Modeling of the Fresh Fruit Bunches Collection and Transportation System with Agent-Based Simulation in Optimizing the Mode of Transportation on Oil Palm Industry
Article Sidebar
Main Article Content
Abstract
Uncertainty number of harvests per day causes the allocation of transportation modes are less optimal in collecting fruit from plantations to palm oil mills causing a shortage of trucks in some afdeling and some are unemployed in some afdeling causing large transportation costs. The use of Agent-Based Simulations can help to identify the factors that cause the number of trucks that are not optimal by looking at the behavior and interaction between the agent actors involved in the tbs collection and transportation process. The results showed the utilization rate of transportation modes increased from 27% to 40% by reducing the number of truck units rented from 177 trucks to 118 trucks and reducing transportation collection costs by 0.225% of the total cost value. With the reduced journey time, the quality of the oil palm is better with the reduction of free fatty acids (FFA) and the average weight loss of fruit.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Pujawan, Nyoman. (2017). Supply Chain Management. Guna Widya. Jakarta
Hakim, Memet. (2017). Kelapa Sawit, Teknis Agronomis dan Manajemen. Lembaga Pupuk Indonesia. Jakarta
Napitupulu, Humala. (2009). Simulasi Sistem Pemodelan dan Analisis. Usu Press Medan
Siegfried, Robert. (2017). Modelling and Simulation of Complex System. Springer Vieweg
Turner, A. P., Sama, M. P., McNeill, L. S. G., Dvorak, J. S., Mark, T., & Montross, M. D. (2019). A discrete event simulation model for analysis of farm scale grain transportation systems. Computers and Electronics in Agriculture, 167(October), 105040.
https://doi.org/10.1016/j.compag.2019.105040
Bakker, C., Zaitchik, B. F., Siddiqui, S., Hobbs, B. F., Broaddus, E., Neff, R. A., … Parker, C. L. (2018). Shocks, seasonality, and disaggregation: Modelling food security through the integration of agricultural, transportation, and economic systems. Agricultural Systems, 164(April), 165–184. https://doi.org/10.1016/j.agsy.2018.04.005
Liu, L., Wang, H., & Xing, S. (2019). Optimization of distribution planning for agricultural products in logistics based on degree of maturity. Computers and Electronics in Agriculture, 160(February), 1–7. https://doi.org/10.1016/j.compag.2019.02.030
Lopes, H. dos S., Lima, R. da S., Leal, F., & Nelson, A. de C. (2017). Scenario analysis of Brazilian soybean exports via discrete event simulation applied to soybean transportation: The case of Mato Grosso State. Research in Transportation Business and Management, 25(February), 66–75. https://doi.org/10.1016/j.rtbm.2017.09.002
Shu, K., Schneider, U. A., & Scheffran, J. (2017). Optimizing the bioenergy industry infrastructure: Transportation networks and bioenergy plant locations. Applied Energy, 192, 247–261. https://doi.org/10.1016/j.apenergy.2017.01.092
Kim, S., Kim, S., & Kiniry, J. R. (2018). Two-phase simulation-based location-allocation optimization of biomass storage distribution. Simulation Modelling Practice and Theory, 86, 155–168. https://doi.org/10.1016/j.simpat.2018.05.006
Chiadamrong, N., & Kawtummachai, R. (2008). A methodology to support decision-making on sugar distribution for export channel: A case study of Thai sugar industry. Computers and Electronics in Agriculture, 64(2), 248–261. https://doi.org/10.1016/j.compag.2008.05.018
Fernandez-Mena, H., Gaudou, B., Pellerin, S., MacDonald, G. K., & Nesme, T. (2020). Flows in Agro-food Networks (FAN): An agent-based model to simulate local agricultural material flows. Agricultural Systems, 180 (February), 102718.
https://doi.org/10.1016/j.agsy.2019.102718
Mogale, D. G., Kumar, S. K., Márquez, F. P. G., & Tiwari, M. K. (2017). Bulk wheat transportation and storage problem of public distribution system. Computers and Industrial Engineering, 104, 80–97. https://doi.org/10.1016/j.cie.2016.12.027
Parthanadee, P., & Buddhakulsomsiri, J. (2010). Simulation modeling and analysis for production scheduling using real-time dispatching rules: A case study in canned fruit industry. Computers and Electronics in Agriculture, 70(1), 245–255. https://doi.org/10.1016/j.compag.2009.11.0
Allaoui, H., Guo, Y., Choudhary, A., & Bloemhof, J. (2018). Sustainable agro-food supply chain design using two-stage hybrid multi-objective decision-making approach. Computers and Operations Research, 89, 369–384. https://doi.org/10.1016/j.cor.2016.10.012
Asgari, N., Farahani, R. Z., Rashidi-Bajgan, H., & Sajadieh, M. S. (2013). Developing model- based software to optimise wheat storage and transportation: A real-world application. Applied Soft Computing Journal, 13(2), 1074–1084. https://doi.org/10.1016/j.asoc.2012.10.002
Benis, K., Reinhart, C., & Ferrão, P. (2017). Development of a simulation-based decision support workflow for the implementation of Building-Integrated Agriculture (BIA) in urban contexts. Journal of Cleaner Production, 147, 589–602. https://doi.org/10.1016/j.jclepro.2017.01.130 [18
Benke, K. K., Wyatt, R. G., & Sposito, V. A. (2011). A discrete simulation approach to spatial allocation of commodity production for revenue optimisation over a local region. Journal of Spatial Science, 56(1), 89–101. https://doi.org/10.1080/14498596.2011.567417