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Modeling of the process of resource allocation between digital software technologies
Resource distribution amongst digital software technology systems is a tough operation that demands high effectiveness and a significant amount of time, especially because it is a dynamic activity with basic factors that vary over time. In this article, we highlight three different situations in the...
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Published in: | BIO web of conferences 2024-01, Vol.105, p.3002 |
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Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | Resource distribution amongst digital software technology systems is a tough operation that demands high effectiveness and a significant amount of time, especially because it is a dynamic activity with basic factors that vary over time. In this article, we highlight three different situations in the technology sector. Firstly, the role of using the dynamic programming approach to construct a model that helps find the maximum profit value for digital software technology subsystems by choosing the optimal sequence of control distribution of resources between these subsystems. Secondly, constructing the optimal order for digital software technology project execution to find minimal waiting time using the compromise solution algorithm. Thirdly, the role of the optimal assignment method in finding a compromise solution is that it determines the "fairest" strategy for assignment in the sense that it minimizes the loss to the owner, who has the maximum loss in profit. The assignment methodology finds the optimal software teams for digital software technology system projects, where each software team is qualified for a digital software technology system project, taking other participants’ choices into account, to achieve the maximum profit. The dynamic programming methods considered here can be useful for planning and allocating resources between the enterprises of the agro-industrial complex. |
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ISSN: | 2117-4458 2117-4458 |
DOI: | 10.1051/bioconf/202410503002 |