Analytical bi-level multi-local-world complex network model on fresh agricultural products supply chain
Keywords:
fresh agricultural products, supplying process, supply chain, complex network, multi-local-world modelAbstract
Lately, in some regions and seasons in China, urban consumers have paid high in buying fresh agricultural products while farmers get unreasonable income from producing them. To seek the reason for the phenomenon and explore ways to simulate it, this study constructed and implemented a complex network model named the Bi-Level Multi-Local-World (BI-MLW model) with characteristics of an interdependent coupling relationship between its participants. To verify the validity of the model, this study implemented an experimental simulation under Small Decentralized Operation Mode (SDOM) and Large Centralized Operation Mode (LCOM) scenarios using Cucurbita pepo and Cucumber in the Tianjin area of China as sample empirical products. Results indicate that nodes do not increase edges rapidly which reflects that even large firms in agricultural business cannot occupy markets fleetly. Furthermore, under the SDOM scenario the BI-MLW model exposes scale-free features with a small average degree value and low average clustering coefficient, while under the LCOM scenario, the model displays a rising average clustering coefficient and a lowered average path length. Both of which are consistent with the common view in literature and features of reality. Thus, the BI-MLW model specially designed for fresh agricultural products supply chain can improve the descriptive ability than conventional Erdös-Rényi (ER), Barabási-Albert (BA), Bianconi-Barabási (BB) network models. Keywords: fresh agricultural products, supplying process, supply chain, complex network, multi-local-world model DOI: 10.25165/j.ijabe.20221501.6353 Citation: Liu Y Q, Xu S W, Liu J J, Zhuang J Y. Analytical bi-level multi-local-world complex network model on fresh agricultural products supply chain. Int J Agric & Biol Eng, 2022; 15(1): 208–215.References
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[2] Braha D, Bar-Yam Y. Information flow structure in large-scale product development organizational networks. Journal of Information Technology, 2004; 19(4): 244–253.
[3] Wang X F, Li X, Chen G R. Complex network theory and its application. Beijing: Tsinghua University Press, 2006; 260p. (in Chinese)
[4] Zhou T, Bai W J, Wang B H, Liu Z J, Yan G. A brief review of complex networks. Progress in Physics, PIP, 2005; 34: 31–36. (in Chinese)
[5] Albert R, Barabási A-L. Statistical mechanics of complex networks. Reviews of Modern Physics, 2002; 74(1): 47–97.
[6] Ren Z M, Zeng A, Zhang Y C. Structure-oriented prediction in complex networks. Physics Reports-Review Section of Physics Letters, 2018; 750: 1-51. doi:10.1016/j.physrep.2018.05.002.
[7] Newman M E J. Clustering and preferential attachment in growing networks. Physical Review E, 2001; 64(2): 025102. doi: 10.1103/PhysRevE.64.025102.
[8] Wojtowicz W M, Vielmetter J, Fernandes R A, Siepe D H, Eastman C L, Chisholm G B, et al. A human IgSF cell-surface interactome reveals a complex network of protein-protein interactions. Cell, 2020; 182(4): 1027–1043.
[9] Garlaschelli D, Boguñá M, Loffredo M I. The world trade web: Structure, evolution and modeling. UNESCO-EOLSS-Sample Chapters-Technology, Information and Systems Management Resources-Complex Networks, 2010. Available at: http://www.eolss.net/sample-chapters/C15/E6-200-05.pdf. Accessed on [2020-01-20].
[10] Wang B H, Zhou T, Zhou C S. Statistical physics research for human behaviors, complex networks, and information mining. Journal of University of Shanghai for Science and Technology, 2012; 34(2): 103–117. (in Chinese)
[11] Hofman J M, Sharma A, Watts D J. Prediction and explanation in social systems. Science, 2017; 355(6234): 486-488.
[12] Erdös P, Rényi A. On random graphs. Publicationes Mathematicae, 1959; 6: 290–297.
[13] Barabási A-L, Albert R. Emergence of scaling in random networks. Science, 1999; 286(5439): 509–512.
[14] Garlaschelli D, Loffredo M I. Structure and evolution of the world trade network. Physica a-Statistical Mechanics and its Applications, 2005; 355(1): 138–144.
[15] Li X, Jin Y Y, Chen G R. Complexity and synchronization of the world trade web. Physica a-Statistical Mechanics And Its Applications, 2003; 328(1-2): 287–296.
