Discrete Event-based Simulation Research Articles

Improving Effectiveness of the Safety Service Patrol Programs: A Discrete Event-Based Simulation Approach

Safety service patrols (SSPs) play an important role in incident management on highways. It is critical to respond to incidents in a timely manner as this can significantly reduce nonrecurrent congestion and improve safety. Therefore, it is essential to allocate available SSP vehicles to highway segments such that their effectiveness is maximized. This study aimed to develop a simulation-based framework to assist with SSP service optimization. More specifically, a discrete event-based simulation tool (i.e., SSP-OPT) with customizable parameters was developed to help plan the optimum patrol routes based on available SSP resources and predicted incidents. The developed tool was tested with roadway traffic and incident data from the Virginia highway network. After model calibration, the simulation results showed that the developed SSP-OPT tool could replicate the patrol routes with similar performance to the field observations, validating the tool. Further, adopting the tool for corridor-level optimization could help to identify the best patrol plan to minimize SSP response time and maximize SSP response rates for a given number of SSP vehicles. The SSP-OPT tool requires minimal user input (e.g., segment lengths, annual average daily traffic) and has the flexibility to be easily applied to any highway corridor once calibrated. The tool generates various performance metrics to enable more informed decision making in SSP route planning.

Preliminary investigation of the sea-rail intermodal system's efficiency using a simulation approach: case of the Port of Trois-Rivieres

Sea-rail intermodal transportation around the globe faces complex challenges that affect the satisfaction of shippers' needs. An efficient cargo flow between the port and its hinterland depends particularly on efficient connectivity between the seaport and rail. Sea-rail intermodal can be a cost-efficient and green alternative to unimodal road transportation. Inefficient sea-rail connectivity in the seaport slows cargo flow and affects port capacity. Various factors could affect the system's efficiency and create bottlenecks in the system. A case study adopts a discrete event-based simulation approach to assess bottlenecks in the sea-rail connection that affect cargo flow and generate congestion. The data were collected from the Port of Trois-Rivières, the focus of our investigation. Our objective is to identify bottlenecks in the sea-rail intermodal system in the port, identify strategies to mitigate bottlenecks and accelerate cargo flow. To this end, we examined various scenarios, including an increase in the share of trains for cargo transportation and an increase in the number of daily train convoys. The findings underscore that elevating the train share to 40% and introducing two daily train services yield significant enhancements in key performance indicators. Noteworthy advantages encompass a reduction in the average time ships spend in the port, a decrease in the average waiting time for trains to depart from the port, an overall improvement in cargo handling efficiency within the port, and a notable alleviation of bottlenecks within the system.

Selecting Efficient and Reliable Preservation Strategies:

This article addresses the problem of formulating efficient and reliable operational preservation policies that ensure bit-level information integrity over long periods, and in the presence of a diverse range of real-world technical, legal, organizational, and economic threats. We develop a systematic, quantitative prediction framework that combines formal modelling, discrete-event-based simulation, hierarchical modelling, and then use empirically calibrated sensitivity analysis to identify effective strategies. Specifically, the framework formally defines an objective function for preservation that maps a set of preservation policies and a risk profile to a set of preservation costs, and an expected collection loss distribution. In this framework, a curator’s objective is to select optimal policies that minimize expected loss subject to budget constraints. To estimate preservation loss under different policy conditions optimal policies, we develop a statistical hierarchical risk model that includes four sources of risk: the storage hardware; the physical environment; the curating institution; and the global environment. We then employ a general discrete event-based simulation framework to evaluate the expected loss and the cost of employing varying preservation strategies under specific parameterization of risks. Source code is available at:https://github.com/MIT-Informatics/PreservationSimulation The framework offers flexibility for the modeling of a wide range of preservation policies and threats. Since this framework is open source and easily deployed in a cloud computing environment, it can be used to produce analysis based on independent estimates of scenario-specific costs, reliability, and risk. We present results summarizing hundreds of thousands of simulations using this framework. This exploratory analysis points to a number of robust and broadly applicable preservation strategies, provides novel insights into specific preservation tactics, and provides evidence that challenges received wisdom. An earlier version of this paper was published previously in IJDC 15(1) 2020

