优化可持续性:智能工厂中的信息聚合,以提高能源效率
Mohd Anjum1, Naoufel Kraiem2, Hong Min3
1Department of Computer Engineering, Aligarh Muslim University, Aligarh, 202002, India.
Scientific reports
|March 13, 2025
概括
本研究介绍了使用直观模糊集 (IFS) 的多标准决策 (MCDM) 框架,为工业4.0智能工厂选择最佳的节能解决方案,增强可持续的工业增长.
科学领域:
- 工业工程 工业工程 工业工程
- 运营研究 运营研究
- 可持续的制造业 可持续的制造业
背景情况:
- 工业4.0强调智能工厂的能源效率,以实现可持续增长.
- 对于节能解决方案的决策是复杂的,因为不确定性和模糊性.
- 现有的方法很难充分解决标准评估中的模糊性.
研究的目的:
- 提出一种新的多标准决策 (MCDM) 框架,用于在工业4.0智能工厂中选择节能解决方案.
- 开发新的基于直觉模糊集 (IFS) 的聚合运算符 (AO),以管理不确定性和模糊性.
- 提供一种可靠的方法来确定标准权重和排名替代品.
主要方法:
- 新型聚合运营商的发展:直观的模糊软max杜波伊斯 - 普拉德 (IFSDP),IFSIDPWA,IFSIDPOWA和IFSIDPWG.
- 使用基于标准消除效应 (MEREC) 的方法来对客观标准进行加权.
- 应用的排名选择使用功能映射的标准子间隔到一个单间隔 (RAFSI) 方法用于替代评估.
主要成果:
- 拟议的MCDM框架有效地处理决策过程中的不确定性和模糊性.
- 新的AOs和MEREC方法为评估节能解决方案提供了一个强大的结构.
- 该RAFSI方法成功地排列了替代方案,证明了该框架的实际适用性.
结论:
- 开发的框架提供了一种可靠的方法来确定智能工厂的最佳节能解决方案.
- 这项研究支持工业4.0范式内的可持续制造实践.
- 这些发现指导决策者走向更环保,更高效的工业运营.
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