Optimisation of Fertiliser Delivery Distribution from Distributor to Farmer Group Using Excel Solver in Tanjung Village
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Excel Solver, Fertiliser Distribution, Optimisation
Abstract
Subsidised fertilisers are declared as goods that are monitored for distribution with a specific target, namely farmers. In the distribution process, the government cooperates with various components including the Ministry of Industry and Trade, the Ministry of Agriculture, the Ministry of State-Owned Enterprises and the Ministry of Home Affairs. At the same time, transportation and communication technologies continue to develop rapidly, such as mobile communications and the Internet, which drive the continuous development of the supply chain and technologies related to its regulatory management. This research aims to find out how to optimise the fertiliser transprortation Fertiliser Delivery Distribution and a series of fertiliser supply systems from Distributors to Farmer Groups Using Excel Solvers in Tanjung Village so as to provide better user control. The benefits of this research for distributors can help buffer distributor management to determine the quantity and time of delivery of subsidised fertiliser to official kiosks. The research method uses a case study which is a method of collecting and processing data through review / study of various research reports, flowsheets, research journals, as well as books and other relevant literature.
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References
Abbou-Ou-Cherif, E. M. (2019). On-the-field simulation of fertilizer spreading: Part 3 – Control of disk inclination for uniform application on undulating fields. Computers and Electronics in Agriculture, 158(Query date: 2024-07-17 17:53:26), 150–158. https://doi.org/10.1016/j.compag.2019.01.050
Ajeng, A. A. (2023). Biochar-Bacillus consortium for a sustainable agriculture: Physicochemical and soil stability analyses. Biochar, 5(1). https://doi.org/10.1007/s42773-023-00215-z
Alaan, M. (2023). Design of an Optimized Second-order Soil-based Drip Fertigation Control System with Internet-of-Things Integration. 2023 8th International Conference on Business and Industrial Research, ICBIR 2023 - Proceedings, Query date: 2024-07-17 17:53:26, 477–482. https://doi.org/10.1109/ICBIR57571.2023.10147572
Al-Rumaihi, A. (2023). Investigation of co-pyrolysis blends of camel manure, date pits and plastic waste into value added products using Aspen Plus. Fuel, 340(Query date: 2024-07-17 17:53:26). https://doi.org/10.1016/j.fuel.2023.127474
Amagada, P. U. (2023). An Inferable Machine Learning Approach for Reservoir Lithology Characterization Using Drilling Data. Proceedings - SPE Annual Technical Conference and Exhibition, 2023(Query date: 2024-07-17 17:53:26). https://doi.org/10.2118/217485-STU
Baccour, E. (2024). Multi-agent reinforcement learning for privacy-aware distributed CNN in heterogeneous IoT surveillance systems. Journal of Network and Computer Applications, 230(Query date: 2024-07-17 17:53:26). https://doi.org/10.1016/j.jnca.2024.103933
Bai, Z. (2024). Optimizing phosphorus fertilizer use to enhance water quality, food security and social equality. Resources, Conservation and Recycling, 203(Query date: 2024-07-17 17:53:26). https://doi.org/10.1016/j.resconrec.2023.107400
Chang, H. (2023). Mini-review of sewage sludge parameters related to system modelling. Waste Management and Research, 41(5), 970–976. https://doi.org/10.1177/0734242X221139171
Fan, H. (2022). Research progress on photo-physiological mechanisms and characteristics of canopy microenvironment in the formation of intercropping advantages. Chinese Journal of Eco-Agriculture, 30(11), 1750–1761. https://doi.org/10.12357/cjea.20220660
Han, Y. (2024). Development of an assessment-based planting structure optimization model for mitigating agricultural greenhouse gas emissions. Journal of Environmental Management, 349(Query date: 2024-07-17 17:53:26). https://doi.org/10.1016/j.jenvman.2023.119322
Huang, L. (2024). A Distributional Perspective on Multiagent Cooperation With Deep Reinforcement Learning. IEEE Transactions on Neural Networks and Learning Systems, 35(3), 4246–4259. https://doi.org/10.1109/TNNLS.2022.3202097
Kodong, F. (2019). Optimization and estimation framework of smart farm based on spatial data mining and geostatistics. IOP Conference Series: Materials Science and Engineering, 620(1). https://doi.org/10.1088/1757-899X/620/1/012097
Kozhushko, A. (2024). OPTIMIZATION OF TRACTION PROPERTIES THE ELECTRIC TRACTOR BASED ON THE SIMULATION DLG-POWERMIX TEST CYCLES. International Journal of Mechatronics and Applied Mechanics, 2024(15), 70–78. https://doi.org/10.17683/ijomam/issue15.9
Kravchuk, M. (2019). Change in soil agrophysical indicators and potato productivity in soil protection agrotechnologies. Scientific Horizons, 11, 61–68. https://doi.org/10.33249/2663-2144-2019-84-11-61-68
Liu, T. (2023). Local Resistance Characteristics of T-Type Tee Based on Chamfering Treatment. Sustainability (Switzerland), 15(19). https://doi.org/10.3390/su151914611
Min, J. (2020). Changes in planting structure and nitrogen and phosphorus loss loads of farmland in Taihu Lake region. Chinese Journal of Eco-Agriculture, 28(8), 1230–1238. https://doi.org/10.13930/j.cnki.cjea.200152
Rahman, M. A. E. A. (2021). Deciphering soil spatial variability through geostatistics and interpolation techniques. Sustainability (Switzerland), 13(1), 1–13. https://doi.org/10.3390/su13010194
Sharipov, G. M. (2021). Variable rate application accuracy of a centrifugal disc spreader using ISO 11783 communication data and granule motion modeling. Computers and Electronics in Agriculture, 182(Query date: 2024-07-17 17:53:26). https://doi.org/10.1016/j.compag.2021.106006
Shi, Y. (2021). Spatial Optimization of Watershed Best Management Practices based on Slope Position-Field Units. Journal of Geo-Information Science, 23(4), 564–575. https://doi.org/10.12082/dqxxkx.2021.200335
Shiyi, Y. (2023). High-resolution risk mapping of heavy metals in soil with an integrated static-dynamic interaction model: A case study in an industrial agglomeration area in China. Journal of Hazardous Materials, 455(Query date: 2024-07-17 17:53:26). https://doi.org/10.1016/j.jhazmat.2023.131650
Zeng, L. (2023). Re-coupling crop and livestock through spatial analysis and site selection of manure transfer hubs for sustainable agriculture. Agronomy for Sustainable Development, 43(5). https://doi.org/10.1007/s13593-023-00921-9
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