Applying Genetic Algorithms to the Optimization of Production Planning in a Real-World Manufacturing Environment
In this paper we present an optimization approach to a real-world production planning problem. Based on raw data that have been extracted from the Production, Planning and Control System of a company which produces special purpose vehicles and equipment, we have developed an architecture of an optimization system for production planning and scheduling in the manufacturing line of this company. The paper itself is divided into two major parts. The first part mainly deals with the theoretical background of production planning problems. In the second part of the paper we give an overview of the concrete scenario which is the subject of our research. Based on these fundamentals, we describe our approach to the problem, the modelling process and the architecture of the optimization system we plan to implement.
Many modern industrial production environments still rely on pure expert knowledge for production planning and scheduling purposes. Optimization in an algorithmic sense is rare and mostly not performed at all. This may seem amazing at first glance since automatization and electronic data processing have pervaded modern companies in almost any area of their daily business. For manufacturing lines in particular, there exist so called Production, Planning and Control Systems that serve for data storage and as a support tool for production related management activities. Such systems are designed in a highly generic and versatile way in order to be applied in many different companies no matter what products they actually manufacture. The systems try to distill the most common and essential aspects of production data. Customization is of course possible but it mostly requires deep insight and an immense effort.
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Tags: Genetic Algorithm, Optimization, Production Planning, Real-World Manufacturing Environment
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