Browsing by Subject "decision support"

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Access status: Restricted ,
Aplikacja wspomagająca pracę sędziów piłkarskich
(Data obrony: 2017-01-18) Mastyna, Krzysztof
Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej
Access status: Open Access ,
Artificial neural networks as a tool for supporting a moulding sand control system based on the dependency between selected moulding sand properties
(AGH University Press, 2023) Mrzygłód, Barbara; Jakubski, Jarosław; Opaliński, Andrzej; Regulski, Krzysztof
The article presents the potential for using artificial neural networks to support decisions related to the rebonding of green moulding sand. The basic properties of the moulding sand tested in foundries are discussed, especially compactibility as it gives the most information about the quality of green moulding sand. First, the data that can predict the compactibility value without the need for testing are defined. Next, a method for constructing an artificial neural network is presented and the network model which produced the best results is analysed. Additionally, two applications were designed to allow the investigation results to be searchable by determining the range of values of the moulding sand parameters.
Access status: Open Access ,
Decision support for allocating farmed fish to customer orders using a bi-objective optimization model
(Wydawnictwa AGH, 2022) Knudseth, Sunniva Haukvik; Molland, Even; Hoff, Arild; Hvattum, Lars Magnus; Oppen, Johan
Aquaculture is an important industry in certain coastal areas. Focusing on the farming of salmon and trout, an operational planning problem arises with the goal of allocating a supply of fish to the demand that is expressed through customer orders. This paper provides a conceptual model of such a planning problem and defines a corresponding bi-objective mathematical programming model. The problem is novel with respect to the structure of fish transport and the rules for satisfying customer orders with respect to fish size, quality, certification, and health status. Computational experiments have been conducted to gain further insight into the use of the provided model to provide support for planners who are involved in operational decision-making. The results indicated that the bi-objective optimization model can be useful in situations where a supply is insufficient to cover all of the demand within a given planning horizon.
Access status: Open Access ,
Reasoning algorithm for a creative decision support system integrating inference and machine learning
(Wydawnictwa AGH, 2017) Wilk-Kołodziejczyk, Dorota
In this paper a reasoning algorithm for a creative decision support system is proposed. It allows to integrate inference and machine learning algorithms. Execution of learning algorithm is automatic because it is formalized as aplying a complex inference rule, which generates intrinsically new knowledge using the facts stored already in the knowledge base as training data. This new knowledge may be used in the same inference chain to derive a decision. Such a solution makes the reasoning process more creative and allows to continue resoning in cases when the knowledge base does not have appropriate knowledge explicit encoded. In the paper appropriate knowledge representation and infeence model are proposed. Experimental verification is performed on a decision support system in a casting domain.