Automatic Resource Allocation in Business Processes: A Systematic Literature Survey
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2021
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910386929795072 |
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| author | Pufahl, Luise Ihde, Sven Stiehle, Fabian Weske, Mathias Weber, Ingo |
| author_facet | Pufahl, Luise Ihde, Sven Stiehle, Fabian Weske, Mathias Weber, Ingo |
| contents | For delivering products or services to their clients, organizations execute manifold business processes. During such execution, upcoming process tasks need to be allocated to internal resources. Resource allocation is a complex decision-making problem with high impact on the effectiveness and efficiency of processes. A wide range of approaches was developed to support research allocation automatically. This systematic literature survey provides an overview of approaches and categorizes them regarding their resource allocation goals and capabilities, their use of models and data, their algorithmic solutions, and their maturity. Rule-based approaches were identified as dominant, but heuristics and learning approaches also play a relevant role. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2107_07264 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Automatic Resource Allocation in Business Processes: A Systematic Literature Survey Pufahl, Luise Ihde, Sven Stiehle, Fabian Weske, Mathias Weber, Ingo Software Engineering For delivering products or services to their clients, organizations execute manifold business processes. During such execution, upcoming process tasks need to be allocated to internal resources. Resource allocation is a complex decision-making problem with high impact on the effectiveness and efficiency of processes. A wide range of approaches was developed to support research allocation automatically. This systematic literature survey provides an overview of approaches and categorizes them regarding their resource allocation goals and capabilities, their use of models and data, their algorithmic solutions, and their maturity. Rule-based approaches were identified as dominant, but heuristics and learning approaches also play a relevant role. |
| title | Automatic Resource Allocation in Business Processes: A Systematic Literature Survey |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2107.07264 |