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Home > Vol 35, No 3 (2025) > Siriruk

การปรับปรุงขั้นตอนวิธีค้นหาแบบแมลงหวี่เพื่อแก้ปัญหาการจัดตารางการเก็บเกี่ยวพืช
The Modified Fruit Fly Optimization Algorithm for Solving Harvest Scheduling

Pavee Siriruk, Apicha Kotekangpoo

Abstract


งานวิจัยนี้มีวัตถุประสงค์เพื่อนำเสนอการพัฒนาอัลกอริทึมด้วยวิธีค้นหาแบบแมลงหวี่ เพื่อใช้ในการแก้ปัญหาการจัดตารางการเก็บเกี่ยวพืชของบริษัทแปรรูปสินค้าเกษตรให้มีผลกำไรตอบแทนต่อปีสูงสุด ขั้นตอนวิธีค้นหาแบบแมลงหวี่ถูกปรับให้เหมาะสมในรูปแบบของเมทริกซ์ เพื่อกำหนดพื้นที่เก็บเกี่ยวในแต่ละเดือนให้มีปริมาณผลผลิตสูงสุดต่อปี ตัวอย่างแบบจำลองทางคณิตศาสตร์สำหรับปัญหาการเก็บเกี่ยวพืชจำนวน 12 ถึง 60 แปลง นำมาใช้เป็นกรณีศึกษาเพื่อตรวจสอบความสามารถของอัลกอริทึมในปัญหาขนาดเล็ก ขนาดกลาง และขนาดใหญ่ เมทริกซ์คำตอบได้รับการปรับปรุงโดยการพิจารณาจุดตัดเพื่อป้องกันการติดคำตอบท้องถิ่น และเพิ่มขีดความสามารถในการค้นหาคำตอบ ผลการทดลองพบว่า วิธีการค้นหาแบบแมลงหวี่สามารถทำให้บริษัทได้รับปริมาณผลผลิตเพียงพอต่อการแปรรูปในแต่ละเดือน รวมทั้งได้รับผลกำไรตอบแทนต่อปีสูงสุด เทียบเท่ากับการจำลองปัญหาผ่านโปรแกรมสำเร็จรูป GAMS ที่สามารถหาคำตอบที่ดีที่สุดได้ในทุก ๆ ขนาดปัญหา

This research develops the Fruit Fly Optimization Algorithm (FOA) to achieve the objectives of maximizing annual profits in a processing company's crop harvest scheduling problem. The fruit fly algorithm is adapted as a matrix to determine the monthly harvest area with the highest annual yield. The crop harvesting problems ranging from 12–60 plots are used to verify the algorithm's capability in small, medium, and large-scale problems. The matrix intersection technique is used to test different problem sizes in order to escape local optima and develop better answers. The results show that the fruit fly optimization algorithm allows the company to obtain sufficient monthly crops for processing. In addition, the highest annual profit has the same optimal value as formulating mathematical models through GAMS in all problem sizes.


Keywords



[1] Songkhla Rajabhat University (SKRU). [Online] (in Thai). Available: http://oservice.skru.ac.th/ ebookft/ 374/chapter8.pdf.

[2] C. S. Chen, “Scheduling of planting and harvesting programmes for processing vegetables,” Journal of Agricultural Engineering Research, vol. 19, no. 1, pp. 51– 57, 1974.

[3] M. R. Garey, D. S. Johnson, and R. Sethi, “The Complexity of Flowshop and Jobshop Scheduling,” Mathematics of Operations Research, vol. 1, no. 2, pp. 117–129, 1976.

[4] S. Bandaru and K. Deb, “Metaheuristic techniques,” in Decision Sciences, Boca Raton, FL: CRC Press, 2016, pp. 693–750.

[5] E. W. Richards and E. A. Gunn, “A model and tabu search method to optimize stand harvest and road construction schedules,” Forest Science, vol. 46, no. 2, pp. 188–203, 2000.

[6] M. Pant, R. Thangaraj, D. Rani, A. Abraham, and D. Srivastava, “Estimation of optimal crop plan using nature inspired meta- heuristics,” World Journal of Modelling and Simulation, vol. 6, pp. 97–109, 2010.

[7] B. J. Stray, J. H. van Vuuren, and C. N. Bezuidenhout, “ A n o p t i m i s a t i o n - b a s e d s e a s o n a l sugarcane harvest scheduling decision support system for commercial growers in South Africa,” Computers and Electronics in Agriculture, vol. 83, pp. 21–31, 2012.

