Discrete Event Simulation Framework Research Articles

A discrete-event multi-agent simulation framework supporting well-to-wheel analysis for greening commercial maritime ports

A discrete-event multi-agent simulation framework supporting well-to-wheel analysis for greening commercial maritime ports

A discrete-event simulation study for pre-hospital emergency systems

ABSTRACT Ambulance dispatch, deployment, and relocation are crucial decisions in pre-hospital emergency medical services (EMS) that significantly impact response times. In metropolitan areas, the rapid movement of people and complex traffic conditions create a challenging environment for effective decision-making. This paper presents a discrete-event simulation framework designed to evaluate and improve pre-hospital emergency systems by capturing core EMS workflows and incorporating comprehensive, dynamic data. To demonstrate the practical application of this framework, a simulation instance is implemented and validated in the context of downtown Shanghai, with an analysis of key performance indicators for EMS in this area. Additionally, we propose a dynamic ambulance relocation model to improve the alignment between ambulance deployment plans and emergency calls. Simulation experiments show that dynamic ambulance relocation effectively reduces response times. Due to its ability to integrate real-time data, this framework has the potential to support immediate decision-making and evaluate proposed strategies in the real world.

Enhanced Scheduling of AI Applications in Multi-Tenant Cloud Using Genetic Optimizations

The artificial intelligence (AI) industry is increasingly integrating with diverse sectors such as smart logistics, FinTech, entertainment, and cloud computing. This expansion has led to the coexistence of heterogeneous applications within multi-tenant systems, presenting significant scheduling challenges. This paper addresses these challenges by exploring the scheduling of various machine learning workloads in large-scale, multi-tenant cloud systems that utilize heterogeneous GPUs. Traditional scheduling strategies often struggle to achieve satisfactory results due to low GPU utilization in these complex environments. To address this issue, we propose a novel scheduling approach that employs a genetic optimization technique, implemented within a process-oriented discrete-event simulation framework, to effectively orchestrate various machine learning tasks. We evaluate our approach using workload traces from Alibaba’s MLaaS cluster with over 6000 heterogeneous GPUs. The results show that our scheduling improves GPU utilization by 12.8% compared to Round-Robin scheduling, demonstrating the effectiveness of the solution in optimizing cloud-based GPU scheduling.

An optimization and discrete event simulation framework for evaluating delivery performance in Swedish wood supply chains under stochastic weather variations

ABSTRACT Delivery performance in wood supply chains is affected by carrying capacity on forest roads. Stochastic weather variations affect carrying capacity of roads and accessibility of harvested wood on landings. In Sweden, annual harvest scheduling is usually based on four seasons and road accessibility classes are set to match these seasons. In reality, road accessibility changes depend on weather variations not season. This causes a risk of poor delivery performance. Delivery performance is evaluated using two performance indicators, backorders (orders not delivered on time) and lead time (time from harvesting to delivery to mill). A data-analytic framework, combining optimization and simulation, is proposed to assess and evaluate these performance indicators for the wood supply chain under stochastic weather variations. An optimization model is used to generate a feasible harvest schedule for expected weather. This is thereafter evaluated given various weather scenarios using a discrete-event simulation model. The combination of optimization and discrete-event simulation is used to assess delivery performance over time, and changes in road accessibility caused by weather variation. An expected decrease in road accessibility implies a stable delivery performance, while an unexpected decrease causes a reduced delivery performance.

Exploring the Dynamics of the Specialty Insurance Market Using a Novel Discrete Event Simulation Framework: A Lloyd's of London Case Study

Exploring the Dynamics of the Specialty Insurance Market Using a Novel Discrete Event Simulation Framework: A Lloyd's of London Case Study

The inclusion of uncertainty in circularity transition modeling: A case study on wind turbine blade end-of-life management

