Simulation Modelling Practice and Theory最新文献

As global shipping undergoes rapid expansion, pivotal waterway transport systems—including significant nodes like the Panama Canal, the Suez Canal, and the Three Gorges-Gezhouba dams—are increasingly emerging as system-wide bottlenecks that limit transportation capabilities. Recognizing the pressing need for efficient traffic organization at these critical junctures, we designed a hybrid simulation model, which integrates Cellular Automaton and Multi-Agent methods, to analyse traffic efficiency and evaluate different ship organization schemes at these key waterway nodes. The Three Gorges-Gezhouba dams serve as a case study, where we crafted and executed four simulation scenarios that accommodate a range of variables such as different traffic organization schemes, traffic flow volumes, and anchorage capacities. Key operational indicators such as the maximum average waiting time of ships at the anchorage, and the period when the anchorage along the waterway reaches saturation, provide insights into the system's operational condition. The simulation outcomes highlight the proposed model's capability to accurately quantify the impact of implementing a linkage-control scheme and underscore the utility of dynamic adjustment of water area ranges under linkage-control for managing various traffic scenarios. Consequently, our research not only enriches high-precision simulation methodologies but also bolsters decision-making processes concerning ship traffic organization at Waterway Transport Key Nodes.

Simulation plays a crucial role in transportation studies. However, most simulation tools are individually developed to tackle specific transportation problems, making it challenging to incorporate multiple simulation tools into a unified setting and generate collaborative output. In this study, we develop a co-simulation system that integrates MATSim with an external fleet-based simulator to extend MATSim's functionalities. The overall structure enables the integration of MATSim simulation and multiple external simulations, which results in a cohesive simulation output. Though only one external simulator engages in the current development, the framework can be easily adapted to involve more fleet-based simulators that meet the system requirements. As a result, more complex transportation systems can be simulated using the framework without the need to develop these dedicated MATSim extensions, e.g. any new fleet algorithm from emergent R&D. The developed co-simulation system is named the Fleet Demand (FD) Simulator. We demonstrate the functionality of the FD Simulator by showcasing a simulation scenario involving MATSim and a ride-pooling simulator, which integrates novel ride-pooling services into the MATSim environment. First, we show the co-simulation system's capability to generate reliable results consistent with those produced by using the "DRT" extension-enabled MATSim. Less than 10 % discrepancies between the two results are observed. We then use the FD Simulator to evaluate ride-pooling services under various scenarios, where we assign different service parameters to two service fleets. Operations of the two fleets are simulated in two separate external simulation environments, showcasing the FD simulator's ability of engaging multiple simultaneous simulations. The affected service parameters are not adjustable in the "DRT" extension, showing the advantage of the co-simulation system. By running these scenarios using the FD Simulator, travel decisions made by agents in MATSim are observed when facing heterogeneous ride-pooling services. The results highlight the relevance of the co-simulation system in evaluating complex transportation systems.

Iron losses appear in Active Magnetic Bearings (AMBs) mostly because of the rotor's movement, but also because of the fluctuation of the control current in the stator's coils. They can be divided into three categories: the hysteresis losses, the eddy current losses and the excess losses and while they depend significantly on the rotating speed and the magnetic flux density applied on the poles, the most contributing factor is the magnetic material used for the core. In this paper, a 2-D Finite Element Method transient model is used to simulate the rotational motion of the shaft inside the AMB and calculate the iron losses that occur due to the alternating magnetic flux inside the rotor, as well as the mechanical load capacity on the vertical direction of the AMB for each case. A simulation is carried out, at first, for a constant control current value and a speed range of 0–30,000 rpm, followed by a second one, for constant rotational speed and control current values 0–0.5 A. Geometry remains the same for all simulations. When it comes to the materials selected for the stator and the rotor, the cases of Hiperco 27, Hiperco 50 and Hiperco 50 HS laminations are tested. The iron losses of the three alloys are compared to the losses of 3 % silicon-iron. The results show that the three iron cobalt alloys have significantly lower losses than the silicon iron for the same AMB size and rotor's speeds. Hiperco 50 has the lowest loss among the three Hiperco alloys, while Hiperco 50 HS provides slightly higher mechanical load capacity under the same operating conditions.

Nowadays, an accurate and reliable traffic forecast is meaningful in making the right decisions for traffic management systems in vehicular environments. Nevertheless, traffic flow prediction is a significant challenge in Vehicular Ad Hoc Networks (VANETs) that has taken much attention. Therefore, in this paper, we propose a hybrid traffic prediction model based on Prophet model and Long Short-Term Memory neural network (LSTM), called Hyper-Flophet, to predict next traffic flow. Hyper-Flophet model adopts the traditional neural prophet model but with major parameter tuning. First, we propose an efficient algorithm for predicting the traffic flow trend then, we develop an interactive LSTM (I-LSTM) model for auto-regression components. After that, we implement a new future regressor component called network mobility and finally, we enhance the event and holiday component by introducing exponential growth term. Through simulations with real VANET data, we show that the proposed hybrid approach can achieve superior forecasting performance over other models.

In industries, cranes are commonly used for transportation of a distributed-mass payload (DMP). However, analysis of the dynamic characteristics of such a system is limited since most of the research work considered a point-mass payload. This paper presents modelling and analysis of dynamic characteristics of a double-pendulum overhead crane carrying a DMP with and without payload hoisting. Nonlinear dynamic models representing the crane in both scenarios are derived. The effects of varying cable lengths, and carrying different payload masses and lengths on the dynamics of hook and payload oscillations are also studied. Simulations are performed to observe the dynamic characteristics of the crane under several conditions, and experiments are carried out to validate the theory and simulation results, and to assess the accuracy of the derived nonlinear dynamic equations. The results demonstrate that the payload oscillation is significantly affected by changes in the cable lengths and DMP parameters. In addition, satisfactory agreements between simulation and experimental results are achieved, which indicate validity of the nonlinear models and good compliance with the theory. It is envisaged that the dynamic characteristic analyses of the crane can be beneficial in designing efficient controllers, especially in suppressing DMP oscillation.

