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Study of the strategy for agricultural machinery maintenance in China based on the improved genetic-bee colony algorithm

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Abstract

A service evaluation model and a solution strategy for agricultural machinery maintenance services based on big data areproposed considering the characteristics of a large business volume, a diversity of service types, a wide geographical distribution and on-site service for agricultural machinery maintenance services in China. First, a maintenance order priority evaluation model is developed based onanevaluation model of quality of service (QoS), and order confirmation is followed by developing a staffing evaluation model. A fuzzy analytical hierarchy process is used to determine the weights of the evaluation indicators. The evaluation models are solved by an improved genetic-bee colony algorithm (GAABC). In the selection operation stage of the genetic algorithm, a combination of an elite retention strategy and a roulette strategy is adopted, which not only guarantees the convergence speed of the algorithm and the diversity of individuals, but also prevents the loss of good genes of individual parents after the crossover and mutation operations. The attractor is introduced at the onlooker bee stage, where the onlooker bees shrink in proportion to the attractor at the center, thereby increasing the convergence speed and algorithm development for subsequent stages, as well as developing the area at the current stage. The mutation operation is added to the artificial bee colony algorithm (ABC) according to the degree of honey source aggregation to improve the local search ability. In addition, the best fusion point assessment strategy is used to determine the switching time between the genetic algorithm (GA) and the artificial bee colony algorithm to increase the convergence speed and accuracy of the solution. Finally, the feasibility and effectiveness of the agricultural machinery maintenance service model and the solution algorithm are verified by simulation experiments. This research provides theoretical support for the decision analysis of agricultural machinery maintenance enterprises in China.

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Acknowledgements

 This study was carried out with the support of the Natural Science Foundation of Shandong Province (ZR2022QE103), the University Youth lnnovation Science and Technology Support Program of Shandong Province, the Key Research and Development Plan Project of Shandong Province (2021CXG010813, 2022SFGC0203), the China Agriculture Research System of MOF and MARA (CARS-24-D-01).

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Authors and Affiliations

  1. College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, 271018, China

    Kai Zhou, Zhiyong Ni, Yongcheng Yin, Tianhua Li & Jialin Hou

  2. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China

    Bo Yang

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  1. Kai Zhou
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Correspondence to Tianhua Li or Jialin Hou.

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Zhou, K., Ni, Z., Yin, Y. et al. Study of the strategy for agricultural machinery maintenance in China based on the improved genetic-bee colony algorithm. J Ambient Intell Human Comput 14, 2275–2289 (2023). https://doi.org/10.1007/s12652-022-04485-6

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  • DOI: https://doi.org/10.1007/s12652-022-04485-6

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