[8] A. Filho, H. Florentino, and M. Pato, “A genetic algorithm for crop rotation,” in Proceedings ICORES, 2012, pp. 454–457.

[9] W. Neungmatcha and K. Sethanan, “Optimal mechanical harvester route planning for sugarcane field operations using particle swarm optimization,” Engineering and Applied Science Research, vol. 42, no. 2, pp. 125–133, 2015.

[10] A. Rath, S. Biswal, S. Samantaray, and P. Swain, “Derivation of optimal cropping pattern in part of hirakud command using cuckoo search,” in Proceedings ICMAEM, 2017.

[11] E. Afifah, A. Alamsyah, and E. Sugiharti, “Scheduling optimization of sugarcane harvest using simulated annealing algorithm,” Scientific Journal of Informatics, vol. 5, pp. 138–147, 2018.

[12] U. Ketsripongsa, R. Pitakaso, K. Sethanan, and T. Srivarapongse, “An improvement differential evolution algorithms for the crop planning in the northeastern region of Thailand,” Mathematical and Computational Applications, vol. 23, no. 3, 2018.

[13] R. Kommadath, D. Maharana, R. Anandalakshmi, and P. Kotecha, “Multi- objective scheduling in the vegetable processing and packaging facility using metaheuristic-based framework,” Food and Bioproducts Processing, vol. 137, pp. 1–19, 2023.

[14] S. Bhupender and K. Ashwani, “Introduction to Hybrid algorithm,” International Journal of Advance Research and Innovative Ideas in Education, vol. 8, no. 3, 2022.

[15] D. Mogale, D. A. Ghadge, S. Kumar, and M. Tiwari, “Modelling supply chain network for procurement of food grains in India,” International Journal of Production Research, vol. 58, 2019.

[16] V. Chouhan, B. S. H. Khan, and M. Hajiaghaei- Keshteli, “Sustainable planning and decisionmaking model for sugarcane mills considering environmental issues,” Journal of Environmental Management, vol. 303, 2022.

[17] H. d. O. Florentino, D. F. Jones, C. A. Irawan, D. Ouelhadj, B. Khosravi, and D. R. Cantane, “An optimization model for combined selecting, planting and harvesting sugarcane varieties,” Annals of Operations Research, vol. 314, no. 2, pp. 451–469, 2022.

[18] Y. Li and M. Han, “Improved fruit fly algorithm on structural optimization,” Brain informatics, vol. 7, no. 1, 2020.

[19] A. Tapao and R. Cheerarot, “Optimum design of reinforced concrete foundations by fruit fly optimization algorithm,” The Journal of KMUTNB, vol. 31, no. 1, pp. 16–24, 2021 (in Thai).

[20] İ. Babaoğlu, “Solving 2D strip packing problem using fruit fly optimization algorithm,” Procedia Computer Science, vol. 111, pp. 52–57, 2017.

[21] H. Iscan and M. Gunduz, “An application of fruit fly optimization algorithm for traveling salesman problem,” Procedia Computer Science, vol. 111, pp. 58–63, 2017.

[22] L. Wang, X.-l. Zheng, and S.-y. Wang, “A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem,” Knowledge-Based Systems, vol. 48, pp. 17–23, 2013.

[23] N. Mhuadthongon, “Linear Array Pattern Synthesis Using Chaotic Sequence Enhanced FOA Method,” Ladkrabang Engineering Journal, vol. 35, no. 3–4, pp. 47–54, 2018 (in Thai).

[24] X. Zhang, X. Liu, S. Tang, G. Królczyk, and Z. Li, “Solving scheduling problem in a distributed manufacturing system using a discrete fruit fly optimization algorithm,” Energies, vol. 12, no. 17, 2019.

[25] F. Xu and Y. Tao, “The Improvement of Fruit Fly Optimization Algorithm-Using Bivariable Function as Example,” Advanced Materials Research, vol. 756–759, pp. 2952–2957, 2013.

[26] I. Fikry, M. Gheith, and A. Eltawil, “An integrated production-logistics-crop rotation planning model for sugar beet supply chains,” Computers & Industrial Engineering, vol. 157, pp. 1–13, 2021.

[27] J. A. Diaz and E. Fernandez, “A tabu search heuristic for the generalized assignment problem,” European Journal of Operational Research, vol. 132, no. 1, pp. 22–38, 2001.

Full Text: PDF

DOI: 10.14416/j.kmutnb.2024.10.012

ISSN: 2985-2145