Dynamic Scheduling Strategy for Shared Agricultural Machinery for On-Demand Farming Services

Li Ma, Minghan Xin, Yi-Jia Wang () and Yanjiao Zhang
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Li Ma: College of Engineering, Northeast Agricultural University, Harbin 150030, China
Minghan Xin: College of Engineering, Northeast Agricultural University, Harbin 150030, China
Yi-Jia Wang: Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong 999077, China
Yanjiao Zhang: Baoneng Automobile Group, Shenzhen 518000, China

Mathematics, 2022, vol. 10, issue 21, 1-22

Abstract: With the development of the “Internet +” model and the sharing economy model, the “online car-hailing” operation model has promoted the emergence of “online-hailing agricultural machinery”. This new supply and demand model of agricultural machinery has brought greater convenience to the marketization of agricultural machinery services. However, although this approach has solved the use of some agricultural machinery resources, it has not yet formed a scientific and systematic scheduling model. Referring to the existing agricultural machinery scheduling modes and the actual demand of agricultural production, based on the idea of resource sharing, in this research, the soft and hard time windows were combined to carry out the research on the dynamic demand scheduling strategy of agricultural machinery. The main conclusions obtained include: (1) Based on the ideas of order resource sharing and agricultural machinery resource sharing, a general model of agricultural machinery scheduling that meet the dynamic needs was established, and a more scientific scheduling plan was proposed; (2) Based on the multi-population coevolutionary genetic algorithm, the dynamic scheduling scheme for shared agricultural machinery for on-demand farming services was obtained, which can reasonably insert the dynamic orders on the basis of the initial scheduling scheme, and realize the timely response to farmers’ operation demands; (3) By comparing with the actual production situation, the path cost and total operating cost were saved, thus the feasibility and effectiveness of the scheduling model were clarified.

Keywords: agricultural machinery scheduling; online-hailing agricultural machinery; co-evolutionary genetic algorithm; dynamic demand analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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