Hostname: page-component-76fb5796d-5g6vh Total loading time: 0 Render date: 2024-04-28T08:15:25.270Z Has data issue: false hasContentIssue false
  • English
  • Français

G-NETWORKS AND THE OPTIMIZATION OF SUPPLY CHAINS

Published online by Cambridge University Press:  08 April 2019

Yi Wang
Yi Wang*
Affiliation:
Intelligent Systems and Networks Group, Department of Electrical and Electronic Engineering, Imperial College, London SW7 2AZ, UK E-mail: yi.wang18@imperial.ac.uk
Get access Rights & Permissions [Opens in a new window]

Abstract

Supply chains are fundamental to the economy of the world and many supply chains focus on perishable items, such as food, or even clothing that is subject to a limited shelf life due to fashion and seasonable effects. G-networks have not been previously applied to this important area. Thus in this paper, we apply G-networks to supply chain systems and investigate an optimal order allocation problem for a N-node supply chain with perishable products that share the same order source of fresh products. The objective is to find an optimal order allocation strategy to minimize the purchase price per object from the perspective of the customers. An analytical solution based on G-networks with batch removal, together with optimization methods are shown to produce the desired results. The results are illustrated by a numerical example with realistic parameters.

Keywords

analytical solutionG-networks with batch removallagrange multipliersoptimizationperishable itemssupply chains
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 35 , Special Issue 1: Learning, Optimization, and Theory of G-Networks , January 2021 , pp. 62 - 74
Copyright
Copyright © Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1Besik, D. & Nagurney, A. (2017). Quality in competitive fresh produce supply chains with application to farmers' markets. Socio-Economic Planning Sciences 60: 62–76.Google Scholar
2Braziotis, C., Bourlakis, M., Rogers, H., & Tannock, J. (2013). Supply chains and supply networks: distinctions and overlaps. Supply Chain Management: an International Journal 18(6): 644–652.CrossRefGoogle Scholar
3Bremang, A., Lyons, A.C., & Michaelides, Z. (2006). An information system's architecture to support responsive supply chains. International Journal of Agile Systems and Management 1(4): 376–387.CrossRefGoogle Scholar
4Chen, J. & Yu, H. (2013) Performance simulation and optimization of agricultural supply chains. In Information Science and Cloud Computing (ISCC), 2013 International Conference on, pages 131–137. IEEE.CrossRefGoogle Scholar
5Cramer, C. & Gelenbe, E. (2000). Video quality and traffic QoS in learning-based subsampled and receiver-interpolated video sequences. IEEE Journal on Selected Areas in Communications 18(2): 150–167.CrossRefGoogle Scholar
6Duan, Q. & Liao, T.W. (2013). A new age-based replenishment policy for supply chain inventory optimization of highly perishable products. International journal of production economics 145(2): 658–671.CrossRefGoogle Scholar
7Fourneau, J.-M. & Gelenbe, E. (2017). G-networks with adders. Future Internet 9(3): 34.CrossRefGoogle Scholar
8Fourneau, J.-M., Marin, A., & Balsamo, S. (2016) Modeling energy packets networks in the presence of failures. In 24th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2016, London, United Kingdom, September 19–21, 2016, pages 144–153.CrossRefGoogle Scholar
9Frazzon, E.M., Silva, L.S., & Hurtado, P.A. (2015). Synchronizing and improving supply chains through the application of cyber-physical systems. IFAC-PapersOnLine 48(3): 2059–2064.CrossRefGoogle Scholar
10Gelenbe, E. (1990). Réseaux neuronaux aléatoires stables. Comptes Rendus de l'Académie des Sciences. Série 2 310(3): 177–180.Google Scholar
11Gelenbe, E. (1991). Product-form queueing networks with negative and positive customers. Journal of Applied Probability 28(3): 656–663.CrossRefGoogle Scholar
