Optimal scheduling of agricultural machines in hilly mountainous areas based on NSGA-Ⅱ-SA hybrid algorithm with applications
DOI:
https://doi.org/10.25165/ijabe.v%25vi%25i.9106Keywords:
multi-region agricultural machine scheduling, hilly mountainous area, hybrid optimization algorithm, agricultural machine scheduling systemAbstract
Optimizing the scheduling of farm machinery is essential to meet farmers’ requirements, minimize scheduling costs, and save time. This study focuses on scheduling farm machinery in multiple cooperatives across various regions, aiming to minimize scheduling costs and reduce scheduling time. Initially, a multi-constraint hybrid clustering algorithm is employed to assign farmland to each farm machinery cooperative by clustering before scheduling. Subsequently, an enhanced version of the Non-dominated Sorting Genetic Algorithm-Ⅱ (NSGA-Ⅱ) is proposed, integrating a local search strategy based on congestion-based neighborhood search and the Simulated Annealing (SA) algorithm to develop the NSGA-Ⅱ-SA algorithm. This hybrid multi-objective evolutionary algorithm effectively optimizes scheduling costs and time. The model’s validity and the algorithm’s superiority are demonstrated through a Web-based multi-region agricultural machine scheduling system and an example study. Experimental results show that the NSGA-Ⅱ-SA algorithm significantly reduces scheduling costs and time, as well as the number of scheduled farm machines, outperforming other algorithms with reductions of 9.8%, 3.1%, and 8.7% in total scheduling costs, and 12.5%, 13.4%, and 11.6% in total scheduling time. This research establishes a theoretical framework for multi-region agricultural machine scheduling in hilly and mountainous areas, enhancing agricultural production efficiency. Keywords: multi-region agricultural machine scheduling, hilly mountainous area, hybrid optimization algorithm, agricultural machine scheduling system DOI: 10.25165/j.ijabe.20251805.9106 Citation: Liu H Y, Luo J H, Zhang L H, Wang F L, Wang S. Optimal scheduling of agricultural machines in hilly mountainous areas based on NSGA-Ⅱ-SA hybrid algorithm with applications. Int J Agric & Biol Eng, 2025; 18(5): 234–245.References
Huang H, Cuan, X W, Chen Z, Zhang L N, Chen H. A multiregional agricultural machinery scheduling method based on hybrid particle swarm optimization algorithm. Agriculture, 2023; 13（5）: 1042.
Liu Y M, Hu W Y, Jetté-Nantel S, Tian Z H. The influence of labor price change on agricultural machinery usage in Chinese agriculture. Canadian Journal of Agricultural Economics/Revue Canadienne d’Agroeconomie, 2014; 62（2）: 219–243.
Sims B, Heney J. Promoting smallholder adoption of conservation agriculture through mechanization services. Agriculture, 2017; 7（8）: 64.
Yang S, Li W. The Impact of Socialized Agricultural Machinery Services on Land Productivity: Evidence from China. Agriculture, 2022; 12（12）: 2072.
Sun Y, Zhao Z, Li M. Coordination of agricultural informatization and agricultural economy development: A panel data analysis from Shandong Province, China. PlosOne, 2022; 17（9）: e0273110.
Martin P L, Olmstead A L. The agricultural mechanization controversy. Science, 1985; 227（4687）: 601–606.
Li W. Research on scheduling algorithm of agricultural machinery cooperative operation based on particle swarm neural network. Advances in Multimedia, 2022; 2022（1）: 1231642.
Zhao B, Zheng D, Yang C, Wang S, Mansurova M, Jomartova S, et al. Design and optimization of an internet of things-based cloud platform for autonomous agricultural machinery using narrowband internet of things and 5G dual-channel communication. Electronics, 2025; 14（8）: 1672.
Liu X, Zhu X, Hao K. Dynamic immune cooperative scheduling of agricultural machineries. Complex & Intelligent Systems, 2021; 7: 2871–2884.
Liu H, Luo J, Zhang L, Yu H, Liu X, Wang S. Research on Traversal Path Planning and Collaborative Scheduling for Corn Harvesting and Transportation in Hilly Areas Based on Dijkstra’s Algorithm and Improved Harris Hawk Optimization. Agriculture, 2025; 15（3）: 233.
Liu C, Qian Y. Optimal allocation of material dispatch in emergency events using multi-objective constraint for vehicular networks. Wireless Networks, 2022; 28（8）: 3715–3727.
Jiang J, Ma J, Chen X. Multi-regional collaborative mechanisms in emergency resource reserve and pre-dispatch design. International Journal of Production Economics, 2024; 270: 109161.
