Abstract:  From the perspective of improving the environmental benefits of the agricultural machinery supply chain, this paper comprehensively considers the energy consumption of industrial Internet resources and the characteristics of knowledge accumulation of multiple participation tasks. Based on the influence of learning rate on service quality, a onetoone multilateral matching model is established to maximize the service satisfaction of agricultural machinery service demander, agricultural machinery service provider, and platform energy consumption. Taking the production of a certain agricultural machinery cover mold on the industrial Internet collaborative management platform of Tianjin agricultural machinery supply chain as an example, the optimal matching scheme is obtained. Compared with the general matching model, the model without considering the learning effect, and the model without considering the energy consumption, the number of nondominant solutions in this model is 6, which is more than the other three models. At the same time, when considering the learning effect, the agricultural machinery manufacturing service with a higher learning rate is selected to complete the task. When considering the energy consumption, the low energy consumption agricultural machinery cloud manufacturing service combination is selected to complete the task. This means that in the Industrial Internet environment, considering both the learning effect and energy consumption of agricultural machinery cloud manufacturing services in the agricultural machinery supply chain in the process of bilateral matchingis more in line with the actual industry needs and can achieve a winwin situation between the environmental performance and economic performance.

Key words:  Industrial Internet, multilateral matching, environmental benefits, agricultural machinery supply chain

摘要： 从改善农机供应链环境效益视角出发,综合考虑了工业互联网资源的能源消耗以及服务具有自我学习能力的特征,通过考虑学习率对服务质量的影响,建立了以农机服务需求方、农机服务提供方服务满意度以及平台能耗满意度最大化的一对一多边匹配模型。以天津农机供应链工业互联网协同管理平台上某农机覆盖件模具生产为例,运用本文所建立模型求得最优服务方案,验证了本文所提模型与一般匹配、不考虑学习效应、不考虑能耗的3种模型相比,所产生的非劣解个数为6个,均多于其他3种模型;同时,在考虑学习效应时,学习率较高的农机制造服务被选择用以完成任务;在考虑能耗时,能耗较低的农机云制造服务组合被选择用以完成任务。这就意味着,在工业互联网环境下,将农机供应链中的农机云制造服务的学习效应以及能耗同时考虑到双边匹配过程中,更加符合现实农机供应链的现实需求并达到供应链的环境绩效和经济绩效的双赢。

关键词: 工业互联网, 多边匹配, 环境效益, 农机供应链