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Environmental Cost Control of Manufacturing Enterprises via Machine Learning under Data Warehouse

Author

Listed:
  • Xiaohan Li

    (School of Public Administration, Shandong Normal University, Jinan 250014, China)

  • Chenwei Ma

    (School of Public Administration, Sichuan University, Chengdu 610065, China)

  • Yang Lv

    (College of Teachers, Chengdu University, Chengdu 610106, China)

Abstract

Environmental cost refers to the cost paid by enterprises to reduce environmental pollution and resource depletion in production and operation. To help enterprises reduce environmental costs, a manufacturing environmental cost control algorithm based on machine learning is proposed. The probabilistic neural network is used to classify the current environmental cost control level of different manufacturing enterprises. Then, the particle swarm optimization (PSO) algorithm is improved to build a multi-objective backbone PSO algorithm for multi-objective decision-making, which is used in the selection of environmental cost control methods. The experimental results show that there is a strong correlation between the original data classification and the proposed probabilistic neural network, and the correlation reaches 96.1%. PSO performance test results show that the algorithm has the best performance, the best stability, and the shortest time needed to find the optimal solution set when the initial particle number is 140 and the number of iterations is 60. Based on the comprehensive experimental results, the following conclusions are drawn. Enterprises should strengthen collaboration and cooperation with customers, suppliers, and waste-profiting enterprises, so as to well control environmental costs. To sum up, the proposed model provides some references for the adoption of machine learning in environmental cost control of manufacturing enterprises.

Suggested Citation

  • Xiaohan Li & Chenwei Ma & Yang Lv, 2022. "Environmental Cost Control of Manufacturing Enterprises via Machine Learning under Data Warehouse," Sustainability, MDPI, vol. 14(18), pages 1-21, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11571-:d:915573
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    References listed on IDEAS

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    1. Juan Pablo Usuga Cadavid & Samir Lamouri & Bernard Grabot & Robert Pellerin & Arnaud Fortin, 2020. "Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1531-1558, August.
    2. M.L. Meena & R. Jain & P. Kumar & S. Gupta & G.S. Dangayach, 2018. "Process improvement in an Indian automotive part manufacturing company: a case study," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 23(4), pages 524-551.
    3. Zhang, Xiaohong & Wu, Liqian & Zhang, Rong & Deng, Shihuai & Zhang, Yanzong & Wu, Jun & Li, Yuanwei & Lin, Lili & Li, Li & Wang, Yinjun & Wang, Lilin, 2013. "Evaluating the relationships among economic growth, energy consumption, air emissions and air environmental protection investment in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 259-270.
    4. Bing Feng & Kaiyang Sun & Min Chen & Tao Gao, 2020. "The Impact of Core Technological Capabilities of High-Tech Industry on Sustainable Competitive Advantage," Sustainability, MDPI, vol. 12(7), pages 1-15, April.
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