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Optimization Model and Application for Agricultural Machinery Systems Based on Timeliness Losses of Multiple Operations

Author

Listed:
  • Jian Sun

    (College of Engineering, Northeast Agricultural University, Harbin 150030, China
    Heilongjiang Province Technology Innovation Center of Mechanization and Materialization of Major Crops Production, Harbin 150030, China
    These authors contributed equally to this work.)

  • Yiming Zhang

    (College of Engineering, Northeast Agricultural University, Harbin 150030, China
    Heilongjiang Province Technology Innovation Center of Mechanization and Materialization of Major Crops Production, Harbin 150030, China
    These authors contributed equally to this work.)

  • Haitao Chen

    (College of Engineering, Northeast Agricultural University, Harbin 150030, China
    Heilongjiang Province Technology Innovation Center of Mechanization and Materialization of Major Crops Production, Harbin 150030, China
    College of Mechanical and Electronic Engineering, East University of Heilongjiang, Harbin 150066, China)

  • Jinyou Qiao

    (College of Engineering, Northeast Agricultural University, Harbin 150030, China
    Heilongjiang Province Technology Innovation Center of Mechanization and Materialization of Major Crops Production, Harbin 150030, China)

Abstract

Present agricultural practices confront issues such as mismatches between tractors and implements, imprecise machinery allocation, and excessive machinery investment. Optimization of agricultural machinery systems was a potent remedy for these concerns. To address inaccuracies in calculating objective functions and the incompleteness of constraints in existing models for agricultural machinery system optimization, a comprehensive mixed integer nonlinear programming (MINP) model for agricultural machinery system optimization was established. The model introduced timeliness loss costs for multiple key operations across various crops into the objective function, and constraints were enhanced by including operation sequence constraints and boundary constraints on initiation and completion dates of those key operations. Taking corn and soybeans as examples, timeliness loss functions of sowing and harvesting operations were derived through experiments. Solving the MINP model by Lingo (V.14.0) software, improvements in total power, workload per unit power, and total operational costs were shown when comparing the optimized machinery system through the MINP model against current systems. When the model omitted considerations for timeliness loss functions and operation sequence constraints, issues arose including an increase in total operational costs and an inversion of operation sequence. The model’s application in devising machinery allocation plans for production units of various operational scales revealed a gradual decrease in total power and costs per unit area with expanding scale, approaching stability when scale exceeded 1600 hm 2 . This study enriches theory and methodology for optimizing agricultural machinery systems, provides theoretical and technological underpinnings for rational machinery acquisition, and promotes the high-quality progression of comprehensive agricultural mechanization.

Suggested Citation

  • Jian Sun & Yiming Zhang & Haitao Chen & Jinyou Qiao, 2023. "Optimization Model and Application for Agricultural Machinery Systems Based on Timeliness Losses of Multiple Operations," Agriculture, MDPI, vol. 13(10), pages 1-19, October.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:10:p:1969-:d:1256102
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    References listed on IDEAS

    as
    1. Jinwu Wang & Xiaobo Sun & Yanan Xu & Wenqi Zhou & Han Tang & Qi Wang, 2021. "Timeliness Harvesting Loss of Rice in Cold Region under Different Mechanical Harvesting Methods," Sustainability, MDPI, vol. 13(11), pages 1-18, June.
    2. Anderson Hoose & Víctor Yepes & Moacir Kripka, 2021. "Selection of Production Mix in the Agricultural Machinery Industry Considering Sustainability in Decision Making," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    3. Michał Cupiał & Zbigniew Kowalczyk, 2020. "Optimization of Selection of the Machinery Park in Sustainable Agriculture," Sustainability, MDPI, vol. 12(4), pages 1-15, February.
    Full references (including those not matched with items on IDEAS)

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