Discrete Event Simulation Research Articles

SimPy Simulation to Optimize Remote Patient Monitoring and Server Usage in Healthcare System

The recent pandemic has shown us the necessity of handling a huge mass of patients in real-time scenarios. This is important because in real-time scenarios when the entire healthcare system is operating in its full swing to deliver services to the patient, it is equally important to monitor the arrival time of the patient at the hospital till the disposal of a patient from the hospital. Hence, that each patient must wait minimum time frame there by using the existing remote healthcare infrastructure. To perform this micro level (i.e. patient level) monitoring of the remote healthcare ecosystem, an integrated cloud-based healthcare system (CHS) should be proposed which will analyse, simulate, and predict a huge load of service requests of a patient. At the same time, it can prepare the entire healthcare ecosystem to face the real-time need of any nation and deliver corresponding service response to every patient in a fastest possible manner. In this regard, Discrete event simulation in SimPy (DES) methodology is of utmost help to the amplification of remote healthcare infrastructure on a real scale. In this report, authors have found 80 percent server efficiency and 1.4 s waiting time average for each patient in the proposed model. Server efficiency and patient waiting time mapping typically has become very promising action for any remote cloud-assisted healthcare service delivery model. Idle time analysis of servers can stimulate remote resource utilization and scalability in patient modelling.

Preventing Workload Interference with Intelligent Routing and Flexible Job Placement Strategy on Dragonfly System

Dragonfly is an indispensable interconnect topology for exascale HPC systems. To link tens of thousands of compute nodes at a reasonable cost, Dragonfly shares network resources with the entire system such that network bandwidth is not exclusive to any single application. Since HPC systems are usually shared among multiple co-running applications at the same time, network competition between co-existing workloads is inevitable. This network contention manifests as workload interference, where a job’s network communication can be severely delayed by other jobs. This study presents a comprehensive examination of leveraging intelligent routing and flexible job placement to mitigate workload interference on Dragonfly systems. Specifically, we leverage the parallel discrete event simulation toolkit, SST, to investigate workload interference on Dragonfly with three contributions. We first present Q-adaptive routing, a multi-agent reinforcement learning routing scheme, and a flexible job placement strategy that, together, can mitigate workload interference based on workload communication characteristics. Next, we enhance SST with Q-adaptive routing and develop an automatic module that serves as the bridge between SST and HPC job scheduler for automatic simulation configuration and automated simulation launching. Finally, we extensively examine the workload interference under various job placement and routing configurations.

Dynamic thermal simulation of a tree-shaped district heating network based on discrete event simulation

The computational complexity involved in modelling district heating (DH) networks impedes the integration of network operations into comprehensive DH system studies. We developed a flexible, accurate, and fast dynamic thermal simulation model utilising discrete event simulation (DES). This model is versatile, suitable for any tree-shaped DH network with a central heating plant and can estimate node temperatures and calculate pipe heat losses. The speed of the model is improved via using variable time steps and by incorporating two advanced techniques: lazy evaluation and a customised priority queue. To further improve the computational speed, we developed a technique to eliminate redundant sampling points. This model was tested and demonstrated excellent consistency with actual measurements. Remarkably, reducing sampling points can speed up the simulation by a factor of three without compromising the temperature accuracy. A 72-day simulation of a network with 102 pipes was completed within 0.219 s. Our findings highlight the significant potential of the DES model for large-scale dynamic network simulations and offer a promising solution for DH network simulations and system optimisation.

