Time-driven Simulation Research Articles

A time-driven simulation–optimization framework for the dynamic heterogeneous order-courier assignment problem for instant deliveries

In recent years, instant delivery services have become very popular for transporting meals to urban areas, with an extensive range of products now available to order. The platforms that offer these services rely on crowdsourced couriers who utilize their personal vehicles, resulting in heterogeneous fleets. Furthermore, the competition among companies to retain both customers and couriers is very intense, which underscores the importance of developing superior decision support systems. These systems must generate real-time assignments that meet the expectations of service providers, customers, and couriers. In this study, we designed a time-driven simulation–optimization framework that addresses the dynamic heterogeneous order-courier assignment problem and incorporates order-vehicle restrictions. The framework efficiently manages real-time order arrivals, courier movements, and positional updates while considering dynamic factors such as traffic congestion and regional speed limits for various vehicle types. Extensive testing using literature instances demonstrated the framework’s ability to satisfactorily address the defined problem. Additionally, the time-driven simulation–optimization framework was applied to a realistic case study, resulting in an approximately 4.5% reduction in the total delivery times (from the submission of the order until the delivery to the client) for all orders when compared to the original assignment.

Event-driven Simulation of Multi-scale Dropwise Condensation

Simulation of the multi-scale dropwise condensation on hydrophobic/superhydrophobic surfaces is of great significance for exploring condensation mechanism and guiding related engineering applications. However, the computational time cost of the conventional time-driven simulation is very expensive because the dropwise condensation covers multiple temporal and spatial scales and involves various behaviors between droplets. Here, an event-driven simulation method is proposed to efficiently simulate the multi-scale three-dimensional dropwise condensation with its simulation accuracy verified against the theoretical model and experimental results in literature. The dynamic octree data structure and its related algorithms are adopted to improve computational efficiency. Compared with the two-dimensional time-driven simulation, the computing efficiency of the three-dimensional event-driven simulation is improved by one to two orders of magnitude. The influence of droplet departure modes on dropwise condensation is then discussed and the results show that when droplets mainly depart through droplet jumping instead of droplet sliding, the surface coverage rate is smaller but the droplet population density and heat flux are larger. The increase of subcooling degree and nucleation density, or the decrease of critical sliding and jumping radius, can promote droplet departure and enhance condensation heat transfer, and the contribution of droplet jumping to heat flux is dominant. This work provides a new method that is efficient and reliable for multi-scale simulation of dropwise condensation and advances the understanding of dropwise condensation.

Modelling the impact of COVID-19 on elective waiting times

ABSTRACT In an early stage of the COVID-19 outbreak, hospitals in England were asked to postpone elective treatments in order to accommodate the expected demand for COVID-19 related admissions. This study aims to forecast the extent to which waiting times could increase as a result of these measures, and estimate the level of effort required to restore performance to pre COVID-19 levels. A time-driven simulation is configured and calibrated based upon conditions in England as of February 2020. As a worst case scenario, where restrictions on elective care extend to twelve months and elective treatment rates are halved, results suggest performance could drop to levels not seen since 2007 and the size of the waiting list could double. Restoring performance would take two years assuming additional capacity injections of 12.5%, costing an estimated £14.7b. The modelling presented here offers clinicians and managers an insight into the outcomes that could result under a range of scenarios considered plausible at the early stage of the outbreak. Freely available as open source code, the model may be locally-calibrated for regional healthcare systems and used more widely in countries where similar elective performance measures exist.

A general framework for dependability modelling coupling discrete-event and time-driven simulation

Analysis of complex failure scenarios and mitigation procedures of an industrial plant is one of the most important activity for the safety of the factory, the personnel and the surrounding areas. The dependability assessment of such systems is fulfilled by risk experts who, adopting well-known Reliability, Availability, Maintenance and Safety (RAMS) techniques, design and solve the stochastic failure model of the system. Traditional techniques like Fault Tree Analysis (FTA) or Reliability Block Diagrams (RBD) are of easy implementation but unrealistic, due to their simplified hypotheses that assume the components malfunction to be independent from each other and from the system working conditions.Dynamic Probabilistic Risk Assessment (DPRA) is the umbrella framework encompassing new mathematical and simulation formalisms aiming to relax the constraints of traditional techniques and increase the accuracy of dependability assessment. At the state of the art, DPRA cannot boast a well-defined methodology because the nature of a dynamic reliability problem can be so complex to require an ad-hoc modelling and resolution. Moreover, one of the main issues encountered by risk-practitioners is that there is a small support in terms of available tools or expert systems, specifically designed for DPRA problems.To tackle this lack, this paper presents the conception of general framework for the modelling and the simulation of a Stochastic Hybrid Fault Tree Automaton (SHyFTA), one of the most promising DPRA methodologies, able to combine Dynamic Fault Tree (DFT) with the deterministic model of the system process.The logic of the repairable DFT gates and the concepts for the implementation of a simulation engine combining Discrete Event Simulation (DES) and Time Driven Simulation (TDS) are illustrated and, a Matlab® toolbox library (SHyFTOO) has been coded and tested with a thorough validation campaign. Finally, a common case study in industrial engineering has been modelled and analysed under different stand-by configurations in order to demonstrate the modelling flexibility of the toolbox.

