{"title":"Discovery, simulation, and optimization of business processes with differentiated resources","authors":"Orlenys López-Pintado, Marlon Dumas, Jonas Berx","doi":"10.1016/j.is.2023.102289","DOIUrl":null,"url":null,"abstract":"<div><p>Business process simulation is a versatile technique to predict the impact of one or more changes on the performance of a process. Process simulation is often used to identify sets of changes that optimize one or more performance measures. Mainstream approaches to process simulation suffer from various limitations, some stemming from the fact that they treat resources as undifferentiated entities grouped into resource pools, and then assuming that all resources in a pool have the same performance and share the same availability calendars. Previous studies have acknowledged these assumptions, without quantifying their impact on simulation model accuracy. This article addresses this gap in the context of simulation models automatically discovered from event logs. Specifically, the contribution of the article is three-fold. First, the article proposes a simulation approach, wherein each resource is treated as an individual entity, with its own performance and availability calendar. Second, it proposes a method for discovering simulation models with differentiated performance and availability, starting from an event log of a business process. Third, it proposes a method to optimize the resource availability calendars in order to minimize resource cost while also minimizing cycle times. An empirical evaluation shows that simulation models with differentiated resources more closely replicate the distributions of cycle times and the work rhythm in a process than models with undifferentiated resources, and that iteratively optimizing resource allocations in conjunction with resource calendars leads to superior cost–time tradeoffs with respect to optimizing these allocations and calendars separately.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306437923001254","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Business process simulation is a versatile technique to predict the impact of one or more changes on the performance of a process. Process simulation is often used to identify sets of changes that optimize one or more performance measures. Mainstream approaches to process simulation suffer from various limitations, some stemming from the fact that they treat resources as undifferentiated entities grouped into resource pools, and then assuming that all resources in a pool have the same performance and share the same availability calendars. Previous studies have acknowledged these assumptions, without quantifying their impact on simulation model accuracy. This article addresses this gap in the context of simulation models automatically discovered from event logs. Specifically, the contribution of the article is three-fold. First, the article proposes a simulation approach, wherein each resource is treated as an individual entity, with its own performance and availability calendar. Second, it proposes a method for discovering simulation models with differentiated performance and availability, starting from an event log of a business process. Third, it proposes a method to optimize the resource availability calendars in order to minimize resource cost while also minimizing cycle times. An empirical evaluation shows that simulation models with differentiated resources more closely replicate the distributions of cycle times and the work rhythm in a process than models with undifferentiated resources, and that iteratively optimizing resource allocations in conjunction with resource calendars leads to superior cost–time tradeoffs with respect to optimizing these allocations and calendars separately.
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.