Clustering routing protocols currently have problems such as Single point of failure of cluster head nodes, poor network dynamics, uneven data transmission, etc., which are critical to the optimization of energy efficiency, network lifespan and network topology control. However, this optimization problem is an NP hard problem that conventional algorithms are difficult to solve. This paper proposes a new multi-objective cluster routing protocol (CHEABC-QCRP) aimed at optimizing network energy consumption, system lifespan, and quality of services (QoS). The protocol is based on a new chaotic hybrid elite artificial bee colony algorithm (CHEABC) proposed in this paper, which has strong search ability and greatly reduces convergence time. At the same time, a new chaotic strategy was designed to effectively prevent falling into local optima and premature convergence. In simulation experiments, compared with multiple routing protocols, a large number of test results show that this protocol significantly reduces network energy consumption, greatly improves system lifespan, and effectively improves QoS in IWSN.

The integration of Software-Defined Networking (SDN) into Network Virtualization (NV) significantly enhances network management, isolation, and troubleshooting capabilities. However, it brings forth the intricate challenge of allocating Substrate Network (SN) resources for various Virtual Network Requests (VNRs), a process known as Virtual Network Embedding (VNE). It encompasses solving two intractable sub-problems: embedding Virtual Machines (VMs) and embedding Virtual Links (VLs). While the research community has focused on formulating embedding strategies, there has been less emphasis on practical implementation at a laboratory scale, which is crucial for comprehensive design, development, testing, and validation policies for large-scale systems. However, conducting tests using commercial providers presents challenges due to the scale of the problem and associated costs. Moreover, current simulators lack accuracy in representing the complexities of communication patterns, resource allocation, and support for SDN-specific features. These limitations result in inefficient implementations and reduced adaptability, hindering seamless integration with commercial cloud providers. To address this gap, this work introduces EFraS (Emulated Framework for Dynamic VNE Strategies over SDN). The goal is to aid developers and researchers in iterating, testing, and evaluating VNE solutions seamlessly, leveraging a modular design and customized reconfigurability. EFraS offers various functionalities, including generating real-world SN topologies and VNRs. Additionally, it integrates with a diverse set of evaluation metrics to streamline the testing and validation process. EFraS leverages Mininet, Ryu controller, and OpenFlow switches to closely emulate real-time setups. Moreover, we integrate EFraS with various state-of-the-art VNE schemes, ensuring the effective validation of embedding algorithms.

Calibrating microscopic traffic simulation models is a prerequisite for simulation applications. This study proposes three novel methods to improve the accuracy and interpretability of the calibration model. The proposed approach involves selecting the calibration parameter, refining the model parameter system, and optimizing the calibration results. The first method expands the single-point mean into a multi-point distribution. The cumulative distribution curve of delay was selected as the calibration parameter. The second method divides the parameter system into global and local parameters. Global parameters were calibrated using NGSIM measured data, and local parameters were calibrated through intelligent algorithms. The third method proposes a methodology of parameter clustering recursion based on the genetic algorithm results, with information entropy selected as the analysis index. To evaluate the effectiveness of the proposed optimization methods, this study used NGSIM trajectory data as a case study. Eight simulation schemes based on the three optimization methods were designed, and simulation experiments were conducted using the VISSIM platform. The results show that the accuracy of the multi-point distribution calibration and parameter value optimization method is significantly higher than the default method. Additionally, the optimization method with calibration of both global and local parameters was more consistent with actual driving characteristics. This study provides a theoretical foundation for improving the practical application of traffic simulation technology, which has significant implications for transportation planning and management.

A Non-Fungible Token (NFT) is a digital asset that is proof of ownership and originality in the digital world. It is generally a unique data unit that can be created from a digital file. But it is not just any digital file; it must be audio, video, image, or photo. This fact is mainly limiting. However, there are many other digital files for which the connection with NFT and blockchain technology would make sense. Such digital files include, among other things, various simulation models. With the development of the use of simulation models for the needs of managing multiple types of logistics processes daily, the questions of how to prevent the unauthorised copying of any simulation model and protect the copyright of its authors are coming to the fore. NFT and blockchain represent a robust technology whose possibilities of use are gradually expanding, while simulation models could be one area of their application. The paper presents the result of research that will enable the implementation of NFT and blockchain technology in simulation models. The research outcome confirmed the possibility of creating an NFT through the decentralised public blockchain XRP Ledger (XRPL) and the marketplace xrp. cafe, which can be used to verify the ownership and originality of the simulation model.

The job shop scheduling problem, which involves the routing and sequencing of jobs in a job shop context, is a relevant subject in industrial engineering. Approaches based on Deep Reinforcement Learning (DRL) are very promising for dealing with the variability of real working conditions due to dynamic events such as the arrival of new jobs and machine failures. Discrete Event Simulation (DES) is essential for training and testing DRL approaches, which are based on the interaction of an intelligent agent and the production system. Nonetheless, there are numerous papers in the literature in which DRL techniques, developed to solve the Dynamic Flexible Job Shop Problem (DFJSP), have been implemented and evaluated in the absence of a simulation environment. In the paper, the limitations of these techniques are highlighted, and a numerical experiment that demonstrates their ineffectiveness is presented. Furthermore, in order to provide the scientific community with a simulation tool designed to be used in conjunction with DRL techniques, an agent-based discrete event simulator is also presented.