Efficient modelling of solute transport in heterogeneous media with discrete event simulation

To address the problem of different time scales present in the simulation of solute transport through systems with a complex permeability structure such as fractured porous rocks, we propose a parallel discrete event simulation (DES) algorithm based on local time stepping criteria, specifically developed for the hybrid finite-element node-centred finite volume (FV) framework. A preemptive-event-processing (PEP) approach is applied to synchronise discrete events with sufficiently close time stamps, thereby facilitating the parallelisation for shared memory architectures. The accuracy of the presented DES-PEP scheme is first verified against the analytical solution of a 1D advection equation with spatially variable coefficients. The DES scheme is then applied to simulate tracer advection through a 3D model of highly heterogeneous fractured rock represented by an unstructured adaptively refined mesh with over 1 million elements. DES produces results comparable to those of a conventional time-driven simulation (TDS), but uses less than 1% of the execution time. Analysis of event distributions shows that updates occur almost exclusively in a small number of FV cells marked by order-of-magnitudes faster fluid flow and advection-dominated transfer, while slow-flowing cells remain inactive and excluded from computations. This focusing of the computational effort leads to high simulation efficiency while simultaneously diminishing round-off errors. Scalability tests with a parallel version of DES on shared memory demonstrate further computational speedups mirroring the increased number of threads. With the use of 20 threads, execution time is reduced from 42.5 days (with TDS) to only 1.5 hours, equivalent to a speedup of over 670. This parallel DES algorithm therefore enables efficient multi-core simulation of solute transport in heterogeneous geologically realistic systems.

Mass imbalances in EPANET water-quality simulations.

EPANET is widely employed to simulate water quality in water distribution systems. However, in general, the time-driven simulation approach used to determine concentrations of water-quality constituents provides accurate results only for short water-quality time steps. Overly long time steps can yield errors in concentration estimates and can result in situations in which constituent mass is not conserved. The use of a time step that is sufficiently short to avoid these problems may not always be feasible. The absence of EPANET errors or warnings does not ensure conservation of mass. This paper provides examples illustrating mass imbalances and explains how such imbalances can occur because of fundamental limitations in the water-quality routing algorithm used in EPANET. In general, these limitations cannot be overcome by the use of improved water-quality modeling practices. This paper also presents a preliminary event-driven approach that conserves mass with a water-quality time step that is as long as the hydraulic time step. Results obtained using the current approach converge, or tend to converge, toward those obtained using the preliminary event-driven approach as the water-quality time step decreases. Improving the water-quality routing algorithm used in EPANET could eliminate mass imbalances and related errors in estimated concentrations. The results presented in this paper should be of value to those who perform water-quality simulations using EPANET or use the results of such simulations, including utility managers and engineers.

Simulation-Based Dynamic Passenger Flow Assignment Modelling for a Schedule-Based Transit Network

The online operation management and the offline policy evaluation in complex transit networks require an effective dynamic traffic assignment (DTA) method that can capture the temporal-spatial nature of traffic flows. The objective of this work is to propose a simulation-based dynamic passenger assignment framework and models for such applications in the context of schedule-based rail transit systems. In the simulation framework, travellers are regarded as individual agents who are able to obtain complete information on the current traffic conditions. A combined route selection model integrated with pretrip route selection and entrip route switch is established for achieving the dynamic network flow equilibrium status. The train agent is operated strictly with the timetable and its capacity limitation is considered. A continuous time-driven simulator based on the proposed framework and models is developed, whose performance is illustrated through a large-scale network of Beijing subway. The results indicate that more than 0.8 million individual passengers and thousands of trains can be simulated simultaneously at a speed ten times faster than real time. This study provides an efficient approach to analyze the dynamic demand-supply relationship for large schedule-based transit networks.