12Gelenbe, E. (1993). G-networks with signals and batch removal. Probability in the Engineering and Informational Sciences 7(3): 335–342.CrossRefGoogle Scholar
13Gelenbe, E. (1993). G-networks with triggered customer movement. Journal of Applied Probability 30(3): 742–748.CrossRefGoogle Scholar
14Gelenbe, E. (2007). Dealing with software viruses: a biological paradigm. Information Security Technical Report 12(4): 242–250.CrossRefGoogle Scholar
15Gelenbe, E. (2007). Steady-state solution of probabilistic gene regulatory networks. Physical Review E 76(3): 031903.CrossRefGoogle ScholarPubMed
16Gelenbe, E. (2009). Steps toward self-aware networks. Communications of the ACM 52(7): 66–75.CrossRefGoogle Scholar
17Gelenbe, E. (2012) Energy packet networks: Adaptive energy management for the cloud. In CloudCP '12 Proceedings of the 2nd International Workshop on Cloud Computing Platforms, page 1. ACM.CrossRefGoogle Scholar
18Gelenbe, E. & Abdelrahman, O.H. (2018). An energy packet network model for mobile networks with energy harvesting. Nonlinear Theory and Its Applications, IEICE 9(3): 1C15.CrossRefGoogle Scholar
19Gelenbe, E. & Fourneau, J.-M. (2002). G-networks with resets. Performance Evaluation 49(1–4): 179–191.CrossRefGoogle Scholar
20Gelenbe, E. & Labed, A. (1998). G-networks with multiple classes of signals and positive customers. European Journal of Operational Research 108(2): 293–305.CrossRefGoogle Scholar
21Gelenbe, E. & Mahmoodi, T. (2011). Energy-aware routing in the cognitive packet network. Energy 7–12.Google Scholar
22Gelenbe, E. & Pujolle, G. (1987) Introduction to Networks of Queues. Translation of “Introduction aux Réseaux de Files d'Attente”, Eyrolles, Paris, 1982, published by John Wiley Ltd, New York and Chichester.Google Scholar
23Gelenbe, E. & Schassberger, R. (1992). Stability of product form g-networks. Probability in the Engineering and Informational Sciences 6(3): 271–276.CrossRefGoogle Scholar
24Gelenbe, E. & Sevcik, K. C. Analysis of update synchronization for multiple copy data-bases. In 3rd Berkeley Workshop on Distributed Data Management and Computer Networks, pages 69–90, August 1978.Google Scholar
25Gelenbe, E. & Timotheou, S. (2008). Random neural networks with synchronized interactions. Neural Computation 20(9): 2308–2324.CrossRefGoogle ScholarPubMed
26Gelenbe, E. & Wang, Y. (March 2019) Modelling the impact of cyber-attacks on web based sales. In Submitted for Publication.Google Scholar
27Gelenbe, E. & Wang, Y. (June 2019) Supply chains for perishable goods and g-networks. In 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), pages 1–6. IEEE.CrossRefGoogle Scholar
28Gelenbe, E. & Wu, F.-J. (2013). Future research on cyber-physical emergency management systems. Future Internet 5(3): 336–354.CrossRefGoogle Scholar
29Gelenbe, E. & Yin, Y. (2016) Deep learning with random neural networks. In Proceedings of SAI Intelligent Systems Conference, pages 450–462. Springer.CrossRefGoogle Scholar
30Gelenbe, E., Glynn, P., & Sigman, K. (1991). Queues with negative arrivals. Journal of Applied Probability 28(1): 245–250.CrossRefGoogle Scholar
31Gelenbe, E., Lent, R., Montuori, A., & Xu, Z. (2002) Cognitive packet networks: Qos and performance. In Modeling, Analysis and Simulation of Computer and Telecommunications Systems, 2002. MASCOTS 2002. Proceedings. 10th IEEE International Symposium on, pages 3–9. IEEE.Google Scholar
32Gu, Q. & Gao, T. (2012). Management of two competitive closed-loop supply chains. International Journal of Sustainable Engineering 5(4): 325–337.CrossRefGoogle Scholar
33Guide, V.D.R., Harrison, T.P., & Van Wassenhove, L.N. (2003). The challenge of closed-loop supply chains. Interfaces 33(6): 3–6.CrossRefGoogle Scholar
34Haijema, R., van Dijk, N., van der Wal, J., & Sibinga, C.S. (2009). Blood platelet production with breaks: optimization by sdp and simulation. International Journal of Production Economics 121(2): 464–473.CrossRefGoogle Scholar
