缺水条件下可持续农业食品生产和供应链规划的混合机器学习元启发式模型

IF 12.4 Q1 ENVIRONMENTAL SCIENCES Resources Environment and Sustainability Pub Date : 2023-08-04 DOI:10.1016/j.resenv.2023.100133
Mobina Mousapour Mamoudan , Ali Jafari , Zahra Mohammadnazari , Mohammad Mahdi Nasiri , Maziar Yazdani
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引用次数: 0

摘要

农业在所有社会中都非常重要,是生产粮食和确保人口生存的根本基础。农业生产过程和相关的后勤要素面临许多困难,气候变化造成的全球缺水进一步加剧了这些困难。然而,现有的文献并没有充分解决水资源短缺对农业食品供应链的影响。为了弥补这一研究空白,并有助于缓解气候变化引起的全球水危机,本研究提出了一个混合模型,该模型将基于元启发式算法的优化神经网络与可持续农业供应链的数学优化相结合。该模型将粒子群算法(PSO)用于特征选择,混合卷积神经网络(CNN)门控循环单元(GRU)与遗传算法(GA)优化结构相结合,用于预测用水量。利用该模型的结果,开发了一个多目标可持续农业供应链模型,以优化供应链的盈利能力,同时解决环境污染物、生产浪费、食物浪费、用水以及制造成本和时间问题。为了评估所提出方法的有效性,本文采用了伊朗的一个真实案例研究,为农业供应链优化设计提供了理论和实践见解,该优化设计包含可持续性因素,并有效应对日益严峻的水资源短缺挑战。这项研究的结果对可持续性重要性日益增长的国家的管理者和决策者具有启示意义。通过整合先进的优化技术和预测模型,本研究为提高农业供应链的可持续性和解决气候变化引起的紧迫水资源短缺问题提供了一个新的框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Hybrid machine learning-metaheuristic model for sustainable agri-food production and supply chain planning under water scarcity

Agriculture is of great importance in all societies, serving as the fundamental basis for producing food and ensuring the survival of human populations. The process of agricultural production and the associated logistical elements face numerous difficulties, which are further intensified by the worldwide water scarcity resulting from climate change. Nevertheless, the existing body of literature has not sufficiently addressed the consequences of water scarcity on agri-food supply chains. To bridge this research gap and contribute to mitigating the global water crisis induced by climate change, this study proposes a hybrid model that combines optimized neural networks based on meta-heuristic algorithms and mathematical optimization for a sustainable agricultural supply chain. The proposed model integrates particle swarm optimization (PSO) for feature selection and a hybrid convolutional neural network (CNN)-gated recurrent unit (GRU) with a genetic algorithm (GA) optimized structure to predict water consumption. Leveraging the model’s results, a multi-objective sustainable agriculture supply chain model is developed to optimize supply chain profitability while simultaneously addressing environmental pollutants, production waste, food waste, water usage, and manufacturing costs and time. To evaluate the effectiveness of the proposed approach, a real case study in Iran is employed, providing both theoretical and practical insights into the design of agriculture supply chain optimization that incorporates sustainability factors and effectively tackles the growing challenge of water scarcity. The findings of this study hold implications for managers and policymakers in countries where the importance of sustainability is growing. By integrating advanced optimization techniques and predictive models, this research offers a novel framework for enhancing the sustainability of agricultural supply chains and addressing the pressing issues of water scarcity induced by climate change.

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来源期刊
Resources Environment and Sustainability
Resources Environment and Sustainability Environmental Science-Environmental Science (miscellaneous)
CiteScore
15.10
自引率
0.00%
发文量
41
审稿时长
33 days
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