Resource allocation in business process executions—A systematic literature study

IF 3 2区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Luise Pufahl , Fabian Stiehle , Sven Ihde , Mathias Weske , Ingo Weber
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引用次数: 0

Abstract

To achieve their goals, organizations execute business processes, which require effective allocation of resources to process activities. This results in the decision-making problem: Which resources should be allocated to which process activities? This problem significantly impacts both process efficiency and effectiveness. Over the past decades, various system-initiated (largely automated) resource allocation approaches have been developed. This study presents a comprehensive overview of this field by analyzing 61 primary studies identified through a rigorous, structured literature review covering publications from 1995 to 2023. We investigate resource allocation goals and cardinalities and describe how process models, execution data, and task attributes, as well as resource attributes, are used to specify the resource allocation problem. Additionally, the type of algorithmic solution and evaluation methods are discussed. This study shows that most approaches support 1-to-1 allocation cardinalities only, specify process-oriented goals, focus on process models, and utilize rule-based methods. Based on the results, we call for future research to define common terminology, support evidence-oriented resource allocation and adaptability, and improve reproducibility and comparability by performing benchmarking studies.
业务流程执行中的资源配置——系统的文献研究
为了实现它们的目标,组织执行业务流程,这需要有效地分配资源来处理活动。这就导致了决策问题:哪些资源应该分配给哪些流程活动?这个问题严重影响流程效率和有效性。在过去的几十年里,开发了各种系统发起的(主要是自动化的)资源分配方法。本研究通过对涵盖1995年至2023年出版物的严格结构化文献综述,分析了61项主要研究,对该领域进行了全面概述。我们将研究资源分配目标和基数,并描述如何使用流程模型、执行数据和任务属性以及资源属性来指定资源分配问题。此外,还讨论了算法解的类型和评价方法。这项研究表明,大多数方法只支持1对1的分配基数,指定面向流程的目标,关注流程模型,并利用基于规则的方法。基于这些结果,我们呼吁未来的研究定义通用术语,支持以证据为导向的资源分配和适应性,并通过进行基准研究来提高可重复性和可比性。
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来源期刊
Information Systems
Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
2.70%
发文量
112
审稿时长
53 days
期刊介绍: Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems. Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.
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