A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU Co-Processing: A Case Study.

Time-driven simulation methods in traditional CPU architectures perform well and precisely when simulating small-scale spiking neural networks. Nevertheless, they still have drawbacks when simulating large-scale systems. Conversely, event-driven simulation methods in CPUs and time-driven simulation methods in graphic processing units (GPUs) can outperform CPU time-driven methods under certain conditions. With this performance improvement in mind, we have developed an event-and-time-driven spiking neural network simulator suitable for a hybrid CPU-GPU platform. Our neural simulator is able to efficiently simulate bio-inspired spiking neural networks consisting of different neural models, which can be distributed heterogeneously in both small layers and large layers or subsystems. For the sake of efficiency, the low-activity parts of the neural network can be simulated in CPU using event-driven methods while the high-activity subsystems can be simulated in either CPU (a few neurons) or GPU (thousands or millions of neurons) using time-driven methods. In this brief, we have undertaken a comparative study of these different simulation methods. For benchmarking the different simulation methods and platforms, we have used a cerebellar-inspired neural-network model consisting of a very dense granular layer and a Purkinje layer with a smaller number of cells (according to biological ratios). Thus, this cerebellar-like network includes a dense diverging neural layer (increasing the dimensionality of its internal representation and sparse coding) and a converging neural layer (integration) similar to many other biologically inspired and also artificial neural networks.

Improving Communication Patterns for Distributed Cluster-based Individual-oriented Fish School Simulations

Parallel discrete event simulation (PDES) have shown to be an useful paradigm for simulating complex and large-scale models. An individual-oriented approach allows modelers capture complex emerging global behaviors generated by simple local interaction, like observed in self-organized systems. Usually, this type of simulations are highly expensive in terms of computing and communications. One one hand, we can reduce the computing involved in individual interactions by means of developing a robust partitioning method. On the other hand, we have to be able to efficiently handle a huge number of individuals interacting with other individuals stored in memory of remote processors. In this work we will analyze and compare three communication strategies: synchronous and asynchronous message passing (via MPI) and bulk-synchronous parallel (BSP) for our distributed cluster-based individual-oriented fish school simulator. In this type of simulations, the main contributions of our work are: a) we showed that distributed time-driven simulations do not always improve the performance when using synchronous communication strategies, b) we show asynchronous communications strategies are more efficient. In addition, we have verified that the bulk-synchronous parallel method is a scalable.

A simulation model for estimating train and passenger delays in large-scale rail transit networks

A simulation model was proposed to investigate the relationship between train delays and passenger delays and to predict the dynamic passenger distribution in a large-scale rail transit network. It was assumed that the time varying original-destination demand and passenger path choice probability were given. Passengers were assumed not to change their destinations and travel paths after delay occurs. Capacity constraints of train and queue rules of alighting and boarding were taken into account. By using the time-driven simulation, the states of passengers, trains and other facilities in the network were updated every time step. The proposed methodology was also tested in a real network, for demonstration. The results reveal that short train delay does not necessarily result in passenger delays, while, on the contrary, some passengers may get benefits from the short delay. However, large initial train delay may result in not only knock-on train and passenger delays along the same line, but also the passenger delays across the entire rail transit network.

Fail-safe detection of threshold crossings of linear integrate-and-fire neuron models in time-driven simulations

