文章基本信息

标题：Robust optimization in production engineering – methods and application
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作者：Adrian Knapczyk ; Sławomir Francik ; Marcin Jewiarz 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2019
卷号：132
页码：1-9
DOI：10.1051/e3sconf/201913201007
出版社：EDP Sciences
摘要：Methods that use robust optimization are aimed at finding robustness to decision uncertainty. Uncertainty may affect the input parameters (problem) and the final solution. Robust optimization is applicable in many areas, such as: operational research, IT, energy, production engineering and others. The aim of the work was to indicate the main methods and examples of applications of robust optimization in the area of production engineering. Documents (articles and proceedings paper) indexed in the Web of Science - Core Collection database (WoSCC) from 2014-2018 were used for analysis. The search has been limited to the WoS-CC category: Engineering Industrial and Engineering Manufacturing. The main areas of application were: the scheduling of projects and tasks, production planning, and risk management. The most common methods were: linear programming, evolutionary algorithms, mixed integer programming, dynamic programming and many others.
其他摘要：Methods that use robust optimization are aimed at finding robustness to decision uncertainty. Uncertainty may affect the input parameters (problem) and the final solution. Robust optimization is applicable in many areas, such as: operational research, IT, energy, production engineering and others. The aim of the work was to indicate the main methods and examples of applications of robust optimization in the area of production engineering. Documents (articles and proceedings paper) indexed in the Web of Science - Core Collection database (WoSCC) from 2014-2018 were used for analysis. The search has been limited to the WoS-CC category: Engineering Industrial and Engineering Manufacturing. The main areas of application were: the scheduling of projects and tasks, production planning, and risk management. The most common methods were: linear programming, evolutionary algorithms, mixed integer programming, dynamic programming and many others.