An Improved Truncated Distribution for Modelling the Procurement Planning of Agricultural Product

Authors

  • Siraprapha Deepradit Kasetsart University
  • Roongrat Pisuchpen

Keywords:

Agricultural Product, Procurement Planning, Mixed-Integer Non-Linear Programming, Truncated Distribution

Abstract

A procurement plan of agricultural products is important because agricultural products had uncertain factors involved make it difficult to plan. This research considered uncertainties that are factory demand, the number of supply coconuts purchased from contracted farms, the number of supply coconuts purchased from collectors, the coconut price purchased from collectors, the selling price which had different distributions each time. The mixed-integer non-linear programming was formulated and run for 500 trials. The objective was to maximize the annual total profit. The truncated distribution of the procurement plan was analyzed. The solution indicates how to define lower and upper truncated distributions. In addition, truncating the distribution interval in three cases: lower truncate case, upper truncate case, and doubly truncate case. By reducing the data interval by 5 to 20% when cutting the destination on the side or both ends. When comparing the non-truncated scenario to the lower truncated case, the result showed that the lower truncated case exhibited a profit increase of 3.60 percent. As a result, the importance of factors that are less likely to occur was reduced. Procurement planning becomes more profitable as a result.

References

[1] K. Willy, and A. Njeru, “Effects of Procurement Planning on Procurement Performance: A Case Study of Agricultural Development Corporation, Nairobi,” International Journal of Business and Commerce., Vol. 3 (No.12), pp.58-68, 2014.
[2] A.O. Ogwang and L. Waweru, “Influence of Procurement Planning on Performance of Kisumu Water and Sewerage Company Limited, Kenya,” International Journal of Economics Commerce and Management., Vol. 5 (No.5), pp.767–789, 2017.
[3] X. Du, Leung S., Zhang L.J. and K. K. Lai, “Procurement of Agricultural Products Using the CPFR Approach,” Supply Chain Management: An International Journal., Vol.14 (No.4), pp.253-258, 2009.
[4] F. Aziz, M. Panitra and A. K. Rivai, “Synthesis and Monte Carlo Simulation of Improved Concrete Composites for Enhanced X-Ray/Gamma-Ray Radiation Shielding,” International Journal of Technology., Vol.9 (No.4), pp.695–706, 2018.
[5] S. Deepradit, R. Pisuchpen and P. Ongkunaruk, “The Harvest Planning of Aromatic Coconut by Using Monte Carlo Simulation,” The 2017 4th International Conference on Industrial Engineering and Applications (ICIEA). 21-23 April 2017: pp. 116–120, 2017.
[6] N. J. Haaning and A. J. Brandstrup, Monte Carlo Simulation in Crystal Ball 7.3: AARHUS University, 2008.
[7] A. Jareonkitpoolpol, P. Ongkunaruk and G. K. Janssens, “Determination of the Optimal Blending Problem of Organic-Chemical Fertilizer under Uncertainty,” Soil Use and Management., Vol.34 (No.4), pp.449–460, 2018.
[8] R. Pisuchpen and P. Ongkunaruk, “Simulation for Production Line Balancing of a Large-Sized Frozen Chicken Manufacturer,” Actual Problems of Economics., Vol.177 (No.3), pp.397-406, 2016.
[9] T. Zhang and M. Xie, “On the Upper Truncated Weibull Distribution and Its Reliability Implications,” Reliability Engineering and System Safety., Vol.96 (No.1), pp.194-200, 2011.
[10] M. S. Tokmachev, “Modeling of Truncated Probability Distributions,” Materials Science and Engineering., Vol.441, pp.1-9, 2018.
[11] S. Chen and W. Gui, “Estimation of Unknown Parameters of Truncated Normal Distribution under Adaptive Progressive Type II Censoring Scheme,” Mathematics., Vol.49, 2021. https://doi. org/10.3390/math9010049
[12] X. Zeng, and W. Gui, “Statistical Inference of Truncated Normal Distribution Based on the Generalized Progressive Hybrid Censoring,” Entropy., Vol.23, pp.186-215, 2021.
[13] A. Gul, M. Mohsin, M. Adil and M. Ali, “A Modified Truncated Distribution for Modeling the Heavy Tail,” Engineering and Environmental Sciences Data., Vol.16 (No.4), 2021. https://doi.org/10.1371/journal.pone.0249001
[14] S. Deepradit, P. Ongkunaruk and R. Pisuchpen, “Tactical Procurement Planning under Uncertainty in Aromatic Coconut Manufacturing,” International Journal of Technology., Vol.11 (No.4), pp. 698-709, 2020.
[15] J. Banks, J. S. Carson, B. C. Nelson and D. M. Nicol, 4th ed., Discrete-Event System Simulation. London: Pearson Education, 2005.
[16] จุฑา พิชิตลำเค็ญ, พิมพ์ครั้งที่ 1, พื้นฐานการจำลองสถานการณ์เชิงสุ่ม เพื่อการประยุกต์ใช้กับปัญหาจริง. กรุงเทพฯ: สำนักพิมพ์มหาวิทยาลัยเกษตรศาสตร์, 2558.
[17] Wikimedia Foundation, Inc. (17 กรกฎาคม 2564). Normal Distribution. [Online] Available: https://en.wikipedia.org/wiki/Normal_distribution
[18] Wikimedia Foundation, Inc. (17 กรกฎาคม 2564). Beta Distribution. [Online] Available: https://en.wikipedia.org/wiki/Beta_distribution
[19] Wikimedia Foundation, Inc. (17 กรกฎาคม 2564). Uniform Distribution. [Online] Available: https://en.wikipedia.org/wiki/Uniform_distribu tion
[20] Wikimedia Foundation, Inc. (17 กรกฎาคม 2564). Weibull Distribution. [Online] Available: https://en.wikipedia.org/wiki/Weibull_distribu
tion
[21] Wikimedia Foundation, Inc. (17 กรกฎาคม 2564). Triangular Distribution. [Online] Available: https://en.wikipedia.org/wiki/Triangular_distribu
tion
[22] J. Burkardt, “The Truncated Normal Distribution. Department of Scientific Computing Website,” Florida State University, pp.1-35. 2014.

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Published

2021-12-21