The characteristics of time-driven simulation are a fixed-size simulation step and a fixed-size communication interval [1]. The former defines update-and-check points, which are the discrete points in time when all neurons update their state variables and check for a super-threshold membrane potential. The latter defines the discrete points in time when all neurons communicate their spikes. The communication interval is a multiple of the simulation step size and limited only by the minimum synaptic transmission delay in the network. The time-driven environment of the simulator NEST [2] provides an 'on-grid' and an 'off-grid' framework that handle spikes differently. In the on-grid framework, spikes are incorporated, detected and emitted only at the pre-defined update-and-check points. In the off-grid framework, spikes can be incorporated and emitted at any point in time [3]. For each neuron the arrival times of incoming spikes introduce additional update-and-check points. Hence, the simulation step can be increased up to the size of the communication interval. The detection of a threshold crossing can only take place at a check point, but the timing of the referring spike is estimated with precision limited only by the limits of double representation. In general, a time-driven simulator that supports the off-grid framework performs neural network simulations with the same precision and faster than an event-driven simulator [4]. However, time-driven simulation still bears the risk of missing a threshold crossing as a very brief excursion of the membrane potential above threshold may not be detected at the next check point. In the off-grid framework, this problem is more pronounced in networks with low connectivity and strong coupling as well as in the case of low firing rates. The on-grid framework is even more affected due to fewer check points and the synchronized arrival of spikes. Here, we present algorithms which are guaranteed to detect all threshold crossings by supplementing the standard test for a super-threshold membrane potential at each check point and that exploit the information about the neuronal state at nearby check points. These additional tests need to be invoked whenever the membrane potential is sub-threshold, which means at virtually all check points. We develop sub-tests of increasing complexity and specificity, starting with simple sifting methods and ending up with a complex expression that faithfully indicates the existence of a threshold crossing between the last and the current check point. An analysis of the test specificities and computational costs results in a cascade of tests which locates all threshold crossings at a low computational cost.

스마트그리드를 위한 초고속 전력선통신기술 연구

In this paper, we study on the reliability of Very High-rate Power Line Communication (VH-PLC) for Smart Grid, so that the resultant data rate is over 400Mbps at a physical layer. Firstly, reviewing the research trend of the PLC, we discuss the required techniques for supporting the Smart Grid. Considering a pre-specification with the value of several parameters, we investigate a multi-carrier modulation technique to overcome limitations of higher rate transmission under power line channel environments. Then, we propose a system specification of the VH-PLC in the sense of enhancing two features. One is resolving the problem of the co-existence of the deployed high-speed PLC according to the published standardization of KS X 4600-1 in Korea. The other is getting better performance on the grid adopting the diverse element techniques, such as multi-carrier modulation, a subcarrier utilization mode, a variable rate LDPC (Low Density Parity Check) code, and a time and frequency diversity technique. Further, a simulation tool, composed of an Event-Driven simulator and a Time-Driven simulator, is developed for the purpose of verifying the system performance and continuously cross-checking the test bench signal of the proposed VH-PLC system.

Efficient spike tests for linear integrate-and-fire neuron models in time-driven simulations

Event Abstract Back to Event Efficient spike tests for linear integrate-and-fire neuron models in time-driven simulations Susanne Kunkel1, 2*, Moritz Helias3, Markus Diesmann3, 4, 5, 6 and Abigail Morrison1, 2 1 Albert-Ludwig University of Freiburg, Faculty of Biology, Germany 2 Albert-Ludwig University of Freiburg, Bernstein Center Freiburg, Germany 3 Research Center Jülich, Computational and Systems Neuroscience, Germany 4 RIKEN Computational Science Research Program, Japan 5 RIKEN Brain Science Institute, Japan 6 RWTH Aachen University, Medical Faculty, Germany The characteristics of time-driven simulation are a fixed-size simulation step and a fixed-size communication interval [1]. The former defines update-and-check points, which are the discrete points in time when all neurons update their state variables and check for a super-threshold membrane potential. The latter defines the discrete points in time when all neurons communicate their spikes. The communication interval is a multiple of the simulation step size and limited only by the minimum synaptic transmission delay in the network. Traditionally, spikes are incorporated, detected and emitted only at the pre-defined update-and-check points. However, the time-driven environment of the simulator NEST [2] provides an 'off-grid' framework that enables spikes to be be incorporated and emitted at any point in time [3,4]. For each neuron the arrival times of incoming spikes introduce additional update-and-check points. As the detection of a threshold crossing can only take place at the next check point, time-driven simulation still bears the risk of missing a threshold crossing: a very brief excursion of the membrane potential above threshold may not be detected. This problem is more pronounced in networks with low connectivity and strong coupling as well as in the case of low firing rates. Here, we investigate spike tests of increasing complexity and specificity that can supplement the standard test for a super-threshold membrane potential at each check point and that guarantee the detection of all threshold crossings. Firstly, we determine the specificities of simple sifting methods for a range of input scenarios. Secondly, we compare the performances of complex spike tests which faithfully indicate the existence of a threshold crossing between the last and the current check point. This stepwise analysis enables us to identify a cascade of tests which locates all threshold crossings at a low computational cost. Acknowledgements Partially funded by BMBF Grant 01GQ0420 to BCCN Freiburg, EU Grant 15879 (FACETS), EU Grant 269921 (BrainScaleS), the Helmholtz Alliance on Systems Biology (Germany), the Next-Generation Supercomputer Project of MEXT (Japan), Neurex, and the Junior Professor Program of Baden-Württemberg.

