Authors:
Tim Neumann
;
Daniel Kretz
;
Joerg Militzer
and
Tobias Teich
Affiliation:
University of Applied Sciences Zwickau, Germany
Keyword(s):
Proposal preparation, Scheduling, Genetic algorithm, Manufacturing, Process variant.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Industrial Engineering
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Production Planning, Scheduling and Control
;
Soft Computing
Abstract:
The success or failure of small and medium sized enterprises (SME) is related to the handling of factors like individual customer demands, price pressure and the probability to deliver at the required date and time. Often such SME’s are on the market for single-part or small-series production and want to be supplier for larger companies. Therefore, the decision makers of their customers have to investigate potential suppliers due to these mostly interrelated criteria. To increase these known factors during the proposal preparation is one possibility to enhance the market position of the SME. Thereby, a consideration of different variants of manufacturing a product and the premature investigation of resources and their capacities is necessary. Within the scope of this paper is introducing a conceptional framework for the evaluation of different process variants to manufacture a product. Thereby, we are using genetic algorithms to optimize and evaluate process variants including the ne
cessary resources and their capacitive use in an evaluated period. Additionally, we want to introduce our prototypical implementation.
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