Simulation Modelling Practice and Theory最新文献

Providing a comprehensive view of the city operation and offering useful metrics for decision-making is a well-known challenge for urban risk-analysis systems. Existing systems are, in many cases, generalizations of previous domain specific tools/methodologies that may not cover all urban interdependencies and makes it difficult to have homogeneous indicators. In order to overcome this limitation while seeking for effective support to decision makers, this article introduces a novel hybrid simulation framework for risk mitigation. The framework is built on a proposed city concept that considers the urban space as a Complex Adaptive System composed by interconnected Critical Infrastructures. In this concept, a Social System, which models daily patterns and social interactions of the citizens in the Urban Landscape, drives the CIs demand to configure the full city picture. The framework's hybrid design integrates agent-based and network-based modeling by breaking down city agents into system-dependent subagents, to enable both inter and intra-system interaction simulation, respectively. A layered structure of indicators at different aggregation levels is also developed, to ensure that decisions are not only data-driven but also explainable. Therefore, the proposed simulation framework can serve as a DSS tool that allows the quantitative analysis of the impact of threats at different levels. First, system-level metrics can be used to get a broad view on the city resilience. Then, agent-level metrics back those figures and provide better explainability. On implementation, the proposed framework enables component reusability (for eased coding), simulation federation (enabling the integration of existing system-oriented simulators), discrete simulation in accelerated time (for rapid scenario simulation) and decision-oriented visualization (for informed outputs). The system built under the proposed approach facilitates to simulate various risk mitigation strategies for a scenario under analysis, allowing decision-makers to foresee potential outcomes. A case study has been deployed on a framework prototype to demonstrate how the DSS can be used in real-world situations, specifically combining cyber hazards over health and traffic infrastructures. The proposal aims at pushing the boundaries of urban city simulation towards more real, intelligent, and automated frameworks.

Simulation–optimization models are well-suited for real-time decision-support to the control room for search and interception of fugitives by Police on a road network, due to their ability to encode complex behavior while still optimizing the interception.

The typical simulation–optimization configuration is simulation model optimization, where the simulation model describes the system to be optimized, and the optimizer attempts to find the combination of decision variables that maximizes the interception probability. However, the repeated evaluation of the simulation model leads to high computation time, thus rendering it inadequate for time-constrained decision contexts. To support police interception operations in real-time, timely calculation of the solution is essential. Sequential simulation–optimization, where the simulation model, with its rich behavior, constructs (part of) the constraints of an optimization problem, could decrease the computation time.

We compare the computation time for two configurations of simulation–optimization (typical simulation model optimization and sequential simulation–optimization) for various problem instances of the fugitive interception problem. We show that sequential simulation–optimization reduces the computation time of large instances of the fugitive interception case study ten-fold. This result illustrates the potential of sequential simulation–optimization to mitigate the expensive optimization of simulation models.

Efficient warehouse management is of crucial significance for the smooth operation of a company's supply chain. With the challenging nature of warehouse environment changes, research on warehouse operational issues is increasingly important. Moreover, system simulation has emerged as a prevalent means of investigating warehouse operational management. However, prior research on warehouse simulation either utilized singular paradigms or lacked a generalized modeling framework. This remains a challenge for modelers exploring diverse domains of warehouse-related issues using simulation techniques. In this context, this study presents an integrated control methodology(ICM) based on the state changes of dispatchers and logistics equipments, the discrimination of task scenarios, and the behaviors of dispatchers. This methodology is incorporated into the warehouse workflow model. The workflow model serves as a conceptual abstraction of a warehouse's parallel scheduling system, while the integrated control methodology (ICM) simulates the entire decision-making process of dispatchers to resolve potential deadlock issues during task execution. Subsequently, we utilize a steel slab warehouse in a case study, employing a dynamic simulation using a hybrid paradigm based on Discrete Event Simulation (DES) and Agent-Based Simulation (ABS) to replicate the historical scheduling process within the warehouse. This demonstration confirms the feasibility of the proposed framework. Finally, we devise multiple dimensions of validation metrics to confirm the model's effectiveness.