A Study of the Dynamic Evolution of Asphaltene Aggregate Size Distribution Using Monte Carlo Simulation

Monte Carlo (MC) simulation is used to determine the dynamic evolution of the asphaltene aggregates size distribution (ASD) in shear-induced organic solvents. This MC method is a time-driven simulation with periodic regulation of the number of particles. Asphaltene molecules are referred to as fractal-like aggregates that undergo simultaneous aggregation and breakage processes in shear-induced organic solvents. The effects of shear rate, initial particle size, and initial asphaltene concentration on the evolution of number average diameter are studied. The simulations are validated through comparison with the experimental data of the asphaltene ASD and numerical results of deterministic solution of population balance. These comparisons revealed that the MC simulation is capable of predicting the dynamic evolution of the ASD with reasonable accuracy.

A General and Efficient Method for Incorporating Precise Spike Times in Globally Time-Driven Simulations

Traditionally, event-driven simulations have been limited to the very restricted class of neuronal models for which the timing of future spikes can be expressed in closed form. Recently, the class of models that is amenable to event-driven simulation has been extended by the development of techniques to accurately calculate firing times for some integrate-and-fire neuron models that do not enable the prediction of future spikes in closed form. The motivation of this development is the general perception that time-driven simulations are imprecise. Here, we demonstrate that a globally time-driven scheme can calculate firing times that cannot be discriminated from those calculated by an event-driven implementation of the same model; moreover, the time-driven scheme incurs lower computational costs. The key insight is that time-driven methods are based on identifying a threshold crossing in the recent past, which can be implemented by a much simpler algorithm than the techniques for predicting future threshold crossings that are necessary for event-driven approaches. As run time is dominated by the cost of the operations performed at each incoming spike, which includes spike prediction in the case of event-driven simulation and retrospective detection in the case of time-driven simulation, the simple time-driven algorithm outperforms the event-driven approaches. Additionally, our method is generally applicable to all commonly used integrate-and-fire neuronal models; we show that a non-linear model employing a standard adaptive solver can reproduce a reference spike train with a high degree of precision.

High-fidelity and time-driven simulation of large wireless networks with parallel processing

Parallel processing is a promising technique for reducing the execution time of a large and complex wireless network simulation. In this article, a parallel processing technique is presented for time-driven simulations of large and complex wireless networks. The technique explicitly considers the physical-layer details of wireless network simulators such as shadowing and co-channel interference. The technique uses multiple processors interconnected with highspeed links. Principles and issues are elaborated, including workload partitioning, synchronization, and database design. Numerical results from two different wireless network simulators employing the technique are presented to demonstrate the performance of the technique. The results indicate that the technique can significantly improve the run-time performance of time-driven simulations of large and complicated wireless networks of different types.

Time-driven simulation with fully asynchronous pulse coupling

Event Abstract Back to Event Time-driven simulation with fully asynchronous pulse coupling Alexander Hanuschkin1*, Suzanne Kunkel1, Moritz Helias1, Abigail Morrison2 and Markus Diesmann1, 2 1 BCCN Freiburg, Germany 2 RIKEN Brain Science Institute, Japan Artificial synchrony can be introduced by discrete time simulation of neuronal networks, since they typically constrain spike times to a grid determined by the computational step size [1]. Event-driven algorithms avoid this problem but are computationally demanding, both with respect to calculating future spike times and to event management, particularly for large network sizes. To address this problem, Morrison et al. [2] developed a general method of handling off-grid spiking in combination with exact subthreshold integration in globally time-driven simulations [3,4] using interpolation to approximate threshold crossings. In the framework of event-driven simulations, Brette [5] presented an elegant method of calculating spike times of integrate-and-fire neurons with exponentially decaying currents. The prediction of the next threshold crossing of the membrane potential is reformulated into a root finding problem of the equivalent polynomial. Here, we show that this scheme can also be implemented in the time-driven environment of NEST [4]. Additionally, we extended the model of Morrison et al. [2] by replacing the interpolation of threshold crossings with the computationally more expensive, but numerically more exact, Newton-Raphson technique. We compare the accuracy of the three approaches in single-neuron simulations and the efficiency in a balanced random network of 12,000 neurons [6]. For small input and output rates the polynomial method is more efficient than the interpolated one whereas for high input or output rates the interpolated method is more efficient than the polynomial one. The direct numerical method based on Newton-Raphson root finding outperforms both the interpolation and the polynomial approach at all input/output rates. Partially funded by DIP F1.2, BMBF Grant 01GQ0420 to the BCCN Freiburg, and EU Grant 15879 (FACETS).