The transition to a more circular economy (CE) is complex and hard to predict, including in sustainable energy technologies. Many sources of uncertainty make it challenging to model CE scenarios and their potential benefits. As an example, the high variability in costs and revenues of different recycling options makes future wind turbine blade recycling highly uncertain. To better understand this challenge, the circular economy, life cycle assessment and visualization (CELAVI) framework – a discrete event simulation and life cycle assessment framework – is modified to incorporate uncertainty analysis capabilities. Moreover, a 3-step procedure that covers different aspects of uncertainty in CE studies and includes a Monte-Carlo analysis is proposed. The procedure is tested in a case study on wind turbine blade recycling using CELAVI. Results highlight that grinding and landfilling costs are the most influential parameters for wind power circularity. The model’s output coefficients of variation (when input parameter uncertainties are propagated) are between 92% and 384% depending on the indicator. The approach developed in this study may help researchers and decision-makers who study circularity prioritize their data collection effort. Finally, our method contributes to a mounting yet critical body of research: the measurement of uncertainty in circularity transitions.

Simulating the Software Development Lifecycle: The Waterfall Model

This study employs a simulation-based approach, adapting the waterfall model, to provide estimates for software project and individual phase completion times. Additionally, it pinpoints potential efficiency issues stemming from suboptimal resource levels. We implement our software development lifecycle simulation using SimPy, a Python discrete-event simulation framework. Our model is executed within the context of a software house on 100 projects of varying sizes examining two scenarios. The first provides insight based on an initial set of resources, which reveals the presence of resource bottlenecks, particularly a shortage of programmers for the implementation phase. The second scenario uses a level of resources that would achieve zero-wait time, identified using a stepwise algorithm. The findings illustrate the advantage of using simulations as a safe and effective way to experiment and plan for software development projects. Such simulations allow those managing software development projects to make accurate, evidence-based projections as to phase and project completion times as well as explore the interplay with resources.

Developing a framework for generating production-dependent failure rate through discrete-event simulation

Over the last decades, maintenance has experienced a transition from being a necessary evil to being a pivotal resource to create value for enterprises. Within the process of maintenance planning, distinct decisions could be responsible for different outcomes concerning profit and equipment reliability. Consequently, maintenance optimization has become pivotal to achieving relevant business goals. One of the most popular approaches to conduct maintenance optimization is simulation-based optimization, especially Discrete-Event Simulation (DES). Most works related to DES for maintenance optimization purposes focus on modeling imperfect maintenance or imperfect inspection and prognosis, while failures are often generated through a Weibull distribution. However, failure strongly depends on the production rate or the stress level, defining a Dynamic Non-Homogeneous Poisson Process (DNHPP). To this end, this paper proposes an algorithm for scheduling such DNHPP failure events in a DES framework model and, as a first implementation to apply it, an open-access library capable of generating stress level-dependent failures within the Rockwell ARENA© simulation environment. The developed package, that in the future will be ported to other relevant off-the-shelf simulation environments, provides a more realistic tool for maintenance engineers and researchers to optimize or compare maintenance strategies from an economic perspective.

Simulation and optimization of dynamic-hybrid parking reservation strategies for one-way vehicle-sharing systems

ABSTRACT In this paper, we explore the regulation of one-way station-based vehicle-sharing system (OSVS) through dynamic-hybrid parking reservation policies. We first propose a dynamic-hybrid parking reservation policy. This policy only requires trips with expected travel distances shorter than a specific threshold to make a parking reservation. The distance threshold varies with time. We develop a discrete event simulation model based on the O2DES (object-oriented discrete event simulation) framework to compare the dynamic-hybrid parking reservation (DHPR) strategy with the no-reservation (NR), static-hybrid parking reservation (SHPR) and complete parking reservation (CPR) strategies. Furthermore, we propose a simulation-optimization model and an Elitism-based Genetic algorithm with the optimal computation budget allocation to determine the fleet size, station capacity, and dynamic reservation distance threshold. The analysis of case studies of a real-world system indicates that DHPR is always superior to NR, SHPR and CPR.

Unprotected Left-Turn Behavior Model Capturing Path Variations at Intersections

Path dispersion (the spatial distribution of vehicular paths) is an important feature of traffic flow inside intersections and differs from traffic flow running along traffic lanes at road segment, especially under conflicting movements. The path dispersion reflects the operational features of traffic flow and is related to driving behaviour, arrival flow patterns, layout design, and the traffic control and management scheme. This study aims to improve the understanding of the overall path dispersion of unprotected left-turns and the opposing through movement. A behavioural simulation model was established to represent the overall path dispersion. Human behaviours regarding vehicle trajectory planning with and without conflicting vehicles were modelled based on optimal control and integrated into the proposed discrete event simulation framework. The descriptive power and accuracy of the proposed simulation model were validated using empirical data. The effects of the spatial size of the intersection, crossing angle, and traffic volume on the path dispersion of the left-turn and through movement were explored based on numerical experiments. The results show that the proposed simulation model can represent the path dispersion of left-turn and opposing through movement well for both the calibrated intersections and newly added intersections without model parameter recalibration with an average error of 8.92%.