Development of the parallel and distributed simulation field

This paper documents the origins and development of the parallel and distributed simulation field, especially as it relates to parallel discrete event simulation (PDES) technology. My focus is on the people and ideas that contributed to the creation and evolution of PDES technology over the past five decades. Much of this paper reviews the research conducted in academia and research laboratories that were the driving forces behind the development of PDES technology as well as subsequent efforts in government and industry to bring the technology to modeling and simulation practitioners. The origins of PDES technology lie in a fundamental technical issue called the synchronization problem. This problem is discussed, as well as the two main solution approaches that were created. The development of these approaches and their evolution to form the time management services of the High Level Architecture (HLA) standard are described, as well as the community of researchers and practitioners that formed in establishing this field. Commercialization efforts are discussed followed by a brief discussion of areas requiring further research. The importance of the Society for Modeling and Simulation International (SCS) and its flagship journal, Simulation, in the development of the field are discussed. Throughout this discussion, I provide my personal observations and take away lessons concerning the development of the field.

Measuring supply chain resilience for informed resilience strategy investment

AbstractExisting supply chain resilience (SCR) research lacks a clear, holistic method to quantify resilience. This paper provides an improved SCR metric while exploring the inherent tradeoff between investments in inventory and production capacity to yield the most resilient supply chain network given budget constraints. Discrete event simulation is used to develop a generalizable metric to provide insight into how supply chain design and investment determine a network's response to a disruption. The developed resilience capability metric (RCM), utilizing area under the curve, measures the cumulative performance level of the system from disruption to a predetermined endpoint, representing how much of the system demand can be served by different network designs. Results indicate inventory and production have different effects on how a system behaves postdisruption, but that the two SCR investments should not be viewed independently since optimal performance is connected to coordinated adjustments to both. Additionally, the use of the RCM shows that a key factor in a supply chain's resilience is the speed of the response. By measuring the cumulative performance over the total event, RCM provides managers with the ability to truly compare different SCR investments to see which has the greatest impact on overall resilience.

Research on the Resource-Allocation-Optimization Strategy for Offshore Wind Power Construction Considering Complex Influencing Factors

The construction process of offshore wind farms involves multiple complexities, which is very complex to be scheduled manually, and the coordinating and optimized scheduling not only decreases project construction costs but also increases the construction speed. The impact of meteorological conditions on offshore wind power construction has been considered, and optimizing resource-allocation strategies under complex influencing factors has been analyzed. Then, a comprehensive strategy optimization index system is developed, which includes key indicators, such as the minimum working hours, resource-allocation-optimization rate, window period utilization rate, and cost–benefit ratio. Additionally, an offshore wind power resource-allocation-optimization model is formulated based on discrete event simulation (DES). A statistical analysis of each optimization index was performed using this model to assess the impact of resource-allocation strategies. The simulation results demonstrate that the model can not only simulate the construction process of offshore wind farms and monitor the state of wind turbines, personnel, and meteorological conditions in real time but also accurately calculate key indicators, such as the minimum working hours, resource-allocation-optimization rate, window period utilization rate, and cost–benefit ratio. This strategy effectively enhances resource-allocation efficiency during the wind farm installation phase and improves the overall construction process efficiency.

Performance Evaluation of Battery Swapping Stations for EVs: A Multi-Method Simulation Approach

This study presents an optimisation framework for operating a battery swapping station (BSS) to enhance efficiency and sustainability in electric vehicle (EV) infrastructure. A hybrid modelling approach combines agent-based discrete event simulation and linear programming to model the dynamic behaviour of batteries and operational processes within the BSS. The model considers factors such as charging speed, battery degradation, grid power constraints, customer behaviour, and range anxiety. The agent-based model simulates the interaction between vehicles, batteries, and the station, capturing the stochastic nature of EVs’ arrivals and battery demand with the discrete event simulation. The linear programming component optimises battery state transitions to minimise degradation and ensure that the demand is met while respecting the power limits of the grid. Different battery types are considered based on vehicle category, each with specific capacity and usage patterns, reflecting real-world market conditions. The results demonstrate that the proposed optimisation framework can effectively manage the complex operational needs of a BSS. The proposed framework effectively balances service quality with resource efficiency by employing a strategic mix of charging modes and inventory management, reducing operational and degradation costs. This approach supports a more sustainable EV infrastructure, highlighting BSS as a viable solution to enhance the efficiency and sustainability of EV operations. Furthermore, the analysis highlights the critical role of power limits in determining charging strategies and their impact on operational efficiency. The findings suggest that with optimised operations, BSS can play a critical role in accelerating the adoption of EVs by offering a faster, more reliable, and sustainable alternative.

