<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In the present work, the maximal generalized network flow, often referred to as the profit-loss flow, is examined, considering the motivation in the decision-making systems built on this flow. The general description of the features of motivation as a psychological process actively involved in decision-making systems through the generalized network flow is given. A method is proposed to embed motivation in a generalized network flow through the motivation coefficients on different network sections (arcs). It is shown that the proposed method offers more possibilities than the classical network flow. It is proven that the initial resource in the source does not match the final resource in the consumer. This has been suggested to be due to the influence of motivation. The theoretical and experimental results convincingly prove the possibilities of considering motivation in decision-making systems when managing the transportation of resources in an arbitrary transport network.</p>
</abstract>

In the present work, the maximal generalized network flow, often referred to as the profit-loss flow, is examined, considering the motivation in the decision-making systems built on this flow. The general description of the features of motivation as a psychological process actively involved in decision-making systems through the generalized network flow is given. A method is proposed to embed motivation in a generalized network flow through the motivation coefficients on different network sections (arcs). It is shown that the proposed method offers more possibilities than the classical network flow. It is proven that the initial resource in the source does not match the final resource in the consumer. This has been suggested to be due to the influence of motivation. The theoretical and experimental results convincingly prove the possibilities of considering motivation in decision-making systems when managing the transportation of resources in an arbitrary transport network.

Maximal Generalized Network Flow Accounting for Motivation

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Maximal Generalized Network Flow Accounting for Motivation"}

In the present work, the maximal generalized network flow, often referred to as the profit-loss flow, is examined, considering the motivation in the...

Maximal Generalized Network Flow Accounting for Motivation

Online veröffentlicht: 27. Juni 2024

Seitenbereich: 3 - 15

Eingereicht: 12. Apr. 2024

Akzeptiert: 30. Mai 2024

DOI: https://doi.org/10.2478/cait-2024-0012

Schlüsselwörter
Intelligent systems, Generalized network flow, Decision-making systems, Motivation

© 2024 Vassil Sgurev et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Maximal Generalized Network Flow Accounting for Motivation

Online veröffentlicht: 27. Juni 2024

Seitenbereich: 3 - 15

Eingereicht: 12. Apr. 2024

Akzeptiert: 30. Mai 2024

DOI: https://doi.org/10.2478/cait-2024-0012

SchlüsselwörterIntelligent systems, Generalized network flow, Decision-making systems, Motivation

© 2024 Vassil Sgurev et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Intelligent systems, Generalized network flow, Decision-making systems, Motivation