35Hülsmann, M., Grapp, J., & Li, Y. (2008). Strategic adaptivity in global supply chains competitive advantage by autonomous cooperation. International Journal of Production Economics 114(1): 14–26.CrossRefGoogle Scholar
36Ivanov, D. & Sokolov, B. (2012). The inter-disciplinary modelling of supply chains in the context of collaborative multi-structural cyber-physical networks. Journal of Manufacturing Technology Management 23(8): 976–997.CrossRefGoogle Scholar
37Ivanov, D., Sokolov, B., Solovyeva, I., Dolgui, A., & Jie, F. (2016). Dynamic recovery policies for time-critical supply chains under conditions of ripple effect. International Journal of Production Research 54(23): 7245–7258.CrossRefGoogle Scholar
38Jesús Sáenz, M. & Revilla, E. (2014). Creating more resilient supply chains. MIT Sloan Management Review 55(4): 22–24.Google Scholar
39Jung, J.Y., Blau, G., Pekny, J.F., Reklaitis, G.V., & Eversdyk, D. (2004). A simulation based optimization approach to supply chain management under demand uncertainty. Computers & Chemical Engineering 28(10): 2087–2106.CrossRefGoogle Scholar
40Kopach, R., Balcıoğlu, B., & Carter, M. (2008). Tutorial on constructing a red blood cell inventory management system with two demand rates. European Journal of Operational Research 185(3): 1051–1059.CrossRefGoogle Scholar
41Li, J. & Sheng, Z. (2008) Simulating the (s, s)-controlled stochastic system in different supply chains by a multi-agent system. In Computer Science and Information Technology, 2008. ICCSIT'08. International Conference on, pages 785–790. IEEE.CrossRefGoogle Scholar
42Nemeth, P., Toth, L., & Hartvanyi, T. (2006) Adopting rfid in supply chains. In Mechatronics, 2006 IEEE International Conference on, pages 263–266. IEEE.CrossRefGoogle Scholar
43Puschmann, T. & Alt, R. (2005). Successful use of e-procurement in supply chains. Supply Chain Management: an International Journal 10(2): 122–133.CrossRefGoogle Scholar
44Rajesh, R. & Ravi, V. (2015). Modeling enablers of supply chain risk mitigation in electronic supply chains: a grey–dematel approach. Computers & Industrial Engineering 87: 126–139.CrossRefGoogle Scholar
45Rajesh, R., Ravi, V., & Venkata Rao, R. (2015). Selection of risk mitigation strategy in electronic supply chains using grey theory and digraph-matrix approaches. International Journal of Production Research 53(1): 238–257.CrossRefGoogle Scholar
46Rong, A., Akkerman, R., & Grunow, M. (2011). An optimization approach for managing fresh food quality throughout the supply chain. International Journal of Production Economics 131(1): 421–429.CrossRefGoogle Scholar
47Sarker, B.R., Jamal, A.M.M., & Wang, S. (2000). Supply chain models for perishable products under inflation and permissible delay in payment. Computers & Operations Research 27(1): 59–75.CrossRefGoogle Scholar
48Serrano, W. & Gelenbe, E. (2018). The random neural network in a neurocomputing application for web search. Neurocomputing 280: 123–134.CrossRefGoogle Scholar
49Wang, L. & Gelenbe, E. (2018). Adaptive dispatching of tasks in the cloud. IEEE Transactions on Cloud Computing 6(1): 33–45.CrossRefGoogle Scholar
50Xu, D.F., Li, Q., Jun, H.-B., Browne, J., Chen, Y.L., & Kiritsis, D. (2009). Modelling for product information tracking and feedback via wireless technology in closed-loop supply chains. International Journal of Computer Integrated Manufacturing 22(7): 648–670.CrossRefGoogle Scholar
51Yang, M., Smart, P., Kumar, M., Jolly, M., & Evans, S. (2018). Product-service systems business models for circular supply chains. Production Planning & Control 29(6): 498–508.CrossRefGoogle Scholar
52Yin, S., Nishi, T., & Grossmann, I.E. (2015). Optimal quantity discount coordination for supply chain optimization with one manufacturer and multiple suppliers under demand uncertainty. The International Journal of Advanced Manufacturing Technology 76(5–8): 1173–1184.CrossRefGoogle Scholar