92,901 research outputs found
INTERDEPENDENCY BETWEEN SIMULATION MODEL DEVELOPMENT AND KNOWLEDGE MANAGEMENT
The paper discusses the relationship between simulation and knowledge management. Simulation models are increasingly being used to simulate processes in many different domains, including urban management, for solving decision problems. Usually, during the model development, the simulation team developing the model must rely on the knowledge provided by domain experts. In order to facilitate this simulation modelling process, appropriate collaborative knowledge management tools may be utilised. On the other hand, simulation models can facilitate knowledge management activities and processes as models could be used to evaluate decision alternatives before they are implemented or models could be used to simulate missing business data needed for discovering knowledge patterns. The paper concludes with a brief literature review of the some interdisciplinary applications that involve the combined use of both approaches. These applications include the use of system dynamics simulation for analysis of knowledge management strategies in organisational populations, learning curves, capital human modelling, and investigation of the influence of knowledge transfer and proximity (geographical, cognitive, and organizational) on the firm agglomeration process inside an industrial district.simulation modelling, knowledge management, system dynamics
Discovering Business Process Simulation Models in the Presence of Multitasking
Business process simulation is a versatile technique for analyzing business processes from a quantitative perspective. 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, several authors have proposed to discover simulation models from process execution logs so that the resulting simulation models more closely match reality. Existing techniques in this field assume that each resource in the process performs one task at a time. In reality, however, resources may engage in multitasking behavior. Traditional simulation approaches do not handle multitasking. Instead, they rely on a resource allocation approach wherein a task instance is only assigned to a resource when the resource is free. This inability to handle multitasking leads to an overestimation of execution times. This paper proposes an approach to discover multitasking in business process execution logs and to generate a simulation model that takes into account the discovered multitasking behavior. The key idea is to 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 processing times. The proposed approach is evaluated using a real-life dataset and synthetic datasets with different levels of multitasking. The results show that, in the presence of multitasking, the approach improves the accuracy of simulation models discovered from execution logs.European Research Council PIX 834141Junta de Andalucía P12--TIC--1867Ministerio de Ciencia, Innovación y Universidades OPHELIA RTI2018-101204-B-C2
Discovering Business Process Simulation Models in the Presence of Multitasking
Business process simulation is a versatile technique for analyzing business
processes from a quantitative perspective. 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, several authors have proposed to
discover simulation models from process execution logs so that the resulting
simulation models more closely match reality. Existing techniques in this field
assume that each resource in the process performs one task at a time. In
reality, however, resources may engage in multitasking behavior. Traditional
simulation approaches do not handle multitasking. Instead, they rely on a
resource allocation approach wherein a task instance is only assigned to a
resource when the resource is free. This inability to handle multitasking leads
to an overestimation of execution times. This paper proposes an approach to
discover multitasking in business process execution logs and to generate a
simulation model that takes into account the discovered multitasking behavior.
The key idea is to 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 processing times.
The proposed approach is evaluated using a real-life dataset and synthetic
datasets with different levels of multitasking. The results show that, in the
presence of multitasking, the approach improves the accuracy of simulation
models discovered from execution logs.Comment: Accepted at The 14th International Conference on Research Challenges
in Information Science (RCIS 2020). 17 pages, 4 figure
Stable Infrastructure-based Routing for Intelligent Transportation Systems
Intelligent Transportation Systems (ITSs) have been instrumental
in reshaping transportation towards safer roads, seamless
logistics, and digital business-oriented services under the umbrella of
smart city platforms. Undoubtedly, ITS applications will demand
stable routing protocols that not only focus on Inter-Vehicle Communications
but also on providing a fast, reliable and secure interface to
the infrastructure. In this paper, we propose a novel stable infrastructure-
based routing protocol for urban VANETs. It enables vehicles
proactively to maintain fresh routes towards Road-Side Units
(RSUs) while reactively discovering routes to nearby vehicles. It
builds routes from highly stable connected intersections using a selection
policy which uses a new intersection stability metric. Simulation
experiments performed with accurate mobility and propagation
models have confirmed the efficiency of the new protocol and its
adaptability to continuously changing network status in the urban
environment
Simulation-time reduction techniques for a retrofit planning tool
The design of retrofitted energy efficient buildings is
a promising option towards achieving a cost-effective
improvement of the overall building sector’s energy
performance. With the aim of discovering the best design
for a retrofitting project in an automatic manner,
a decision making (or optimization) process is usually
adopted, utilizing accurate building simulation
models towards evaluating the candidate retrofitting
scenarios. A major factor which affects the overall
computational time of such a process is the simulation
execution time. Since high complexity and prohibitive
simulation execution time are predominantly
due to the full-scale, detailed simulation, in this work,
the following simulation-time reduction methodologies
are evaluated with respect to accuracy and computational
effort in a test building: Hierarchical clustering;
Koopman modes; and Meta-models. The simplified
model that would be the outcome of these
approaches, can be utilized by any optimization approach
to discover the best retrofitting option
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