Application of microscopic discrete event-based simulation in the modelling of the city logistics systems of concentrated sets of delivery locations

ABSTRACT Nowadays, city logistics issues are getting more and more attention. Within this, our City Logistics Research Group is currently focusing on the urban concentrated sets of delivery locations. MS Excel- and AnyLogic-based simulation models were developed in the last years to analyze them, but still, problems were encountered above a certain model size in both cases. For this reason, we started looking for a new tool that is flexible, fast, and available for everyone. The use of Python Programming Language was selected, with the primary goal of eliminating the previous technical problems and obtaining detailed information about the examined processes. In this paper, we are going to present the problems of the previous models, the development, operation, and main advantages of the new, Python-based DES model, the design and parameterization of the pilot models, the results of the tests, and the next steps of the research.

Bi-Objective Modeling and Optimization for Stochastic Two-Stage Open Shop Scheduling Problems in the Sharing Economy

Nowadays, many manufacturing and service industries prefer to share resources such as facilities and workers to cooperatively perform tasks, which can efficiently improve resource utilization and customer satisfaction. Generally, the decision-makers need to pay more for resource usage, leading to an urgent demand to decrease operational costs. This article proposes a stochastic bi-objective two-stage open shop scheduling problem that models a vehicle maintenance process where tasks are appointed to be completed by multiple third-party companies with professional equipment. We formulate this optimization problem by minimizing the total tardiness and processing cost subject to various resource constraints. A hybrid multiobjective migrating birds optimization combined with a genetic operation and a discrete event system is designed by considering problem characteristics to solve the problem. In this method, the migrating birds optimization with some particular strategies aims at searching candidate solutions from the entire solution domain. Simultaneously, the discrete event system, by using stochastic simulation and discrete event-based simulation approaches, focuses on evaluating the performance of searched solutions. Simulation experiments are performed, and state-of-the-art algorithms are used as competitive approaches. The results confirm that this approach has an excellent performance in handling our considered problem.

A Simulation-Based Model for Evacuation Demand Estimation under Unconventional Metro Emergencies

When metro networks are shut down due to an unconventional emergency, numerous passengers will get stranded and wait to be evacuated. A clear understanding of the stranded passengers in the network is the basis for designing an efficient evacuation strategy. Previous studies focused more on disruption management and assumed a known evacuation demand. Few studies considered the distribution of stranded passengers, especially when the metro network is shut down due to unconventional emergencies. This motivates us to develop a discrete event-based simulation model to estimate the number and distribution of stranded passengers. The proposed model takes the numbers of stranded passengers at stations and trains as the state variables and develops the state evolution rule of the metro system. The numbers and destinations of stranded passengers at trains and on stations at each time step, including the transfer and non-transfer stations, are calculated to describe the state evolution of the metro system. Chongqing Rail Transit (CRT) network is taken as an example. The result shows that the stations with the most stranded passengers are located downtown. Origin-Destinations (ODs) with more stranded passengers have a relatively shorter travel distance, while ODs with fewer stranded passengers have a relatively long travel distance. The average directional disequilibrium factor of stranded passengers of ODs in the whole network is high, especially for those ODs with many stranded passengers. These findings reveal the distribution characteristics of stranded passengers and can provide significant assistance to an emergency manager who is designing an efficient emergency evacuation plan.

Determination of the Number of Required Charging Stations on a German Motorway Based on Real Traffic Data and Discrete Event-Based Simulation

Abstract To make travelling with electric vehicles (EVs) over long distances as convenient as travelling with traditional vehicles, charging stations along motorways are necessary. Furthermore, waiting times for free charging points must be short to enable a fast onward journey, and this also on days with heavy traffic volumes. To determine the required number of charging stations in more detail, a model was created that simulates the process of arriving and leaving cars at a charging park based on real traffic data. For the traffic data, a location and date in the Munich region were chosen that represent a peak demand and thus a “worst case” scenario. The ability to cover such scenarios as well seems to be important because otherwise severe congestion with long waiting times would appear on days with heavy traffic, which would make the use of EVs very unattractive. It turned out that 150 to 600 charging stations – depending on the considered scenario – would be necessary to charge proportions of 10% to 20% of all passing cars.