Ma L, Xin M, Wang Y J. Zhang Y. Dynamic scheduling strategy for shared agricultural machinery for on-demand farming services. Mathematics, 2022; 10（21）: 3933.
Katoch S, Chauhan S S, Kumar V. A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications, 2021; 80: 8091–8126.
Khalid Q S, Azim S, Abas M, Babar A R, Ahmad I. Modified particle swarm algorithm for scheduling agricultural products. Engineering Science and Technology, an International Journal, 2021; 24（3）: 818–828.
Li S, Zhang M, Wang N, Cao R, Zhang Z, Ji Y, Li H, Wang H. Intelligent scheduling method for multi-machine cooperative operation based on NSGA-Ⅲ and improved ant colony algorithm. Computers and Electronics in Agriculture, 2023; 204: 107532.
Rodias E C, Sopegno A, Berruto R, Bochtis D D, Cavallo E, Busato P. A combined simulation and linear programming method for scheduling organic fertiliser application. Biosystems Engineering, 2019; 178: 233–243.
He P, Li J, Wang X. Wheat harvest schedule model for agricultural machinery cooperatives considering fragmental farmlands. Computers and Electronics in Agriculture, 2018; 145: 226–234.
Cao R, Guo Y, Zhang Z, Li S, Zhang M, Li H, Li M. Global path conflict detection algorithm of multiple agricultural machinery cooperation based on topographic map and time window. Computers and Electronics in Agriculture, 2023; 208: 107773.
Worasan K, Sethanan K, Pitakaso R, Moonsri K, Nitisiri K. Hybrid particle swarm optimization and neighborhood strategy search for scheduling machines and equipment and routing of tractors in sugarcane field preparation. Computers and Electronics in Agriculture, 2020; 178: 105733.
Ding C, Wang L, Chen X, Yang H, Huang L, Song X. A blockchain-based wide-area agricultural machinery resource scheduling system. Applied Engineering in Agriculture, 2023; 39（1）: 1–12.
Borodin V, Bourtembourg J, Hnaien F, Labadie N. A quality risk management problem: case of annual crop harvest scheduling. International Journal of Production Research, 2014; 52（9）: 2682–2695.
Foulds L R, Wilson J M. Scheduling operations for the harvesting of renewable resources. Journal of food engineering 2005; 70（3）: 281–292. doi:10.1016/j.jfoodeng.2003.12.009.
Basnet C B, Foulds L R, Wilson J M. Scheduling contractors’ farm-to-farm crop harvesting operations. International Transactions in Operational Research, 2006; 13（1）: 1–15.
Ma L, Wang Y, Ma M, Bai J. Research on multi-cooperative combine-integrated scheduling based on improved NSGA-Ⅱ algorithm. International Journal of Agricultural and Environmental Information Systems （IJAEIS）, 2021; 12（4）: 1–21.
Tan B, Çömden N. Agricultural planning of annual plants under demand, maturation, harvest, and yield risk. European Journal of Operational Research, 2012; 220（2）: 539–549.
Bochtis D D, Sørensen C G C, Busato P. Advances in agricultural machinery management: A review. Biosystems Engineering, 2014; 126: 69–81.
Kumar M, Guria C. The elitist non-dominated sorting genetic algorithm with inheritance （i-NSGA-Ⅱ） and its jumping gene adaptations for multi-objective optimization. Information Sciences, 2017; 382: 15–37.
Han Y, Shao M, Wu Y, Zhang X. An improved complete coverage path planning method for intelligent agricultural machinery based on backtracking method. Information, 2022; 13（7）: 313.
Zhang J, Li D. Research on path tracking algorithm of green agricultural machinery for sustainable development. Sustainable Energy Technologies and Assessments, 2023; 55: 102917.
Oksanen T, Visala A. Coverage path planning algorithms for agricultural field machines. Journal of Field Robotics, 2009; 26（8）: 651–668.
Martínez-Vargas A, Domínguez-Guerrero J, Andrade Á G, Sepúlveda R, Montiel-Ross O. Application of NSGA-Ⅱ algorithm to the spectrum assignment problem in spectrum sharing networks. Applied Soft Computing, 2016; 39: 188–198.
Yuan M, Li Y, Zhang L, Pei F. Research on intelligent workshop resource scheduling method based on improved NSGA-Ⅱ algorithm. Robotics and Computer-Integrated Manufacturing, 2021; 71: 102141.
Khoshahval F, Zolfaghari A, Minuchehr H, Abbasi M. A new hybrid method for multi-objective fuel management optimization using parallel PSO-SA. Progress in Nuclear Energy, 2014; 76: 112–121.
Downloads
Published
How to Cite
Issue
Section
License
IJABE is an international peer reviewed open access journal, adopting Creative Commons Copyright Notices as follows.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
