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

Sustainability, 2022-09, Vol.14 (18), p.11571 [Peer Reviewed Journal]

COPYRIGHT 2022 MDPI AG ;2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2071-1050 ;EISSN: 2071-1050 ;DOI: 10.3390/su141811571

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  • Title:
    Environmental Cost Control of Manufacturing Enterprises via Machine Learning under Data Warehouse
  • Author: Li, Xiaohan ; Ma, Chenwei ; Lv, Yang
  • Subjects: Algorithms ; Artificial intelligence ; Carbon ; Control methods ; Control theory ; Cost control ; Costs ; Data warehouses ; Decision making ; Depletion ; Energy consumption ; Learning algorithms ; Logistics ; Machine learning ; Manufacturing ; Manufacturing industry ; Mathematical optimization ; Neural networks ; Optimization ; Performance tests ; Pollution control ; Product life cycle ; Resource depletion ; Steel industry ; Supply chains
  • Is Part Of: Sustainability, 2022-09, Vol.14 (18), p.11571
  • Description: 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.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2071-1050
    EISSN: 2071-1050
    DOI: 10.3390/su141811571
  • Source: GFMER Free Medical Journals
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
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