Simulation Modelling Practice and Theory最新文献

High-density built-up areas in cities often enlist the underground realm to provide solution space for transport, shopping and other purposes. The special location, layout, and accessibility of underground structures often generate unique and acute safety-risk concerns. They are inadequately understood and managed and cannot be tackled appropriately by conventional risk assessment and abatement methods. This study focused on evacuating underground commercial streets (UCS) with a heavy concentration of people in Fuzhou city in China. Despite the widespread use of building information modeling (BIM) in construction, it has rarely been applied to studies of underground shopping streets. This study adopted BIM technology as the core method, in conjunction with PyroSim fire and Pathfinder evacuation simulation software. Different fire scenarios in four fire protection zones and the most unfavorable fire sources were set in the model. Based on a calculated number of persons at the start of a fire, different movement paths, stair configuration and exit width were simulated. The choice of escape routes, congestion locations, and slack time windows were identified by the graphical images of the simulation programs. Required safe egress time was compared with available safe egress time, and the number of successful evacuees was reckoned. The effects of three escape-stair forms on evacuee utilization and evacuation rates were evaluated. Their evacuation efficiency was ranked: crossed stair > straight stair > parallel-double stair. The simulation results can optimize building layout design and improve understanding of evacuation-efficiency factors. The findings can contribute to reducing casualties and property losses and improving UCS's fire safety management.

With the emergence of new applications driven by the popularization of mobile devices, the next generation of mobile networks faces challenges to meet different requirements. Virtual Network Functions (VNFs) have been deployed to minimize operational costs and make network management more flexible. In this sense, strategies for VNF placement can impact different metrics of interest. Invoking and visiting VNFs in a specific execution order may be required for different use cases, resulting in a complete network service called Service Function Chain (SFC). The SFC placement problem is to define a feasible path in the physical infrastructure whose nodes and edges meet the computational and bandwidth requirements for the VNFs and virtual links, respectively. It has already been proved that this process is NP-hard and it is difficult to find an optimal solution to this problem. Therefore, in this paper, we propose the use of meta-heuristics to solve the SFC placement problem in cellular networks. We consider a triathlon competition leading to different mobility patterns. We collected real data about the competitors to simulate their movements through the scenario as well as the measured signal quality of the network. We formulate the SFC placement problem as a multi-objective problem where we try to minimize the placement cost and the total SFC delay. To solve the problem, we propose the use of two algorithms, NSGA-II and GDE3, which compare two different greedy approaches that prioritize the different optimization metrics considered in this work. Our results show that the meta-heuristics provide better results for each of the metrics. For all competition stages, GDE3 presented a slightly lower placement costs than NSGA-II, while NSGA-II had a lower delay in some scenarios.

Multi-resolution modelling (MRM) has been increasingly applied in engineering practice. While in a distributed simulation environments, time advance method for MRM is less studied. When the system is running in a relatively low-resolution status, it usually adopts basic time advance modes to save simulation resources. However, these basic modes usually suffer from inaccurate response of interactions and are separate from the resolution switching process. In this paper, we propose an improved time advance method for distributed multi-resolution models. We introduce an interaction table to enable dynamic management of interactions, and decouple the interaction request process from the interaction response process, resulting in a more fine-grained interactive mode. Moreover, the interactive deviation index is introduced to quantitatively characterize the cumulative errors from delayed interactions. This index has been incorporated into resolution switching conditions to provide a more flexible and intuitive way for multi-resolution modelling. Proposed method has been verified on both theoretical cases and practical applications, and can achieve high simulation accuracy with less time consumption.

In the rapidly evolving domain of serverless computing, the need for efficient and accurate predictive methods of function invocation becomes paramount. This study introduces a comprehensive suite of innovations to improve the predictability and efficiency of function invocation within serverless architectures. By employing multi-output regression models, we perform a multi-level analysis of function invocation patterns across user, application, and function levels, revealing insights into granular workload behaviors. We rigorously investigate the impact of windowing techniques and dimensionality reduction on model performance via Principal Component Analysis (PCA), offering a nuanced understanding of data complexities and computational implications. Our novel comparative analysis framework meticulously evaluates the performance of these methods against various windowing configurations, utilizing the Azure Functions dataset for real-world applicability. In addition, we assess the temporal stability of the models and the variation of day-to-day performance, providing a holistic view of their operational viability. Our contributions address critical gaps in the predictive modeling of serverless computing and set a new benchmark for operational efficiency and data-driven decision-making in cloud environments. This study is poised to guide future advancements in serverless computing, driving theoretically sound and practically viable innovations.

Effective evacuation guidance can guarantee people's safety by facilitating their swiftly exit hazardous areas during an emergency. However, pre-determined guidance plans based solely on distance comparisons to exits may not always be the most effective due to unstable accessibility conditions and uneven crowd distribution. Therefore, it is imperative to incorporate real-time optimal guidance information in the plan. Coupling simplified CTM (Cell Transmission Model)-based simulation, this study proposed a computationally efficient DRF (Directed Rooted Forest)-encoded planning for developing evacuation guidance plan. Taking them as a holistic model, the simulator predicts evacuation dynamics at a constant computational cost regardless of crowd size, while the planning module optimizes the guidance plan directionally by leveraging the simulation's intermediate and final outputs. Numerical tests have demonstrated that the tight coupling between optimization and simulation module has substantially enhanced the model's capacity to fine-tune the guidance plan and optimization efficiency. The proposed model may serve as the foundation for developing real-time evacuation guidance plans for large-scale crowded buildings, either on the premise of accelerated simulation or as an efficient generator of training data for machine learning models.

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.