A new algorithm for linearly constrained c-convex vector optimization with a supply chain network risk application

2015 ◽  
Vol 247 (2) ◽  
pp. 359-365 ◽  
Author(s):  
Shaojian Qu ◽  
Mark Goh ◽  
Ying Ji ◽  
Robert De Souza
2018 ◽  
Vol 2018 ◽  
pp. 1-18
Author(s):  
Tirazheh Zare-Garizy ◽  
Gilbert Fridgen ◽  
Lars Wederhake

Globalization and outsourcing are two main factors which are leading to higher complexity of supply chain networks. Due to the strategic importance of having a sustainable network, it is necessary to have an enhanced supply chain network risk management. In a supply chain network many firms depend directly or indirectly on a specific supplier. In this regard, unknown risks of network’s structure can endanger the whole supply chain network’s robustness. In spite of the importance of risk identification of supply chain network, firms are not willing to exchange the structural information of their network. Firms are concerned about risking their strategic positioning or established connections in the network. The paper proposes to combine secure multiparty computation cryptography methods with risk identification algorithms from social network analysis to address this challenge. The combination enables structural risk identification of supply chain networks without endangering firms’ competitive advantage.


Author(s):  
Johannes Göllner ◽  
Andreas Peer ◽  
Stefan Rass ◽  
Gerald Quirchmayr ◽  
Viliam Zathurecky

2015 ◽  
Vol 9 (4) ◽  
pp. 18-25
Author(s):  
Saurabh Bhardwaj ◽  
◽  
C.S. Jawalkar ◽  

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