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  • Open Access


    Evaluating the Derivative Value of Smart Grid Investment under Dual Carbon Target: A Hybrid Multi-Criteria Decision-Making Analysis

    Na Yu1, Changzheng Gao2, Xiuna Wang2, Dongwei Li2,*, Weiyang You2

    Energy Engineering, Vol.120, No.12, pp. 2879-2901, 2023, DOI:10.32604/ee.2023.029426

    Abstract With the goal of “carbon peaking and carbon neutralization”, it is an inevitable trend for investing smart grid to promote the large-scale grid connection of renewable energy. Smart grid investment has a significant driving effect (derivative value), and evaluating this value can help to more accurately grasp the external effects of smart grid investment and support the realization of industrial linkage value with power grid investment as the core. Therefore, by analyzing the characterization of the derivative value of smart grid driven by investment, this paper constructs the evaluation index system of the derivative value of smart grid investment including… More >

  • Open Access


    Modelling Dry Port Systems in the Framework of Inland Waterway Container Terminals

    Milovan Kovač1, Snežana Tadić1, Mladen Krstić1,*, Violeta Roso2

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 1019-1046, 2023, DOI:10.32604/cmes.2023.027909

    Abstract Overcoming the global sustainability challenges of logistics requires applying solutions that minimize the negative effects of logistics activities. The most efficient way of doing so is through intermodal transportation (IT). Current IT systems rely mostly on road, rail, and sea transport, not inland waterway transport. Developing dry port (DP) terminals has been proven as a sustainable means of promoting and utilizing IT in the hinterland of seaport container terminals. Conventional DP systems consolidate container flows from/to seaports and integrate road and rail transportation modes in the hinterland which improves the sustainability of the whole logistics system. In this article, to… More >

  • Open Access


    Envisaging Employee Churn Using MCDM and Machine Learning

    Meenu Chaudhary1, Loveleen Gaur1, NZ Jhanjhi2,*, Mehedi Masud3, Sultan Aljahdali3

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1009-1024, 2022, DOI:10.32604/iasc.2022.023417

    Abstract Employee categorisation differentiates valuable employees as eighty per cent of profit comes from twenty per cent of employees. Also, retention of all employees is quite challenging and incur a cost. Previous studies have focused on employee churn analysis using various machine learning algorithms but have missed the categorisation of an employee based on accomplishments. This paper provides an approach of categorising employees to quantify the importance of the employees using multi-criteria decision making (MCDM) techniques, i.e., criteria importance through inter-criteria correlation (CRITIC) to assign relative weights to employee accomplishments and fuzzy Measurement Alternatives and Ranking according to the Compromise Solution… More >

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