Two-Level Full Factorial Design Approach for the Analysis of Multi-Lane Highway Section under Saturated and Unsaturated Traffic Flow Conditions

Oversaturation of highways occurs due to their inadequate assessment and design. In this paper, we propose both a mathematical queuing model and a Discrete-Event Simulation (DES) framework based on Newell’s triangular flow-density relationship for the performance analysis of a multi-lane highway section. The proposed framework is a finite capacity queuing system, which captures an increase in the flow with the vehicle density up to the capacity of the section in an unsaturated condition and a decrease in the flow in the case of a saturated condition, depicting the actual traffic conditions on the highway section. First, the Birth–Death Process is used to build the mathematical queuing model (BDP), and the average number of vehicles (average queue length) and blocking probability on the highway section are estimated. Then, the accuracy of the mathematical queuing model is verified by the proposed DES framework. The “significance and effects” of different design factors are evaluated using the two-level full factorial design technique. The analysis of the experimental results reveals that the length of the highway section and the number of lanes are the most significant factors affecting the average queue length and blocking probability, while the jam density only has a significant effect on the average queue length and does not affect the blocking probability. In case of a two-way interaction, the combined effect of the “length-lanes” significantly affects the average queue length. In the end, a multiple-factor linear regression model is also developed for the prediction of the average number of vehicles on the highway section based on the design factors.

Using a hybrid simulation model to assess the impacts of combined COVID-19 containment measures in a high-speed train station

ABSTRACT In this study, we present a hybrid agent-based model (ABM) and discrete event simulation (DES) framework where ABM captures the spread dynamics of COVID-19 via asymptomatic passengers and DES captures the impacts of environmental variables, such as service process capacity, on the results of different containment measures in a typical high-speed train station in China. The containment and control measures simulated include as-is (nothing changed) passenger flow control, enforcing social distancing, adherence level in face mask-wearing, and adding capacity to current service stations. These measures are evaluated individually and then jointly under a different initial number of asymptomatic passengers. The results show how some measures can consolidate the outcomes for each other, while combinations of certain measures could compromise the outcomes for one or the other due to unbalanced service process configurations. The hybrid ABM and DES models offer a useful multi-function simulation tool to help inform decision/policy makers of intervention designs and implementations for addressing issues like public health emergencies and emergency evacuations. Challenges still exist for the hybrid model due to the limited availability of simulation platforms, extensive consumption of computing resources, and difficulties in validation and optimisation.

FaMoSim: a facilitated discrete event simulation framework to support online studies

Paper aims To propose a framework to support online simulation studies considering facilitated modeling and concepts of modern industry context, such as agility and flexibility. Originality Since the frameworks in the literature deal with simulation projects focused on healthcare and face-to-face meetings, the present work innovates by offering an agile and flexible guide for simulation projects in production systems, which also supports online interventions. Research method Action Research method was used to develop the framework. After its development, the FaMoSim (Facilitated Modeling Simulation) framework was applied in a real case to evaluate its applicability. Main findings In the application of FaMoSim, we achieved the framework's objectives: carrying out a faster (up to 3 months) and more flexible online modeling process; creating a simple computer model that does not require a complex data collection structure nor a specialist team; generating a better understanding of the process and assisting the stakeholders in identifying improvements. Implications for theory and practice Considering some challenges that prevent the expansion of DES studies, the framework assists in expanding DES studies in environments where it is not widely used. The framework supports online interventions, making it an interesting tool that can be used mainly in times of social distancing.

Evaluating the long-term operational performance of a large-scale inland terminal: A discrete event simulation-based modeling approach.