Quantitative benefits of the digital product passport and data sharing in remanufacturing

As a part of the circular economy – which aims at closing the loop of materials – remanufacturing is getting more and more attention in recent years. With remanufacturing, companies are carrying out a recovery operation of used products with the aim of rebuilding a like-new condition. As various members of the supply chain are involved in this process, and product data needed for optimal operation is often missing, remanufacturing processes can become uncertain and hard to optimize. The goal of a new European Commission initiative, the digital product passport (DPP), is to facilitate information exchange between stakeholders, in order to reduce uncertainties. DPP, assigned to a product, could contain information about e.g., its origin, composition, repair and dismantling possibilities. In the paper, the essence of DPP is introduced, and a case study is presented to show the qualitative benefits of the increased amount of information about the product. A discrete event-based simulation model of three real production lines that are (re)manufacturing cylinder heads is presented. Experiments are also conducted, where quantitative benefits of using DPP are highlighted, such as decreasing unnecessary production time, increasing ratio of remanufactured parts and increasing output.

Experimental Analysis of a Stochastic Backward Simulation Approach Under the Specifics of Semiconductor Manufacturing

Manufacturing companies are experiencing many challenges with regard to customer-oriented and on-time production, especially in the context of an intensified global business and while advancing the digital transformation. Accordingly, the ongoing development and deployment of Industry 4.0 solutions for customisable products in small batch sizes, not only pose new problems for work preparation, but also for operative production planning in connection with high cost, time and quality pressure. Modern, complex and highly automated production systems (in this case from the semiconductor domain) must be operated close to an optimal operating state in order to be economically reasonable. Promised delivery dates and throughput times specified in contracted service agreements must be ensured and require a permanent and effective adjustment of production planning and control in daily execution. All other general conditions of an economic production remain unchanged. Today, the question of whether the production target is realistic and whether all promised delivery dates are met are today still answered with rather simple backward-oriented approaches, mostly without taking into account uncertainties, stochastic behaviour of the manufacturing system or alternatives that arise during operation. As shown in previous publications, these questions can be answered in more detail and more resilient using a backward-oriented discrete event-based simulation approach (SimBack). This article presents additional findings. The results show and deepen the impression, that the SimBack-approach can be successfully solve scheduling questions for customer-specific orders in a real-world environment.

Improving efficiency of material flows in an automotive assembly plant: A case study

This paper investigates the in-plant logistics processes in automotive manufacturing and specifically, the material flows between the storage facility, the production lines, and the internal returns area. The aim is to improve the efficiency of the plant by looking at the current material flows of the case study company. Qualitative and quantitative data was collected through interviews and personal observations and was analysed. Using discrete event-based simulation, the current material flows were investigated. Our analysis showed that in plant’s internal returns area efficiency gains are possible by reducing traffic congestion. A solution with minor changes in the layout was proposed and tested which showed an increase in the capacity of the system and a reduction in delays.

Hybrid Model-Based Simulation Analysis on the Effects of Social Distancing Policy of the COVID-19 Epidemic.

This study utilizes modeling and simulation to analyze coronavirus (COVID-19) infection trends depending on government policies. Two modeling requirements are considered for infection simulation: (1) the implementation of social distancing policies and (2) the representation of population movements. To this end, we propose an extended infection model to combine analytical models with discrete event-based simulation models in a hybrid form. Simulation parameters for social distancing policies are identified and embedded in the analytical models. Administrative districts are modeled as a fundamental simulation agent, which facilitates representing the population movements between the cities. The proposed infection model utilizes real-world data regarding suspected, infected, recovered, and deceased people in South Korea. As an application, we simulate the COVID-19 epidemic in South Korea. We use real-world data for 160 days, containing meaningful days that begin the distancing policy and adjust the distancing policy to the next stage. We expect that the proposed work plays a principal role in analyzing how social distancing effectively affects virus prevention and provides a simulation environment for the biochemical field.

