In manufacturing system management, the decisions are currently made on the base of 'what if' analysis. Here, the suitability of the model structure based on which a model of the activity will be built is crucial and it refers to multiple conditionality imposed in practice. Starting from this, finding the most suitable model structure is critical and represents a notable challenge. The paper deals with the building of suitable structures for a manufacturing system model by data-driven causal modelling. For this purpose, the manufacturing system is described by nominal jobs that it could involve and is identified by an original algorithm for processing the dataset of previous instances. The proposed causal modelling is applied in two case studies, whereby the first case study uses a dataset of artificial instances and the second case study uses a dataset of industrial instances. The causal modelling results prove its good potential for implementation in the industrial environment, with a very wide range of possible applications, while the obtained performance has been found to be good.
This paper concerns a new approach of the optimization problem, intending to turn into profit the last evolutions from IT domain. On the base of this approach, both new concept (the holistic optimization), and method ("zoom & pick") for its implementation it in manufacturing process optimization were developed. According to the new concept, the optimization problem gets a new structure, which includes not only the optimal solution finding, but also the optimal formalization of the problem as well as the tooling for assessing the position of potential solutions relative to the optimal one. The method for holistic optimization successively addresses the optimized object at different levels of its description. The main application domain is the manufacturing process from "Make to Order" environment, which is difficult to optimize because of dealing with a wide range of products.
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