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Based on the information commons service model, the aim of this article is to propose a new model for knowledge commons. It seeks to define the conceptual model and constructing framework of knowledge commons, which aim for a collaborative knowledge‐sharing environment to support innovative community activities of university library.

By analyzing the innovation activities of communities, infusing theories of knowledge management, collaboration and Library 2.0, the knowledge commons conceptual model is brought forward to improve communication, collaboration, sharing and conversation.

Since the innovative community is interdisciplinary and cross‐campus, the scattered research team and study group requires a library to extend the services to a logical system, while the virtual layer is to make this spatial decentralized and logically centralized system a reality. The core elements, namely information technology, organization and management, culture and spirit, make up the supporting layer, of which, trust and collaboration culture for innovation is important.

Research work and practice of information commons and library 2.0 have aroused a new round of the library service movement, while the knowledge commons conceptual model would provide steering for a knowledge sharing environment.

In this paper, the new model is based on information commons and assimilating the theories of knowledge management, collaboration and Library 2.0, intended to integrate digital library, physical resource, virtual resource and human resource into a whole. It is of great importance for the library to serve education and scientific research well.

Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.

This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.

The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.

The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.

In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.