The performance of solvers for integrate-and-fire models with exact spike timing

Event Abstract Back to Event The performance of solvers for integrate-and-fire models with exact spike timing Markus Diesmann1, 2*, Alexander Hanuschkin2, Suzanne Kunkel2, Moritz Helias2 and Abigail Morrison1 1 RIKEN Brain Science Institute, Japan 2 BCCN Freiburg, Germany Discrete time neuronal network simulation strategies typically constrain spike times to a grid determined by the computational step size. This approach can have the effect of introducing artificial synchrony [1]. However, continuous time (i.e. event-driven) approaches can be computationally demanding, both with respect to calculating future spike times and to event management, particularly for large network sizes. To address this problem, Morrison et al. [2] presented a general method of handling off-grid spiking in combination with exact subthreshold integration in globally time-driven simulations [3,4]. Within each time step the neuron model emulates an event-driven environment to process incoming spikes, whereas the timing of outgoing spikes is based on interpolation. Therefore, the computation step size is a decisive factor for both integration error and simulation time. An alternative approach for calculating the exact spike times of integrate-and-fire neurons with exponential currents was recently published by Brette [5]. The problem of accurate detection of the first threshold crossing of the membrane potential is converted into finding the largest root of a polynomial. Common numerical means like Descartes' rule and Sturm's theorem are applicable. Although this approach was developed in the context of event-driven simulations, we take advantage of its ability to predict future threshold crossings in the time-driven environment of NEST [3]. We compare the accuracy of the two approaches in single-neuron simulations and the efficiency in a balanced random network of 10,000 neurons [6]. We show that the network simulation time when using the polynomial method depends only weakly on the computational step size, and the single neuron integration error is independent of it. Although the polynomial method attains the maximum precision expected from double numerics for all input rates and computation step sizes, the interpolation method is more efficient for input rates above a critical value. These results suggest that the cost of processing incoming spikes, rather than the calculation of outgoing spikes, is the dominant factor. Therefore, we extend the model of Morrison et al. [2] by replacing the interpolation of threshold crossings with the computationally more expensive, but numerically more exact, Newton-Raphson technique. The resulting implementation achieves the maximum precision at all computation step sizes and is more efficient for all input and output rates than either the interpolation or the polynomial implementation. Acknowledgements: Partially funded by DIP F1.2, BMBF Grant 01GQ0420 to the Bernstein Center for Computational Neuroscience Freiburg, and EU Grant 15879 (FACETS).

가감속을 고려한 교착없는 AGV 주행경로설정

In the environment where multiple AGVs(Automated Guided Vehicles) operate concurrently in limited space, collisions, deadlocks, and livelocks which have negative effect on the productivity of AGVs occure more frequently. The accelerated motion of an AGV is also one of the factors that make the AGV routing more difficult because the accelerated motion makes it difficult to estimate the vehicle`s exact travel time. In this study, we propose methods of avoiding collisions, deadlocks, and livelocks using OAR(Occupancy Area Reservation) table, and selecting best route by estimating the travel time of an AGV in accelerated motion. A set of time-driven simulation works validated the effectiveness of the proposed methods.

A distributed time-driven simulation method for enabling real-time manufacturing shop floor control

This paper proposes a distributed simulation approach for scheduling discrete-events in manufacturing shop floors. The proposed approach employs a time-driven method to simulate occurrence of discrete-events using distributed entities that replicate physical entities in the manufacturing shop floor. In specific, the proposed approach iteratively controls the timing of discrete-events occurrence using a control theoretic model. In this approach, changing the speed of the simulation clock, termed time-scaling factor, can accelerate or decelerate the simulation speed resulting in simpler synchronizations of discrete-events and faster simulation than standard distributed discrete-event simulations according to the capability of the communication networks. Computational experiments are conducted to test the performance of the proposed system with different values of the time-scaling factor, and the relationship between the system performance and the time-scaling factor is investigated through analysis of the system model. Results obtained from the computational experiments show significant successes in speeding up discrete-event simulations in such a way that the proposed approach can be used for the control of manufacturing shop floors, providing real-time decision supports.