A generic framework for real-time discrete event simulation (DES) modelling

Tavakoli,; Mousavi, Amir; Komashie,

doi:10.1109/wsc.2008.4736285

Cited by 23 publications

(19 citation statements)

References 12 publications

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“…The proposed scenario coupled with the signals and their location with respect to the model is depicted in the following Event-driven Process Chain (EPC) diagrams [figures [10][11][12][13]. Each diagram shows a process and its constituent operations.…”

Section: A Side-panel Manufacturing Process Discrete-event Modelmentioning

confidence: 99%

“…The acquired data is fed into a commercial discrete event simulation package (Arena™ Rockwell Automation). Previous integration work has addressed the need for direct translation of multiple input signals into inputs of the simulation model [13]. The so-called Real-time Model Matching Mechanism (R3M) [13] was implemented to clearly define the relationship between signals and processes in the model.…”

Section: A Side-panel Manufacturing Process Discrete-event Modelmentioning

confidence: 99%

“…Previous integration work has addressed the need for direct translation of multiple input signals into inputs of the simulation model [13]. The so-called Real-time Model Matching Mechanism (R3M) [13] was implemented to clearly define the relationship between signals and processes in the model. For example, the model is capable of relating signals from LS301 and LS305 that relate to the start and end of the forming process shown in figure 13.…”

Section: A Side-panel Manufacturing Process Discrete-event Modelmentioning

confidence: 99%

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Event Tracking for Real-Time Unaware Sensitivity Analysis (EventTracker)

Tavakoli

Mousavi

Broomhead

2013

IEEE Trans. Knowl. Data Eng.

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Abstract-This paper introduces a platform for online Sensitivity Analysis (SA) that is applicable in large scale real-time data acquisition (DAQ) systems. Here we use the term real-time in the context of a system that has to respond to externally generated input stimuli within a finite and specified period. Complex industrial systems such as manufacturing, healthcare, transport, and finance require high quality information on which to base timely responses to events occurring in their volatile environments. The motivation for the proposed EventTracker platform is the assumption that modern industrial systems are able to capture data in real-time and have the necessary technological flexibility to adjust to changing system requirements. The flexibility to adapt can only be assured if data is succinctly interpreted and translated into corrective actions in a timely manner. An important factor that facilitates data interpretation and information modelling is an appreciation of the affect system inputs have on each output at the time of occurrence. Many existing sensitivity analysis methods appear to hamper efficient and timely analysis due to a reliance on historical data, or sluggishness in providing a timely solution that would be of use in real-time applications. This inefficiency is further compounded by computational limitations and the complexity of some existing models.In dealing with real-time event driven systems, the underpinning logic of the proposed method is based on the assumption that in the vast majority of cases changes in input variables will trigger events. Every single or combination of events could subsequently result in a change to the system state. The proposed event tracking sensitivity analysis method describes variables and the system state as a collection of events. The higher the numeric occurrence of an input variable at the trigger level during an event monitoring interval, the greater is its impact on the final analysis of the system state.Experiments were designed to compare the proposed event tracking sensitivity analysis method with a comparable method (that of Entropy). An improvement of 10% in computational efficiency without loss in accuracy was observed. The comparison also showed that the time taken to perform the sensitivity analysis was 0.5% of that required when using the comparable Entropy based method. ATA acquisition systems that deal with large quantities of input variables and have higher sampling frequencies result in high bandwidth communication and place a heavy computational load on the higher tier data processing and information systems within their hierarchy. The focus of researchers and practitioners in this area has been to minimize this computational overhead by eliminating input variables that have the least impact on the system, this so called sensitivity analysis is discussed in [1]- [6]. Sensitivity analysis techniques help system analysts to focus on the most valuable information, information that most significantly impacts on system behaviour. Sensitivit...

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Section: A Side-panel Manufacturing Process Discrete-event Modelmentioning

confidence: 99%

Section: A Side-panel Manufacturing Process Discrete-event Modelmentioning

confidence: 99%

Section: A Side-panel Manufacturing Process Discrete-event Modelmentioning

confidence: 99%

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Event Tracking for Real-Time Unaware Sensitivity Analysis (EventTracker)

Tavakoli

Mousavi

Broomhead

2013

IEEE Trans. Knowl. Data Eng.

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“…In addition to these DDDAS applications, Hunter et al (2006) developed a simulation model based on inflow data aggregated over a short time interval to create an accurate estimate of the evolving state of transportation systems. In another example, a generic simulation platform for real time simulation modeling in healthcare and manufacturing applications was developed (Tavakoli et al 2008).…”

Section: Real Time Simulationmentioning

confidence: 99%

Automated knowledge discovery and data-driven simulation model generation of construction operations

Akhavian

Behzadan

2013

2013 Winter Simulations Conference (WSC)

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Computer simulation models help construction engineers evaluate different strategies when planning field operations. Construction jobsites are inherently dynamic and unstructured, and thus developing simulation models that properly represent resource operations and interactions requires meticulous input data modeling. Therefore, unlike existing simulation modeling techniques that mainly target long-term planning and close to steady-state scenarios, a realistic construction simulation model reliable enough for short-term planning and control must be built using factual data obtained from ongoing processes of the real system. This paper presents the latest findings of authors' work in designing an integrated data-driven simulation framework that employs a distributed network of sensors to collect multi-modal data from construction equipment activities. Collected data are fused to create metadata structures and data mining methods are then applied to extract key parameters and discover contextual knowledge necessary to create or refine data-driven simulation models that represent the latest conditions on the ground.

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“…Application examples include the rerouting of parts in a flexible manufacturing shop given changes in demand (Saygin, Cheng, and Singh 2001), dynamic production planning (Frantzén, Ng, and Moore 2011), or a real time route recommendation for drivers (Ben-Akiva et al 1998), among others. Real-rime applications in healthcare are practically non-existent (Tavakoli, Mousavi, and Komashie 2008).…”

Section: Introductionmentioning

confidence: 99%

Real-time simulation as a way to improve daily operations in an Emergency Room

Espinoza¹,

Pascual²,

Ramis³

et al. 2014

Proceedings of the Winter Simulation Conference 2014

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Emergency Rooms (ER) are resource constrained systems that demand efficient management in order to fulfill their care and service objectives. This article explores the use of real-time simulation to improve daily operations at an ER where unplanned events may occur. Such a simulation model must adequately portray the current state of the system; however ER workflow management systems often provide only limited information for this purpose. This paper studies the impact of the model's ability to predict ER performance with a limited amount of input information, such that what-if questions may be asked to guide decision making. Towards this end, two input data scenarios are compared to a case of perfect information. One scenario considers only patient arrival times and the other assumes additional knowledge of patient care pathways. Although both generate similar performance measures, the case with least information yields a slightly worse estimate of patient care pathway composition.

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A generic framework for real-time discrete event simulation (DES) modelling

Cited by 23 publications

References 12 publications

Event Tracking for Real-Time Unaware Sensitivity Analysis (EventTracker)

Event Tracking for Real-Time Unaware Sensitivity Analysis (EventTracker)

Automated knowledge discovery and data-driven simulation model generation of construction operations

Real-time simulation as a way to improve daily operations in an Emergency Room

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