Digital Technology Empowers Grain Supply Chain Optimization Simulation
The issue of the balance of food supply and demand has always been the main issue of national and even world food security. There are many factors that affect food supply and demand, and the factors are interrelated. Therefore, it is necessary to study this complex issue in a systematic way in order...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6496713 |
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| _version_ | 1832549632795738112 |
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| author | Xiaoyan Xu Zhongye Sun |
| author_facet | Xiaoyan Xu Zhongye Sun |
| author_sort | Xiaoyan Xu |
| collection | DOAJ |
| description | The issue of the balance of food supply and demand has always been the main issue of national and even world food security. There are many factors that affect food supply and demand, and the factors are interrelated. Therefore, it is necessary to study this complex issue in a systematic way in order to provide a reliable theoretical basis for the country to formulate effective policy measures. Based on the analysis of the current situation of grain supply and demand, this paper uses system dynamics (SD) to carry out digital elevation model (DEM) and latitude correction for land surface temperature (LST). The LST, combined with the normalized difference vegetation index (NDVI), has initially constructed a temperature vegetation dryness index (TVDI) model; it has constructed five subsystems including arable land, production capacity, import, population, and consumption. This paper proposes a food supply chain network construction model from the dimensions of the food supply chain network’s information flow, logistics, and business flow. Through detailed empirical analysis of each subsystem, we judge the development trend of the total grain system, perform operational tests and historical tests on the simulation results of the model to judge the rationality of the model system structure and simulation prediction, and give the simulation results. Finally, based on the forecast results, targeted countermeasures and suggestions are proposed. |
| format | Article |
| id | doaj-art-14ff151b1c5a4eff87192ba8e4090fef |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-14ff151b1c5a4eff87192ba8e4090fef2025-02-03T06:10:45ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/64967136496713Digital Technology Empowers Grain Supply Chain Optimization SimulationXiaoyan Xu0Zhongye Sun1Food Science and Engineering, Department of Economic and Trade, Henan University of Technology, ZhengZhou 450001, ChinaFood Science and Engineering, Department of Economic and Trade, Henan University of Technology, ZhengZhou 450001, ChinaThe issue of the balance of food supply and demand has always been the main issue of national and even world food security. There are many factors that affect food supply and demand, and the factors are interrelated. Therefore, it is necessary to study this complex issue in a systematic way in order to provide a reliable theoretical basis for the country to formulate effective policy measures. Based on the analysis of the current situation of grain supply and demand, this paper uses system dynamics (SD) to carry out digital elevation model (DEM) and latitude correction for land surface temperature (LST). The LST, combined with the normalized difference vegetation index (NDVI), has initially constructed a temperature vegetation dryness index (TVDI) model; it has constructed five subsystems including arable land, production capacity, import, population, and consumption. This paper proposes a food supply chain network construction model from the dimensions of the food supply chain network’s information flow, logistics, and business flow. Through detailed empirical analysis of each subsystem, we judge the development trend of the total grain system, perform operational tests and historical tests on the simulation results of the model to judge the rationality of the model system structure and simulation prediction, and give the simulation results. Finally, based on the forecast results, targeted countermeasures and suggestions are proposed.http://dx.doi.org/10.1155/2021/6496713 |
| spellingShingle | Xiaoyan Xu Zhongye Sun Digital Technology Empowers Grain Supply Chain Optimization Simulation Complexity |
| title | Digital Technology Empowers Grain Supply Chain Optimization Simulation |
| title_full | Digital Technology Empowers Grain Supply Chain Optimization Simulation |
| title_fullStr | Digital Technology Empowers Grain Supply Chain Optimization Simulation |
| title_full_unstemmed | Digital Technology Empowers Grain Supply Chain Optimization Simulation |
| title_short | Digital Technology Empowers Grain Supply Chain Optimization Simulation |
| title_sort | digital technology empowers grain supply chain optimization simulation |
| url | http://dx.doi.org/10.1155/2021/6496713 |
| work_keys_str_mv | AT xiaoyanxu digitaltechnologyempowersgrainsupplychainoptimizationsimulation AT zhongyesun digitaltechnologyempowersgrainsupplychainoptimizationsimulation |