In this work, we present a new approach based on a discrete event formalism to model and simulate micro-scale urban traffic systems. The formalism is a coupling between the P-DEVS (Parallel-Discrete Event System Specification) formalism and UML (Unified Modeling Language) state machines. A system is represented by a set of coupled components. Each component supports the dynamics and logic of a system element. The models presented include the streets, intersections and traffic signs, all of which can be synchronized together through specific mechanisms. These models can be applied to real-world OpenStreetMap networks. A discrete event-driven adaptation of the simplified Gipps car-following model is introduced, and subsequently compared to its discrete time counterpart. The results show that our discrete event model follows dynamics which are similar to those of a discrete time model with a low update time step of 0.1s, despite not taking certain non-linearities of the latter into account. In terms of vehicle state changes and computation time, our approach outperforms the discrete time one with an update time step of 1s, both on a simple case study and on a real network.

In this paper, the ELEKTRA framework is introduced, a novel community energy management system that organizes the prosumers into autonomous communities and determines the prosumers’ optimal energy procurement in a distributed manner. Our motivation stems from the increasing complexity of the energy procurement for prosumers and the need for more efficient and decentralized approaches to optimize their energy consumption. The first element of the ELEKTRA framework implements an autonomous hedonic game-theoretic model at the prosumers’ home energy management controllers, considering the prosumers’ individual energy consumption and generation patterns, as well as the utility-provided rewards for proactive participation. Specifically, the prosumer grouping is modeled as a hedonic game, demonstrating the existence of a Nash-stable and individually-stable prosumer grouping. The second element of the ELEKTRA framework employs a distributed non-cooperative game-theoretic approach. This addresses how prosumers strategically consume energy to meet their needs while maximizing their payoff by procuring additional energy from the utility company. Also, utilizing the theory of $n$-person concave games allows for a thorough evaluation of accuracy, performance, and complexity in determining optimal energy consumption for prosumers. A comprehensive evaluation of the ELEKTRA framework is conducted using real data from the southwest region of USA. The results demonstrate the operational effectiveness of the ELEKTRA framework, showcasing its superiority in optimizing prosumer payoff compared to existing models. The performance assessment, grounded in real-world data, provides valuable insights into the efficacy of the ELEKTRA framework in contrast to current state-of-the-art.

Complex Event Processing (CEP) is a successful method to transform simple IoT events created by sensors into meaningful complex business events. To enhance availability, an event fabrication mechanism is integrated within the CEP model, generating synthetic events to offset missing data, resulting in a quality-aware CEP model. In this model, generated complex events are characterized by quality properties, namely completeness and timeliness. To empirically assess the quality of complex events through experimentation, we have developed a hybrid simulation platform. The platform’s dual nature stems from its distinctive approach of simulating sensor behaviors while concurrently running the quality-aware CEP IoT platform. Users can conduct experiments that closely mimic actual operational scenarios and have, in real-time, full visibility and control over all involved aspects, including composite transformations, quality assessment, event fabrication and its effectiveness, and aggregated reports. A representative experiment in an IoT-enabled greenhouse with missing events is presented to demonstrate the usefulness of the platform. The contribution of the hybrid simulation platform is twofold: provide (a) quality assessment of complex events, using two established quality properties for IoT environments with specific computation formulas and (b) a comprehensive testbed covering all aspects of a typical IoT setup for realistic experimentation. Together, these elements provide significant cost–benefit advantages by enabling researchers and practitioners to pre-optimize operational efficiency and decision-making in IoT systems.

The computation offloading problem in mobile edge computing (MEC) has received a lot of attention, but service caching is also a research topic that cannot be ignored in MEC. Due to the limited resources available on the Edge Server (ES), a wise computation offloading and service caching policy must be formulated in order to maximize system offload efficiency. In this paper, a many-objective joint optimization computation offloading and service caching model (MaJOCOSC) is designed. The model takes into account the limited computing and storage resources of ES, the delay and energy consumption constraints of different types of tasks, and multiple processing modes of user tasks, and sets delay, energy consumption, task hit service rate, service cache balancing, and load balancing as the five optimization objectives of MaJOCOSC. Meanwhile, a non-dominated sorting genetic algorithm (NSGAIII-ASF&WD) based on achievement scalar function (ASF) and the k-nearest neighbor weighted distance mating selection strategy is proposed for better solving the model. The ASF ensures that the given strategy performs well for each objective value, and the k-nearest neighbor weighted distance provides the user with a diversity of strategies. Simulation results show that NSGAIII-ASF&WD can obtain better objective values when solving the model compared with other many-objective evolutionary algorithms, and a suitable computation offloading and service caching strategy is obtained.