Inland terminals, or dry ports, have played an important role in multimodal transportation networks as transportation hubs that provide connections between seaports and hinterland economies. While important, evaluating the operational performance of a dry port is especially challenging since it depends not only on internal factors, such as the variety and number of container handling equipment (CHE) deployed, but also on other external factors, including changes in transportation policies and container demands experienced by a dry port. To properly evaluate the holistic performance of a dry port while considering all the aforementioned factors, a discrete event simulation (DES) framework is herein developed and applied to the Ladkrabang Inland Container Depot (LICD)-one of the largest dry ports in Southeast Asia-under various operational settings. Despite complicated internal operations, the devised DES framework has shown itself useful in the analyses of LICD, due largely to its flexibility that allows users to include sophisticated operational rules into models. According to our computational results, the current LICD operation is markedly ineffective as the usage rates of all CHE types are relatively low and varied across gate operators-especially the yard truck whose values range between 2.46% and 11.15% on yearly average. We also find that, by redesigning the LICD and its internal operations, the LICD's performance could be substantially enhanced-even with fewer numbers of CHE. Regarding the four CHE types, the reach stacker seems to limit LICD's capability, as its utilization tends to first reach the maximum allowable rate of 75%, while the rubber tyred gantry crane could help boost the usage rate of yard trucks, which, in turn, results in reduced container dwelling times. Nonetheless, the modified LICD could accommodate up to 140% of the current container demand before it experiences operational difficulties induced by the saturation of container flow from rail transportation.

Multi-voltage level distributed backward–forward sweep power flow algorithm in an agent-based discrete-event simulation framework

In the energy transition context, the use of steady-state time series is a promising approach to account for temporal interdependencies and flexibilities in modern distribution power system analysis, planning, and operation processes. This paper proposes a distributed backward–forward sweep power flow algorithm executed in a discrete-event, agent-based simulation framework. The algorithm shows fast convergence, allows for concurrent execution, and scales up to large-scale multi-voltage level grids with arbitrary topology. An agent-based simulation model integrates the developed algorithm to generate detailed grid utilization, asset, and system participant time series. We demonstrate the capabilities of our approach by performing several simulations, leveraging the proposed algorithm, on nine different benchmark grid models. The selected models comprise grids at a single voltage level, medium voltage level, and combined multi-voltage levels. The evaluation of the numerical results validates the approach and demonstrates its capabilities.

Modified State-Dependent Queuing Model for the Capacity Analysis of Metro Rail Transit Station Corridor during COVID-19

The COVID-19 pandemic policies have had a significant impact on the daily commuter flow at the metro rail transit stations. In this study, we propose a modified state-dependent M(n)/G(n)/C/C queuing model for the analysis of commuter flow in the corridor of metro rail transit stations in the COVID-19 situation in order to ensure safe social distance. The proposed model is a finite capacity queuing system with state-dependent commuter arrivals and state-dependent service rates based on the flow–density relationship. First, a mathematical queuing model is developed by using the birth–death process (BDP) and the expected number of commuters, and average area occupied per commuter and blocking probabilities are computed. Then, the accuracy of the proposed model is verified by a discrete-event simulation (DES) framework. (1) The proposed model’s results are compared to those of the existing M/G(n)/C/C model. The proposed modified model’s sensitivity analysis revealed that the anticipated number of commuters in the corridor remains smaller when the arrival rate is state-dependent. (2) In accordance with COVID-19 protocol, when the facility is congested, commuters are discouraged from entering and a safe social distance is maintained between them. (3) No commuters are impeded, and adequate throughput is ensured from the corridor. The proposed model will assist the metro rail transit station operators in making intelligent decisions regarding the operations in the COVID-19 situation.

A human behavior-based simulation model for resource allocation at a multi-specialty outpatient clinic

ABSTRACT Improving the performance of healthcare organisations and implementing management tools successfully depends heavily on understanding human behaviour. As a core behavioural dimension, stress affects outcomes in a variety of related decision-making and performance contexts, including resource and capacity planning. In this regard, this study proposes a human behaviour-based simulation approach for resource allocation in multi-speciality outpatient clinics. First, a stress model was developed for physicians, as the most critical resource in outpatient settings. Then, the resulting relationships were integrated into a discrete event simulation framework. To prove the relevance of our approach, we applied it to a case study conducted in a public multi-speciality outpatient clinic. Multiple “what-if” scenarios were tested and compared to the optimal exam room allocation. Findings show that, in contrast to a basic model, the proposed stress-driven model allocates effective numbers of exam rooms, setting physicians in their optimal stress zone, thereby leading to better performance.

