. In this paper, this paper proposed a two-stage stochastic programming model for a four-echelon global supply chain network design problem considering possible disruptions at facilities. A modified simulated annealing (SA) algorithm is developed to determine the strategic decision at the first stage. The comparison of traditional supply chain network decision framework shows that under disruption, the stochastic solutions outperform the traditional one. This study demonstrates the managerial viability of the proposed model in designing a supply chain network in which disruptive events are proactively accounted for.


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