Cloud computing has revolutionized the IT landscape, providing scalable, on-demand computing resource. For efficiency in cloud environments, it is essential for modern organizations, where objectives often include cost reduction, resource consumption, operational efficiency and load balancing etc, to implement multi objective solutions. Single-objective systems can fail in handling dynamic and diverse workloads. This study introduces the Multi-Objective Whale Optimization-Based Scheduler (WOA-Scheduler) for efficient task scheduling in cloud computing environments. Leveraging the Whale Optimization Algorithm (WOA), the scheduler optimizes multiple objectives simultaneously, including cost, time, and load balancing. A key feature of the WOA-Scheduler is its flexibility in accommodating user-defined weights for different objectives, allowing organizations to prioritize optimization goals based on their specific requirements. Comparative analysis across various cloud environments demonstrates the superiority of the WOA-Scheduler over traditional single-objective approaches. By achieving a better balance between cost, time, and resource utilization, the scheduler enhances overall performance. Moreover, its multi-objective optimization capabilities enable dynamic adjustment of task assignments in response to changing workload conditions, ensuring efficient resource utilization and workload distribution. Overall, the WOA-Scheduler offers a customizable and adaptable solution for addressing the complexities of modern cloud services, ultimately improving performance and efficiency.

In an increasingly digitalized world, cybersecurity has emerged as a critical component of safeguarding sensitive information and infrastructure from malicious threats. The threat actors are often in line or even one step ahead of the defense, causing the increasing reliance of security teams on artificial intelligence while trying to detect zero-day attacks. However, most of the cybersecurity solutions based on artificial intelligence that can be found in the literature are trained and tested on reference datasets that are at least five or more years old, which gives a vague insight into their security performances. Moreover, they often tend to be designed as isolated, self-focused components. The aim of this paper is to design and implement a modular network intrusion detection architecture capable of simulating cyberattacks based on real-world scenarios while evaluating its defense capabilities. The architecture is designed as a full pipeline from real-time network data collection and transformation to threat-information presentation and visualization, with a pre-trained artificial intelligence module at its core. Well-known components like CICFlowMeter, Prometheus, and Grafana are used and modified to fit our data preparation and core modules to form the proposed architecture for real-world network traffic security monitoring. For the sake of cyberattack simulation, the proposed architecture is situated within a virtual environment, surrounded by the Kali Linux-based penetration simulation agent on one side and a vulnerable agent on the other. The intrusion detection artificial intelligence module is trained on the CICIDS-2017 dataset, and it is demonstrated using the proposed architecture that, despite being trained on an outdated dataset, the trained module is still effective in detecting sophisticated modern attacks. Two case studies are given to illustrate how modular architectures and virtual environments can be valuable tools to assess the security properties of artificial intelligence-based solutions through simulation in real-world scenarios.

This paper presents a model formulation for balanced twin lip vane pumps and an experimental activity to validate the model. The simulation model begins with a geometrical module that preprocesses the CAD drawings of a given unit. The model then performs a fluid dynamic analysis using a lumped-parameter formulation to solve for the pressures inside properly defined control volumes within the unit. The fluid dynamic model is solved simultaneously with a motion module that evaluates the planar motions of the vanes using Newton’s law of motion and with a lubricating interface solver based on the Reynolds equation. Contact dynamics formulations and elastohydrodynamic relations are applied at the vane locations in contact with the cam ring. The comparison with experimental results highlights a good match in volumetric and hydromechanical efficiencies. The measured outlet pressure ripple matches the simulated one for all tested speeds and pressures. The paper also shows a breakdown of the distribution of volumetric and power losses arising from various components of the machine. The proposed methodology is computationally inexpensive, so it can be used in future design and optimization studies aimed at improving the performance of such units.