[16] Bianconi G, Barabási A-L. Competition and multiscaling in evolving networks. Europhysics Letters, 2001; 54(4): 436–442.
[17] Caldarelli G, Marchetti R, Pietronero L. The fractal properties of Internet. Europhysics Letters, 2000; 52(4): 386–391.
[18] Chen G R. Problems and challenges in control theory under complex dynamical network environments. Acta Automatica Sinica, 2013; 39(4): 312–321. (in Chinese)
[19] Fan Z, Chen G, Zhang Y. A comprehensive multi-local-world model for complex networks. Physics Letters A, 2009; 373(18-19): 1601–1605.
[20] Virkar Y, Clauset A. Power-law distributions in binned empirical data. Annals of Applied Statistics, 2014; 8(1): 89–119.
[21] Beamon B M. Supply chain design and analysis: Models and methods. International Journal of Production Economics, 1998; 55(3): 281–294.
[22] Kim S H, Yoon S-G, Chae S H, Park S. Economic and environmental optimization of a multi-site utility network for an industrial complex. Journal of Environmental Management, 2010; 91(3): 690–705.
[23] Hayami Y, Kikuchi M, Marciano E B. Middlemen and peasants in rice marketing in the Philippines. Agricultural Economics, 1999; 20(2): 79–93.
[24] Arya A, Loeftier C, Mittendorf B, Pfeiffer T. The middleman as a panacea for supply chain coordination problems. European Journal of Operational Research, 2015; 240(2): 393–400.
[25] Gabre-Madhin E Z. The role of intermediaries in enhancing market efficiency in the Ethiopian grain market. Agricultural Economics, 2001; 25(2-3): 311–320.
[26] Wang B, Yu S H. Report on The development of China’s agricultural
product circulation. China Business and Market, 2009; 1: 13–17. (in Chinese)
[27] Capaldo A, Giannoccaro I. How does trust affect performance in the supply chain? The moderating role of interdependence. International Journal of Production Economics, 2015; 166: 36–49.
[28] Kim Y, Chen Y-S, Linderman K. Supply network disruption and resilience: A network structural perspective. Journal of Operations Management, 2015; 33-34(1): 43–59.
[29] Konar M, Dalin C, Suweis S, Hanasaki N, Rinaldo A, Rodriguez-Iturbe I. Water for food: The global virtual water trade network. Water Resources Research, 2011; 47(5): W05520. doi: 10.1029/2010WR010307.
[30] Nair A, Vidal J M. Supply network topology and robustness against disruptions - an investigation using multi-agent model. International Journal of Production Research, 2011; 49(5): 1391–1404.
[31] Zhao K, Kumar A, Yen J. Achieving high robustness in supply distribution networks by rewiring. IEEE Transactions on Engineering Management, 2011; 58(2): 347–362.
[32] Xu G, Feng J, Chen F, Wang H, Wang Z. Simulation-based optimization of control policy on multi-echelon inventory system for fresh agricultural products. Int J Agric & Biol Eng, 2019; 12(2): 184–194.
[33] Newman M E J. Random graphs as models of networks. Random Graphs as Models of Networks, 2002; pp.35–68.
[34] Barrat A, Weigt M. On the properties of small-world network models. European Physical Journal B, 2000; 13(3): 547–560.
[35] Kim Y, Choi T Y, Yan T, Dooley K. Structural investigation of supply networks: A social network analysis approach. Journal of Operations Management, 2011; 29(3): 194–211.
[36] Dorogovtsev S N, Mendes J F F, Samukhin A N. Structure of growing networks with preferential linking. Physical Review Letters, 2000; 85(21): 4633–4636.
[37] Milgram S. The small world problem. Psychology Today, 1967; 2: 60–67.
[38] Albert R, Jeong H, Barabási A-L. Diameter of the world-wide web. Nature, 1999; 401(6749): 130–131.
[39] Travers J, Milgram S. An experimental study of the small world problem. Sociometry, 1969; 32: 425–443.
[40] Orenstein P. How does supply network evolution and its topological structure impact supply chain performance? 2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO). IEEE, 2016; pp.562–569.
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Published
2022-02-26
How to Cite
Liu, Y., Xu, S., Liu, J., & Zhuang, J. (2022). Analytical bi-level multi-local-world complex network model on fresh agricultural products supply chain. International Journal of Agricultural and Biological Engineering, 15(1), 208–215. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/6353
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