https://doi.org/10.1016/j.compind.2021.103586

A framework for data-driven digitial twins of smart manufacturing systems

In many service delivery systems, the quantity of available resources is often a decisive factor of service quality. Resources can be personnel, offices, devices, supplies, and so on, depending on the nature of the services a system provides. Although service computing has been an active research topic for decades, general approaches that assess the impact of resource provisioning on service quality matrices in a rigorous way remain to be seen. Petri nets have been a popular formalism for modeling systems exhibiting behaviors of competition and concurrency for almost a half century. Stochastic timed Petri nets &#x0028 STPN &#x0029, an extension to regular Petri nets, are a powerful tool for system performance evaluation. However, we did not find any single existing STPN software tool that supports all timed transition firing policies and server types, not to mention resource provisioning and requirement analysis. This paper presents a generic and resource oriented STPN simulation engine that provides all critical features necessary for the analysis of service delivery system quality vs. resource provisioning. The power of the simulation system is illustrated by an application to emergency health care systems.

A simulation engine for stochastic timed petri nets and application to emergency healthcare systems

Recent advances in wireless technologies have led to an increased deployment of Wireless Sensor Networks (WSNs) for a plethora of diverse surveillance applications such as health, military, and environmental. However, sensor nodes in WSNs usually suffer from short device lifetime due to severe energy constraints and therefore, cannot guarantee to meet the Quality of Service (QoS) needs of various applications. This is proving to be a major hindrance to the widespread adoption of WSNs for such applications. Therefore, to extend the lifetime of WSNs, it is critical to optimize the energy usage in sensor nodes that are often deployed in remote and hostile terrains. To this effect, several energy management schemes have been proposed recently. Node scheduling is one such strategy that can prolong the lifetime of WSNs and also helps to balance the workload among the sensor nodes. In this article, we discuss on the energy management techniques of WSN with a particular emphasis on node scheduling and propose an energy management life-cycle model and an energy conservation pyramid to extend the network lifetime of WSNs. We have provided a detailed classification and evaluation of various node scheduling schemes in terms of their ability to fulfill essential QoS requirements, namely coverage, connectivity, fault tolerance, and security. We considered essential design issues such as network type, deployment pattern, sensing model in the classification process. Furthermore, we have discussed the operational characteristics of schemes with their related merits and demerits. We have compared the efficacy of a few well known graph-based scheduling schemes with suitable performance analysis graph. Finally, we study challenges in designing and implementing node scheduling schemes from a QoS perspective and outline open research problems.

/pdf/qos-aware-energy-management-and-node-scheduling-schemes-for-4v00vw9wd5.pdf

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

Formal Analysis of Emergency Department Staffing Based on Stochastic Timed Petri Net Models

AbstractPatients moving though a healthcare facility to receive medical service form patient flow. Optimizing patient flow is crucial to improving the efficiency of healthcare practice, providing a positive experience for patients and healthcare teams. Patient flow is event-driven, and thus Petri nets are a good fit for its modeling and analysis. In this chapter, the fundamental concepts of Petri nets, including transition firing rules, modeling power, structural and behavioral properties are briefly introduced. Petri net modeling practice in healthcare systems and patient flows, in particular, is discussed. Details of patient flow performance analysis based on stochastic timed Petri nets, such as the average patient waiting time, resource constraints modeling, task durations modeling, and patient flow hierarchical modeling that handles complexity are discussed.KeywordsPatient flowPetri netsModelingPerformance analysisResource constraints 

Healthcare Patient Flow Modeling and Analysis with Timed Petri Nets

Performance analysis can be carried out in several ways, especially in case of Markovian models. In order to interpret high level of abstraction, we often use modeling tools like timed Petri nets (TPNs). Although some subclasses of TPNs (e.g. stochastic Petri nets (SPNs) [17, 19]) can be handled analytically, a general timed Petri net is hard to evaluate via numerical analysis. However, the simulation of SPNs or deterministic and stochastic Petri nets (DSPNs) [16] are supported by many known tools (see, e.g. [3, 20]), it is hard to find a simulation tool for timed Petri nets with generally distributed (i.e., particular but arbitrarily chosen) firing times.

In this paper, we present TiPeNeSS (Timed Petri Net Simulator Software) which supports the simulation of timed Petri nets containing transitions with generally distributed firing delays. The input of the software (the Petri net and the parameters) is defined in an XML file, what allows us to generate results in batch mode. Besides, we describe a case study in which we optimize the frequency of the regular maintenance in a manufacturing process.

/pdf/tipeness-a-timed-petri-net-simulator-software-with-generally-2h8z58jo4j.pdf

TiPeNeSS: a timed petri net simulator software with generally distributed firing delays

TiPeNeSS: A Timed Petri Net Simulator Software with Generally Distributed Firing Delays

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TiPeNeSS: a timed petri net simulator software with generally distributed firing delays

TiPeNeSS: A Timed Petri Net Simulator Software with Generally Distributed Firing Delays