Using Discrete-Event Simulation for a Holistic Aircraft Life Cycle Assessment

With growing environmental awareness and the resulting pressure on aviation, ecological impact assessments are becoming increasingly important. Life cycle assessment has been widely used in the literature as a tool to assess the environmental impact of aircraft. However, due to the complexity of the method itself and the long lifespans of aircraft, most studies so far have made strong simplifications, especially concerning the operational phase. Using a combined discrete-event simulation framework, this paper aims to ecologically assess the individual life cycle phases of an aircraft. The method will be demonstrated in a case study of an A320 and subsequently compared with findings from the literature. Despite the significant environmental impact of flight operations, which covers almost 99.8% of the entire life cycle of the aircraft, a detailed consideration of all life cycle phases is essential to serve as a reference for the ecological assessment of novel aircraft concepts. The presented assessment method thus enables a holistic analysis at an early stage of the design process and supports the decision-making for new technologies and operational changes.

An Agricultural Event Prediction Framework towards Anticipatory Scheduling of Robot Fleets: General Concepts and Case Studies

Harvesting in soft-fruit farms is labor intensive, time consuming and is severely affected by scarcity of skilled labors. Among several activities during soft-fruit harvesting, human pickers take 20–30% of overall operation time into the logistics activities. Such an unproductive time, for example, can be reduced by optimally deploying a fleet of agricultural robots and schedule them by anticipating the human activity behaviour (state) during harvesting. In this paper, we propose a framework for spatio-temporal prediction of human pickers’ activities while they are picking fruits in agriculture fields. Here we exploit temporal patterns of picking operation and 2D discrete points, called topological nodes, as spatial constraints imposed by the agricultural environment. Both information are used in the prediction framework in combination with a variant of the Hidden Markov Model (HMM) algorithm to create two modules. The proposed methodology is validated with two test cases. In Test Case 1, the first module selects an optimal temporal model called as picking_state_progression model that uses temporal features of a picker state (event) to statistically evaluate an adequate number of intra-states also called sub-states. In Test Case 2, the second module uses the outcome from the optimal temporal model in the subsequent spatial model called node_transition model and performs “spatio-temporal predictions” of the picker’s movement while the picker is in a particular state. The Discrete Event Simulation (DES) framework, a proven agricultural multi-robot logistics model, is used to simulate the different picking operation scenarios with and without our proposed prediction framework and the results are then statistically compared to each other. Our prediction framework can reduce the so-called unproductive logistics time in a fully manual harvesting process by about 80 percent in the overall picking operation. This research also indicates that the different rates of picking operations involve different numbers of sub-states, and these sub-states are associated with different trends considered in spatio-temporal predictions.

Assessment of Emissions and Energy Consumption for Construction Machinery in Earthwork Activities by Incorporating Real-World Measurement and Discrete-Event Simulation

Earthwork, an essential activity in most construction projects, consumes large quantities of fossil fuel and produces substantial air pollution with adverse environmental impacts. To achieve more sustainable construction processes, novel methodologies to evaluate and improve the performance of earthwork operations are required. This study quantifies the real-world emissions and fuel consumption of construction equipment within an earthwork project in China. Two wheel loaders and two dump trucks are examined through on-board measurements and in-lab engine tests. The duty cycles of construction equipment are categorized with respect to their power efficiency and working patterns. Moreover, the power-specific and time-based emission factors for these duty cycles are computed and compared with relevant legislative emission limits. Significant emission variations among different duty cycles were found, and the real-world emission measurements exceeded the results from the in-lab test required for emission certification. In addition, a discrete-event simulation (DES) framework was developed, validated, and integrated with the computed emission factors to analyze the environmental and energy impacts of the earthwork project. Furthermore, the equipment fleet schedule was optimized in the DES framework to reduce greenhouse gas emissions and fuel consumption by 8.1% and 6.6%, respectively.