Assessing resilience measures in the Austrian dairy supply chain: a case-based simulation integrating strategic, tactical, and operational decisions

Given the increasing complexity of supply chains and the potential for disruptive environmental factors, such as the COVID-19 pandemic, decision-makers must increase their (operational) resilience. In particular, food supply chain actors must adopt proactive and reactive strategies and measures to mitigate disruptions and avoid negative consequences, especially for short-term food supply. We present a comprehensive approach to resilience-related decision-making based on empirical data from Austria's dairy supply chain. The research identifies actionable resilience measures across strategic, tactical, and operational dimensions alongside generic resilience strategies. Once done, we integrate them into a discrete-event simulation model that emulates Austria's dairy supply chain. Using a realistic decision-making environment, the model facilitates quantifying and evaluating the effectiveness of the measures in response to disruptions. Insights from the case study assessed in the simulation model emphasise the importance of preparing for different types of disruptions and impact magnitudes with a diverse set of resilience measures. Furthermore, their integration must be carefully assessed for potential unintended consequences in the post-disruption phase. Effective supply chain resilience-related decision-making requires a thoughtful selection of strategic initiatives combined with tactical and operational implementation across generic resilience strategies to prepare for and respond to disruptions.

Spatial/Temporal Locality-Based Load-sharing in Speculative Discrete Event Simulation on Multi-core Machines

Shared-memory multi-processor/multi-core machines have become a reference for many application contexts. In particular, the recent literature on speculative parallel discrete event simulation has reshuffled the architectural organization of simulation systems in order to deeply exploit the main features of this type of machine. A core aspect dealt with has been the full sharing of the workload at the level of individual simulation events, which enables keeping the rollback incidence minimal. However, making each worker thread continuously switch its execution between events destined to different simulation objects does not favor locality. This problem appears even more evident in the case of Non-Uniform-Memory-Access (NUMA) machines, in which memory accesses generating a cache miss to be served by a far NUMA node give rise to both higher latency and higher traffic at the level of the NUMA interconnection. In this article, we propose a workload-sharing algorithm in which the worker threads can have short-term binding with specific simulation objects to favor spatial locality. The new bindings—carried out when a thread decides to switch its execution to other simulation objects—are based on both (a) the timeline according to which the object states have passed through the caching hierarchy and (b) the (dynamic) placement of objects within the NUMA architecture. At the same time, our solution still enables the worker threads to focus their activities on the events to be processed whose timestamps are closer to the simulation commit horizon—hence, we exploit temporal locality along virtual time and keep the rollback incidence minimal. In our design, we exploit lock-free constructs to support scalable thread synchronization while accessing the shared event pool. Furthermore, we exploit a multi-view approach of the event pool content, which additionally favors local accesses to the parts of the event pool that are currently relevant for the thread activity. Our solution has been released as an integration within the USE (Ultimate-Share-Everything) open-source speculative simulation platform available to the community. Furthermore, in this article we report the results of an experimental study that shows the effectiveness of our proposal.

Impact of the learning curve on the production performance of shipyard welding workers

ABSTRACT The growth of mechanisation and automation in welding technology has been significant, but welders continue to play an essential role in shipbuilding yards for productivity and health and safety purposes. Therefore, this study aims to investigate the correlation between welder performance and experience in manual gas metal arc welding operations in shipbuilding. The study utilises statistical data and discrete event simulation, within the scope of learning curve theory. The study determines how welder experience affects the welding quality and production rate by conducting an independent sample t-test and a single block erection simulation for welders with less experience (2 to 5 years) and highly experienced workers (8 to 17 years) respectively. The findings of this study contribute to the existing literature by providing evidence that welding quality and production rate in manual gas metal arc welding operations are majorly affected by welders’ job experience.

