Automatic Resource Allocation in Business Processes: A Systematic Literature Survey

• URL: http://arxiv.org/abs/2107.07264v2
• Date: Thu, 28 Mar 2024 13:53:44 GMT
• Title: Automatic Resource Allocation in Business Processes: A Systematic Literature Survey
• Authors: Luise Pufahl, Sven Ihde, Fabian Stiehle, Mathias Weske, Ingo Weber,
• Abstract summary: 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.
• Score: 0.0699049312989311
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Towards Cost Sensitive Decision Making [14.279123976398926]
  In this work, we consider RL models that may actively acquire features from the environment to improve the decision quality and certainty. We propose the Active-Acquisition POMDP and identify two types of the acquisition process for different application domains. In order to assist the agent in the actively-acquired partially-observed environment and alleviate the exploration-exploitation dilemma, we develop a model-based approach.
  arXiv  Detail & Related papers  (2024-10-04T19:48:23Z)
• Agent-Oriented Planning in Multi-Agent Systems [54.429028104022066]
  We propose a novel framework for agent-oriented planning in multi-agent systems, leveraging a fast task decomposition and allocation process. We integrate a feedback loop into the proposed framework to further enhance the effectiveness and robustness of such a problem-solving process.
  arXiv  Detail & Related papers  (2024-10-03T04:07:51Z)
• A Systematic Review of Business Process Improvement: Achievements and Potentials in Combining Concepts from Operations Research and Business Process Management [0.0]
  Business Process Management and Operations Research aim to enhance value creation in organizations. This systematic literature review identifies and analyzes work that uses combined concepts from both disciplines. Results indicate a strong focus on resource allocation and scheduling problems.
  arXiv  Detail & Related papers  (2024-09-02T14:13:14Z)
• Evaluating the Efficacy of Open-Source LLMs in Enterprise-Specific RAG Systems: A Comparative Study of Performance and Scalability [0.0]
  This paper presents an analysis of open-source large language models (LLMs) and their application in Retrieval-Augmented Generation (RAG) tasks. Our findings indicate that open-source LLMs, combined with effective embedding techniques, can significantly improve the accuracy and efficiency of RAG systems.
  arXiv  Detail & Related papers  (2024-06-17T11:22:25Z)
• Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey [48.06362354403557]
  This survey reviews the literature, mainly from 2019 to 2024, on efficient resource allocation and workload scheduling strategies for large-scale distributed DL. We highlight critical challenges for each topic and discuss key insights of existing technologies. This survey aims to encourage computer science, artificial intelligence, and communications researchers to understand recent advances.
  arXiv  Detail & Related papers  (2024-06-12T11:51:44Z)
• WESE: Weak Exploration to Strong Exploitation for LLM Agents [95.6720931773781]
  This paper proposes a novel approach, Weak Exploration to Strong Exploitation (WESE) to enhance LLM agents in solving open-world interactive tasks. WESE involves decoupling the exploration and exploitation process, employing a cost-effective weak agent to perform exploration tasks for global knowledge. A knowledge graph-based strategy is then introduced to store the acquired knowledge and extract task-relevant knowledge, enhancing the stronger agent in success rate and efficiency for the exploitation task.
  arXiv  Detail & Related papers  (2024-04-11T03:31:54Z)
• The Foundations of Computational Management: A Systematic Approach to Task Automation for the Integration of Artificial Intelligence into Existing Workflows [55.2480439325792]
  This article introduces Computational Management, a systematic approach to task automation. The article offers three easy step-by-step procedures to begin the process of implementing AI within a workflow.
  arXiv  Detail & Related papers  (2024-02-07T01:45:14Z)
• Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models [33.50873478562128]
  Large Language Models (LLMs) bring forth challenges in the high consumption of computational, memory, energy, and financial resources. This survey aims to systematically address these challenges by reviewing a broad spectrum of techniques designed to enhance the resource efficiency of LLMs.
  arXiv  Detail & Related papers  (2024-01-01T01:12:42Z)
• Automated Process Planning Based on a Semantic Capability Model and SMT [50.76251195257306]
  In research of manufacturing systems and autonomous robots, the term capability is used for a machine-interpretable specification of a system function. We present an approach that combines these two topics: starting from a semantic capability model, an AI planning problem is automatically generated.
  arXiv  Detail & Related papers  (2023-12-14T10:37:34Z)
• Learning policies for resource allocation in business processes [0.0]
  This paper proposes two learning-based methods for resource allocation in business processes. The first method leverages Deep Reinforcement Learning (DRL) to learn near-optimal policies by taking action in the business process. The second method is a score-based value approximation approach, which learns the weights of a set of curated features to prioritize resource assignments.
  arXiv  Detail & Related papers  (2023-04-19T21:05:38Z)
• Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming [77.38174112525168]
  We present Nemo, an end-to-end interactive Supervision system that improves overall productivity of WS learning pipeline by an average 20% (and up to 47% in one task) compared to the prevailing WS supervision approach.
  arXiv  Detail & Related papers  (2022-03-02T19:57:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.