Intelligent Control of Urban Fresh Agricultural Products Supply Chain using Big Data and Internet of Things

International Journal of Computer Science and Engineering
© 2020 by SSRG - IJCSE Journal
Volume 7 Issue 9
Year of Publication : 2020
Authors : Jun LI, Shang XIANG

pdf
How to Cite?

Jun LI, Shang XIANG, "Intelligent Control of Urban Fresh Agricultural Products Supply Chain using Big Data and Internet of Things," SSRG International Journal of Computer Science and Engineering , vol. 7,  no. 9, pp. 1-6, 2020. Crossref, https://doi.org/10.14445/23488387/IJCSE-V7I9P101

Abstract:

Aim to the problems among the supply chain of fresh agricultural products in China, such as difficult to sell and buy, frequent quality problems, and low logistics efficiency, this project focus on theses following key technologies: a top design for supply chain management and control of fresh agricultural products, Internet of Things (IoT) innovative design to supply chain management and control of fresh agricultural products, life cycle management of fresh agricultural products under big data environment, innovative service model of the urban supply chain of fresh agricultural products, cost control and revenue sharing for the urban supply chain of fresh agricultural products, and dynamic evolution and security control for the urban supply chain of fresh agricultural products.

Keywords:

City Public Service, Smart City, Supply Chain, Intelligent Management and Control, Fresh Agricultural Product

References:

[1] Richards P, Reardon T, Tschirley D, et al. “Cities and the future of agriculture and food security: policy and programmatic roundtable”. Food Security, 2016, 8(4): 871~877
[2] Frayne B, McCordic C. “Planning for food secure cities: Measuring the influence of infrastructure and income on household food security in Southern African cities”. GEOFORUM, 2015, 65: 1~11
[3] Eigenbrod C, Gruda N. “Urban vegetable for food security in cities, a review”. Agronomy for Sustainable Development, 2015, 35(2): 483~498
[4] Shi N, Chen Y, Huang H, et al. “Characteristics of food safety supervision system in foreign countries and its implications for China”. Science & Technology of Food Industry, 2017, 38(16): 239~241, 252
[5] Chen TQ, Wang L, Wang JN. “Transparent Assessment of the Supervision Information in China’s Food Safety: A Fuzzy-ANP Comprehensive Evaluation Method”. Journal of Food Quality, 2017, Article No: UNSP 4340869, 1~14
[6] Chen HG. “Review on safety supervision of food supply chain”. Science & Technology of Food Industry, 2013, 34(2): 49~53, 86
[7] Jin HS, Liu YS. “Risk Identification and Safety Supervision and Management of Agricultural Product Supply Chains”. Food Science, 2015, 36(13): 265~271
[8] Verdouw CN, Wolfert J, Beulens AJM, et al. “Virtualization of food supply chains with the internet of things”. Journal of Food Engineering, 2016, 176: 128~136
[9] Ji GJ, Hu LM, Tan KH. “A study on decision-making of food supply chain based on big data”. Journal of Systems Science and Systems Engineering, 2017, 26(2): 183~198
[10] Bruzzone AG, Massei M, Longo F, et al. “Simulation Based Design of Innovative Quick Response Processes in Cloud Supply Chain Management for ‘Slow Food’ Distribution.” Communications in Computer and Information Science, 2016, 645: 25~34
[11] Tu MR. “An exploratory study of Internet of Things (IoT) adoption intention in logistics and supply chain management: A mixed research approach”. International Journal of Logistics Management, 2018, 29(1): 131~151
[12] Li B, Li Y.L. “Internet of things drives supply chain innovation: a research framework”. International Journal of Organizational Innovation, 2017, 9(3): 71~92
[13] Yan B, Wu XH, Ye B, et al. “Three-level supply chain coordination of fresh agricultural products in the Internet of Things.” Industrial Management & Data Systems, 2017, 117(9): 1842~1865
[14] Galimberti A, Labra M, Sandionigi A, et al. “DNA Barcoding for Minor Crops and Food Traceability”. Advances in Agriculture, 2014
[15] Wang D, Li Y.H., Li B, et al. “The Applied Research of HACCP Food Safety System in Pork Traceability”. Advanced Materials Research, 2014, 1056: 103~106
[16] Yan B, Yan C, Ke CX, et al. “Information sharing in supply chain of agricultural products based on the Internet of Things”. Industrial Management & Data Systems, 2016, 116(7): 1397~1416
[17] Verdouw CN, Beulens AJM, van der Vorst JGAJ. “Virtualization of floricultural supply chains: A review from an Internet of Things perspective.” Computers and Electronics in Agriculture, 2013, 99: 160~175
[18] Ringwall K. “Estimates of the effectiveness of current beef cattle tracking systems”. Journal of Animal Science, 2005, 83(2): 118~119
[19] Trevarthen A. “The national livestock identification system: the importance of traceability in e-business”. Journal of Theoretical and Applied Electronic Commerce Research, 2007, 2(2): 49~62
[20] Badia-Melis R, Mishra P, Ruiz-García L. “Food traceability: New trends and recent advances. A review”. Food Control, 2015, 57: 393~401
[21] Yan R. “Optimization approach for increasing revenue of perishable product supply chain with the Internet of Things”. Industrial Management & Data Systems, 2017, 117(4): 729~741
[22] Verdouw CN, Wolfert J, Beulens AJM, et al. “Virtualization of food supply chains with the internet of things”. Journal of Food Engineering, 2016, 176: 128~136
[23] Choi TM. “A System of Systems Approach for Global Supply Chain Management in the Big Data Era.” IEEE Engineering Management Review, 2018, 46(1): 91~97
[24] Ji GJ, Hu LM, Tan KH. “A study on decision-making of food supply chain based on big data”. Journal of Systems Science and Systems Engineering, 2017, 26(2): 183~198
[25] Zhao R, Liu YY, Zhang N, et al. “An optimization model for green supply chain management by using a big data analytic approach”. Journal of Cleaner Production, 2017, 142(S.I.): 1085~1097
[26] Hu Y. “A genetic-algorithm-based remnant grey prediction model for energy demand forecasting”. Plos One, 2017, 12(10): 1~11
[27] Liu Y, Ju W, Zhao J, et al. “Product life cycle based demand forecasting by using artificial bee colony algorithm optimized two-stage polynomial fitting.” Journal of Intelligent & Fuzzy Systems, 2016, 31(2): 825~836
[28] Adamowski JF. “Peak Daily Water Demand Forecast Modeling Using Artificial Neural Networks”. Journal of Water Resources Planning & Management, 2008, 134(2): 119~128
[29] Wang G. “Research On Supply Chain Demand Prediction Based On BP Neural Network Algorithm”. Inmateh - Agricultural Engineering, 2013, 40(2): 27~34
[30] Cao J, Jiang Z, Wang K. “Customer demand prediction of service-oriented manufacturing using the least square support vector machine optimized by particle swarm optimization algorithm”. Engineering Optimization, 2017, 49(7): 1197~ 1210
[31] Michel Roberto. Success with Last Mile starts early. Logistics Management, 2015, 54(4): 46~49
[32] Chris Dubelar, Amrik Sohal, Vedrana Savic. Benefits, impediments and critical success factors in B2C E-business adoption. Technovation, 2010 (25): 1251~1262
[33] Isasi NKG, Frazzon EM, Uriona M. “Big Data and Business Analytics in the Supply Chain: A Review of the Literature. “IEEE Latin America Transactions, 2015, 13(10): 3382~3391
[34] Liu P, Yi SP. “Pricing policies of green supply chain considering targeted advertising and product green degree in the Big Data environment”. Journal of Cleaner Production, 2017, 164: 1614~1622
[35] Liu P. “Pricing Strategies of a Three-Stage Supply Chain: A New Research in the Big Data Era”. Discrete Dynamics in Nature and Society, 2017, Article Number: 9024712
[36] Richard AT, Helo P.T. “Big data applications in operations / supply-chain management: A literature review.” Computers & Industrial Engineering, 2016, 101: 528~543
[37] Zhong RY, Newman ST, Huang GQ, et al. “Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives”. Computers & Industrial Engineering, 2016, 101: 572~591
[38] Giannakis M, Louis M. “A multi-agent based system with big data processing for enhanced supply chain agility.” Journal of Enterprise Information Management, 2016, 29(5): 706~727
[39] Ma K, Wang LC, Chen Y. “A Collaborative Cloud Service Platform for Realizing Sustainable Make- To-Order Apparel Supply Chain.” Sustainability, 2018, 10(1): Article Number: 11
[40] Xing K, Qian W, Zaman AU. “Development of a cloudbased platform for footprint assessment in green supply chain management.” Journal of Cleaner Production, 2016, 139: 191~203
[41] Karan Sharma, "Securing smart cities by fingerprint matching" SSRG International Journal of Computer Science and Engineering 3.5 (2016): 63-67.