Delay reduction and interference mitigation by resource management for IAB networks

To overcome the increasing demand for high-throughput data services, the 3rd Generation Partnership Project (3GPP) has proposed Integrated Access and Backhaul (IAB) in Release 16 as a scalable and cost-effective alternative to traditional fiber backhaul for 5 G systems. A key feature of IAB is the shared use of the same spectrum band for both access and backhaul communications, which introduces challenges related to interference, delays, capacity sharing, and additional complexity due to the half-duplex constraint. In this paper, we provide an overview of the 3GPP guidelines for IAB resource management and introduce a semi-centralized, heuristic-based resource management policy aimed at jointly enhancing end-to-end delay and minimizing interference in IAB networks, in compliance with 3GPP standards. We conduct a comparative study of our proposed policy against several policies, including the baseline proposed by 3GPP. The evaluation is performed through discrete-event simulations under two types of traffic and varying network loads. The simulation results underline that our semi-centralized resource management policy effectively controls interference, leading to improved network Key Performance indicators (KPIs) like system throughput and end-to-end delay. Specifically, our solution achieves up to an 11% increase in system throughput and a 30% reduction in end-to-end delay compared to the baseline for constant traffic, while for variable traffic, it results in a 16% increase in system throughput and a 26% reduction in end-to-end delay.

Optimal multiresource planning for the elderly care system: a case study

This paper investigates an optimal long-term bed and caregiver plan for the elderly care system (ECS) to meet the increasing care demand due to accelerating population aging in China. Care demand is dynamic due to various arrival ratepatterns, heterogeneous lengths of stay, health status, and balking and reneging behaviours, while supply depends on health/age/waiting-time-based priority queueing strategies. We develop a simulation-optimisation model that combines discrete event simulation, a bisection search algorithm and a feasibility detection procedure for this problem. Based on data from the Chinese Longitudinal Healthy Longevity Survey, the Ministry of Civil Affairs of China, and the queue system for senior care institutions in Shenzhen, the results show that the increasing arrival pattern saves operational costs and waiting time. The age-based-priority (ABP) strategy can reduce evaluation and waiting costs and prevent rent-seeking before application because it counts only older adult’ age. Balking helps to reduce reneging. Low ratio of caregivers to seniors will lead to significant underestimation of caregiver capacity. The ECS can purchase beds step by step, apply the ABP strategy for admission, release waiting information to induce balking behaviour, and hire part-time caregivers or rent service robots to reach the optimal plan while serving more older adults.

A Combined Capacity Planning and Simulation Approach for the Optimization of AGV Systems in Complex Production Logistics Environments

Background: The capacity planning of production systems is one of the most fundamental strategic problems in the creation of a production plant. However, the implementation of increasingly complex production systems combined with sophisticated automated material handling justifies the development of novel approaches to solve the combined capacity planning and material handling problem, which is also the objective of the current study. Methods: The presented approach combines the use of capacity planning formulas and discrete event simulation for optimizing extensive automated guided vehicle (AGV) systems from the aspect of the number of required vehicles. Extensive series of simulation experiments are applied in the case of each model variant for optimal results and to account for machine failures in the system. Results: The application of the proposed method is demonstrated through a realistic sample problem in a plastic industry setting with the use of the Siemens Tecnomatix Plant Simulation software (version 2302.0003, Educational license). Conclusions: The results from the sample problem demonstrate the usefulness of the approach, as a non-intuitive solution proved to be the most efficient. Additionally, the main advantage of the method is that it provides a standardized framework for the simulation-based optimization of AGV systems starting out from the comprehensive production capacity parameters.

