Robust optimization in production engineering – methods and application

Adrian Knapczyk,Sławomir Francik,Krzysztof Mudryk,Marek Wróbel,Marcin Jewiarz,A Szeląg-Sikora

doi:10.1051/e3sconf/201913201007

Adrian Knapczyk, Sławomir Francik + Show 4 more

Open Access

https://doi.org/10.1051/e3sconf/201913201007

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Abstract

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.

Highlights

Robust Optimization (RO) is a modern modeling methodology that is combined with various computational tools
241 documents were analysed in total
The authors come from China (60 documents), Iran (50 documents), China (47 dokuments), USA (50 documents) and France (18 documents)

Summary

Introduction

Robust Optimization (RO) is a modern modeling methodology that is combined with various computational tools. It is used for modeling and optimization of processes, with high data uncertainty [1]. One of the main problems when planning production is the high uncertainty regarding the terms of the schedule - not all production tasks are known at the beginning of the planning process. They may appear after the scheduling, which necessitates its correction. Modern industry successfully uses various methods of scheduling tasks in the production scheduling process [6]. The bibliometric analysis (quantitative and qualitative) was used for this purpose

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