Supplementary data for: Characteristics of decision support systems in production logistics - an analytical review

This dataset contains the data analyzed in the systematic literature review described in the following publication: "Decision support systems in production logistics: An analytical review". For each publication analyzed in the literature review, the title, the authors, the year of publication and a source are given.
The content of the corresponding publication can be briefly described as follows: Decision Support Systems (DSS) are a crucial component in production logistics, aiding companies in solving complex decision problems with multiple influences. This publication provides a structured review of the literature on the application of DSS in production logistics, focusing on methods for decision support, such as simulation and Artificial Intelligence (AI). The analysis considers scientific publications from 2015 to 2024, including industry use cases. Data analysis of categorizations of DSS is used. The findings highlight trends and limitations of current DSS application cases from the literature. Optimization methods, particularly heuristic and metaheuristic, are the most commonly employed decision support methods, followed by simulation. Despite the increased interest in AI technologies, their role in DSS for production logistics remains secondary. Like simulation methods, AI technologies are highly relevant when combined with optimization methods. The study provides a foundation for future research and practical advancements in decision support for manufacturing environments.

本数据集包含下述已发表学术文献《生产物流中的决策支持系统：一项分析性综述》中系统文献综述所分析的全部数据。针对该文献综述纳入分析的每一篇学术文献，均提供了其标题、作者、发表年份及来源信息。
对应学术文献的核心内容可简述如下：决策支持系统（Decision Support Systems, DSS）是生产物流领域的关键组成部分，可协助企业解决多影响因素下的复杂决策问题。该文献对生产物流场景中决策支持系统的应用相关研究开展了系统性综述，重点聚焦于仿真、人工智能（Artificial Intelligence, AI）等决策支持方法。本次分析覆盖了2015年至2024年间的学术文献，并纳入了工业应用案例。研究采用了针对决策支持系统分类的数据分析方法。研究结果揭示了当前文献中决策支持系统应用案例的发展趋势与现存局限：优化方法（尤其是启发式与元启发式方法）是最常用的决策支持手段，其次为仿真技术。尽管人工智能技术的关注度持续提升，但其在生产物流领域决策支持系统中的应用仍处于次要地位；与仿真方法类似，人工智能技术仅在与优化方法结合时才具备较高的应用价值。本研究为制造环境下决策支持领域的后续研究与实践升级提供了重要的理论基础。