dc.creator | Estrada Torres, Bedilia | es |
dc.creator | Camargo, Manuel | es |
dc.creator | Dumas, Marlon | es |
dc.creator | García Bañuelos, Luciano | es |
dc.creator | Mahdy, Ibrahim | es |
dc.creator | Yerokhin, Maksym | es |
dc.date.accessioned | 2022-03-16T09:07:58Z | |
dc.date.available | 2022-03-16T09:07:58Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Estrada Torres, B., Camargo, M., Dumas, M., García Bañuelos, L., Mahdy, I. y Yerokhin, M. (2021). Discovering business process simulation models in the presence of multitasking and availability constraints. Data and Knowledge Engineering, 134 (July 2021, nº101897) | |
dc.identifier.issn | 0169-023X | es |
dc.identifier.uri | https://hdl.handle.net/11441/130857 | |
dc.description.abstract | Business process simulation is a versatile technique for quantitative analysis of business
processes. A well-known limitation of process simulation is that the accuracy of the simulation
results is limited by the faithfulness of the process model and simulation parameters given as
input to the simulator. To tackle this limitation, various authors have proposed to discover
simulation models from process execution logs, so that the resulting simulation models more
closely match reality. However, existing techniques in this field make certain assumptions
about resource behavior that do not typically hold in practice, including: (i) that each resource
performs one task at a time; and (ii) that resources are continuously available (24/7). In reality,
resources may engage in multitasking behavior and they work only during certain periods
of the day or the week. This article proposes an approach to discover process simulation
models from execution logs in the presence of multitasking and availability constraints. To
account for multitasking, we adjust the processing times of tasks in such a way that executing
the multitasked tasks sequentially with the adjusted times is equivalent to executing them
concurrently with the original times. Meanwhile, to account for availability constraints, we
use an algorithm for discovering calendar expressions from collections of time-points to infer
resource timetables from an execution log. We then adjust the parameters of this algorithm
to maximize the similarity between the simulated log and the original one. We evaluate the
approach using real-life and synthetic datasets. The results show that the approach improves
the accuracy of simulation models discovered from execution logs both in the presence of
multitasking and availability constraints | es |
dc.description.sponsorship | European Research Council PIX 834141 | es |
dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades OPHELIA RTI2018-101204-B-C22 | es |
dc.description.sponsorship | Junta de Andalucía EKIPMENTPLUS (P18–FR–2895) | es |
dc.format | application/pdf | es |
dc.format.extent | 20 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Data and Knowledge Engineering, 134 (July 2021, nº101897) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Multitasking | es |
dc.subject | Process mining | es |
dc.subject | Process simulation | es |
dc.subject | Resource availability | es |
dc.subject | Timetables | es |
dc.title | Discovering business process simulation models in the presence of multitasking and availability constraints | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | PIX 834141 | es |
dc.relation.projectID | OPHELIA RTI2018-101204-B-C22 | es |
dc.relation.projectID | EKIPMENT-PLUS (P18-FR-2895) | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0169023X21000240 | es |
dc.identifier.doi | 10.1016/j.datak.2021.101897 | es |
dc.journaltitle | Data and Knowledge Engineering | es |
dc.publication.volumen | 134 | es |
dc.publication.issue | July 2021, nº101897 | es |
dc.contributor.funder | European Research Council (ERC) | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (MICINN). España | es |
dc.contributor.funder | Junta de Andalucía | es |