A systematic review of machine learning for hybrid intelligence in production management

Carl René Sauer, Peter Burggräf, Fabian Steinberg
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引用次数: 0

Abstract

The increasing use of intelligent data processing and its capacity to handle vast data sets enhance efficiency and effectiveness in production management. Consequently, machine learning models have become essential for decision-making in this domain. Previous literature reviews have not considered the perspective of real business requirements from the domain environment, including a knowledge base of theoretical foundations and available methods within the domain. To provide a scientific overview of the current state of the art and to establish a starting point for developing new approaches, this paper presents the results of a systematic literature review. 217 publications were analyzed and synthesized. The publications are classified based on a developed framework that considers the decision type, the production management application, the underlying objective, type, technique, concrete algorithm of the ML model, and decision support for production management issues. A descriptive analysis reveals that there are approaches for all decision types, including unstructured decisions. Surprisingly, some of these approaches are not solely based on simulations to find an optimum. Remarkably, the number of publications related to the type of decision support does not decrease with increasing complexity. Although this paper provides practical guidance to practitioners in selecting applications and ML models to assist their decisions in their production environment, there is a significant need for further research to assist production managers. This can be achieved by developing hybrid models involving interaction between machine and human agents.
生产管理中混合智能机器学习的系统综述
越来越多地使用智能数据处理及其处理大量数据集的能力,提高了生产管理的效率和有效性。因此,机器学习模型对于该领域的决策至关重要。以前的文献综述没有考虑到来自领域环境的实际业务需求的角度,包括领域内的理论基础和可用方法的知识库。为了对目前的技术状况提供一个科学的概述,并为开发新的方法建立一个起点,本文提出了一个系统的文献综述的结果。分析和综合了217份出版物。这些出版物是根据一个开发的框架进行分类的,该框架考虑了决策类型、生产管理应用程序、潜在目标、类型、技术、ML模型的具体算法以及对生产管理问题的决策支持。描述性分析揭示了所有决策类型都有方法,包括非结构化决策。令人惊讶的是,其中一些方法并不是完全基于模拟来找到最优的。值得注意的是,与决策支持类型相关的出版物数量并没有随着复杂性的增加而减少。尽管本文为从业者在选择应用程序和ML模型以帮助他们在生产环境中做出决策提供了实用指导,但仍需要进一步的研究来帮助生产经理。这可以通过开发涉及机器和人类代理之间交互的混合模型来实现。
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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