Enhancing the productivity of mould shop using continuous improvement tools and simulation

In today’s competitive manufacturing environment, enhancing productivity and operational efficiency is crucial for companies to meet the rising customer demands and expectations driven by recent technological advancements. This study presents a case of productivity enhancement in mould shop manufacturing, which was experiencing bottlenecks and non-value-added (NVA) activities, leading to inefficiencies that limit the throughput. A Discrete Event Simulation (DES) model is developed for the mould shop considering the major components such as Hub, Mainframe, and Planet Carrier. Bottleneck resources are identified from the DES model. The validation of the simulation results is carried out using Pareto analysis. Root Cause Analysis (RCA) and Five Whys (5-Why) analysis are used to brainstorm the causes that lead to lower productivity. Time-reduction strategies using the Theory of Inventive Problem Solving (TRIZ) improved the throughput. The improvements are incorporated into the DES model, and updated results are obtained. As a result, throughput for the hub improved by 60% for the hub, 40% for the mainframe, and 55.88% for the planet carrier. These results show how integrating continuous improvement tools with simulation models enables streamlining operations and substantially increases productivity. The scenario reveals that Lean principles successfully overcome real-world manufacturing problems.

An Integrated Stochastic Optimization and Simulation Approach to SERU vs. Assembly Line Manufacturing Systems

This research compares SERU manufacturing systems to traditional assembly lines, focusing on the impact of uncertainty in task processing time on production output. The study considers worker skill levels and team identity, using a stochastic mixed integer linear programming approach to model uncertainty and optimize workforce allocation. Discrete event simulation is then integrated to evaluate performance using five key performance indicators (KPIs). Results show that SERU systems outperform traditional lines in terms of throughput when uncertainty is considered. The integrated approach provides more reliable performance data than deterministic optimization alone. The study also highlights the advantages of SERU systems when worker skill levels and team identity are factored in. This research fills a gap in the literature by proposing a stochastic optimization approach that considers uncertainty and worker skill levels, and by integrating stochastic optimization with simulation for comprehensive analysis. This approach provides valuable guidance for production managers in optimizing production systems.

Accident cause analysis for lifting prefabricated components of assembled buildings based on hybrid simulation technology from human factor perspective

PurposeThis paper aims to explore the characteristics of lifting accidents and the significance of influencing factors and explain the causes from the perspective of human factors, thereby achieving a more accurate understanding of and prevention of lifting accidents.Design/methodology/approachA mixed simulation model for prefabricated component lifting is established by combining discrete event simulation (DES) with the system dynamics (SD) method. In addition, essential parameters and relationships within the system dynamics model are determined through survey questionnaires. Finally, the human factors analysis and classification system (HFACS) is used to analyze the cause of the accident.FindingsThe results show that workers falling from height and workers struck by objects are the most frequent types of lifting accidents. In 2072 experiments, these two types of accidents occurred three and five times, respectively. Besides, the links of “crane movement,” “component binding,” “component placement” and “component unhooking” are particularly prone to lifting accidents. In addition, the completeness of emergency plans, failure to observe the status of the tower crane and lack of safety education and training have emerged as primary influencing factors contributing to the occurrence of lifting accidents.Originality/valueThe findings of the study can serve as a reference basis for practitioners, enabling them to preemptively identify possible risk accidents and adopt corresponding measures to prevent them, ensuring the safety and property of practitioners. Additionally, targeted suggestions and innovative ideas are provided to enhance the safety guarantee of the lifting industry and promote its healthy and stable development through a more concrete theoretical foundation and practical guidance.

Performance Analysis of Wireless Sensor Networks Using Damped Oscillation Functions for the Packet Transmission Probability

Wireless sensor networks are composed of many nodes distributed in a region of interest to monitor different environments and physical variables. In many cases, access to nodes is not easy or feasible. As such, the system lifetime is a primary design parameter to consider in the design of these networks. In this regard, for some applications, it is preferable to extend the system lifetime by actively reducing the number of packet transmissions and, thus, the number of reports. The system administrator can be aware of such reporting reduction to distinguish this final phase from a malfunction of the system or even an attack. Given this, we explore different mathematical functions that drastically reduce the number of packet transmissions when the residual energy in the system is low but still allow for an adequate number of transmissions. Indeed, in previous works, where the negative exponential distribution is used, the system reaches the point of zero transmissions extremely fast. Hence, we propose different dampening functions with different decreasing rates that present oscillations to allow for packet transmissions even at the end of the system lifetime. We compare the system performance under these mathematical functions, which, to the best of our knowledge, have never been used before, to find the most adequate transmission scheme for packet transmissions and system lifetime. We develop an analytical model based on a discrete-time Markov chain to show that a moderately decreasing function provides the best results. We also develop a discrete event simulator to validate the analytical results.

Discrete-event modeling of human behavior for spread of diseases on university campuses

The COVID-19 pandemic has highlighted the importance of defining sound policies to make attending workplaces safely. Sometimes, deciding on different policies is challenging as this highly depends on the behavior of the individuals. This research introduces a Discrete Event-based methodology and a prototype implementation to study such policies, including human behavior along with information about the workplace layout and building characteristics such as ventilation rate or room capacity. The method is based on a combination of agent-based models, diffusion processes and discrete-event simulation. We exemplify how to use this method using a case study based on Carleton University’s Campus, in which we use the methodology and tools to study the effect of ventilation, as well as the application of a policy where sick students are denied entry to the campus on the number of disease cases on campus.

Assessing the impact of technology partners on the level of cyberattack damage in hospitals

ObjectiveReliable performance of medical devices is crucial for hospitals. However, these devices are increasingly connected with the internet and, thus, prone to cyberattacks resulting in risks for patient safety and financial loss. As the number of specialists in this field is limited in most hospitals, technology partnerships with medical technology manufacturers can be a suitable concept for increasing the level of security or limiting damage in the event of a cyberattack. MethodsBased on a discrete event simulation model (DES), the effects of security incidents with different degrees of impact on downtime costs, length of stay, staff utilization and lost arrivals in an emergency department of a general hospital were modelled and simulated. The effects of a technology partnership were simulated using what-if scenarios in order to be able to draw conclusions about the benefits by comparing the avoidable damage effects and the investment costs incurred for a technology partnership. ResultsDepending on the scenario, the resulting savings range from €245,579 to €315,768, with a cost-benefit ratio between 4 and 5 over a 21-day period. Non-financial benefit (e.g. shorter lengths of stay or reduction IT resources) can also be achieved. ConclusionOur analysis demonstrates that the level of security for hospitals and their medical devices as well as the operational functionality in the event of damage can be increased if such a concept it applied, i.e., patient safety can be increased while costs can be cut.

Simulation-based deep reinforcement learning for multi-objective identical parallel machine scheduling problem

In the shipbuilding industry, traditional optimization studies based on linear programming and constraint programming have been conducted to solve mid-term or long-term scheduling problems. However, due to the extensive computational time, these methods face limitations in addressing short-term scheduling problems for the unit production systems of shipbuilding processes, where various environmental uncertainties must be considered. This study employs a deep reinforcement learning approach to develop a dynamic scheduling algorithm for the welding process in profile shops, considering the random arrival of materials and variability in processing time. The scheduling problems of the welding process are formulated as multi-objective identical parallel machine scheduling problems, aimed at minimizing both setup time and tardiness. This study proposes a novel Markov decision process model for the multi-objective scheduling problems for the welding process, incorporating setup requirements and due date-related constraints into the state representation, action modelling, and reward design. Additionally, based on the proposed Markov decision process model, this study develops a learning environment in which a discrete-event simulation model of the welding process is integrated for state transition considering the uncertainties in the welding process. In the training phase of the scheduling agent, the Proximal Policy Optimization algorithm is applied to learn the scheduling policy, which is approximated by deep neural networks. The performance of the proposed algorithm is validated in comparison to four priority rules (SSPT, ATCS, MDD, and COVERT) for various test scenarios with different workloads and levels of variability in processing time.