An exemplary definition of a vertidrome's airside concept of operations

In many cities the (public) transportation system already faces challenges to adjust appropriately to the growing population. Lifting the transportation offer into the third dimension by introducing Urban Air Mobility (UAM) may be a promising step towards an improvement. But what characteristics does UAM need to offer in order to represent successfully an extension to our traditional (public) transportation system and to fit into the concept of Mobility-as-a-Service (MaaS) where various modes of transportation are bundled and accessible on demand? On the one hand, the supporting ground infrastructure specialized in accommodating UAM and vertical take-off and landing (VTOL) operations, in this research named vertidrome, and on the other hand a consortium of stakeholders (vehicle operator, vertidrome operator, air traffic management, passenger, city councils, etc.) will determine a considerable portion of these requirements. The vertidrome is a UAM traffic junction, which merges airside and landside operations and interactions between the vertidrome infrastructure components, the vehicles and the passengers. This research work focuses on the exemplary development of a vertidromes airside operation considering a UAM application at the Munich trade fair (Germany). Following this objective, we analyze the unique characteristics of UAM, review the current state of the art regarding UAM ground infrastructure designs and introduce a new and VTOL-specific terminology vertidrome. In addition, we define an exemplary vertidrome concept of operations: a linear and expandable drive-through (LIEDT) topology, distinct approach and departure mission profiles based on the VTOL vehicle Volocopter 2X, as well as operating (airside air) and traffic (airside ground) rules for arriving and departing vehicles. The results will contribute to the development of a corresponding discrete event-based simulation (DES) depicting the elaborated vertidrome's airside structure and operations followed by the development of a performance assessment method evaluating a vertidrome's airside traffic flow (Vertidrome Airside Level of Service - VALoS).

A simulation approach towards a sustainable and efficient container terminal layout design

ABSTRACT Seaports are considered gateways for international maritime trade. The flow of trade and the size of containerships have been growing exponentially in the last two decades, and as a result, the exchange of containers has increased dramatically. In consequence, adapting container terminal designs is required in order to confront these new challenges more efficiently and with improved sustainability. This paper studies the effect of changing the layout of seaport terminals by taking into consideration both costs and emissions. A new design of a container terminal located in the Port of Montreal is proposed and compared with the current layout. A discrete event-based simulation approach is adopted to investigate the impact of terminal layout on economic and environmental performance. This study aims to improve the performance and handling capacity of a terminal by recommending a new layout. Computational experiments were conducted to evaluate and compare the performance of both layouts using data collected from the Port of Montreal. The results indicate that terminal layout design has a significant impact on terminal performance and emission under the configuration of different transportation modes.

Using PRRSV-Resilient Sows Improve Performance in Endemic Infected Farms with Recurrent Outbreaks.

Simple SummaryPorcine reproductive and respiratory syndrome (PRRS) is a viral disease responsible for huge economic losses to the pig industry. The selection of PRRSV resilient sows has been proposed as a strategy to control this disease. A simulation model was developed to test the differences in reproductive performance and economic outcome of resilient or susceptible sows under farm PRRSV endemic conditions with or without recurrent PRRSV outbreaks. The data from phenotyped sows came from a PRRSV-positive farm with 1500 sows that suffered a PRRSV outbreak that lasted 24 weeks within three years. The reproductive parameters were generally better for resilient than for susceptible sows in PRRSV-positive farms suffering recurrent PRRSV outbreaks. Consequently, the piglet production cost was lower for resilient than for susceptible sows in any condition but showed only significant differences in PRRSV endemic farms suffering recurrent outbreaks. Finally, the annual gross margin by sow is significantly better for resilient than for susceptible sows under endemic conditions with or without recurrent outbreaks. Thus, the selection of PRRSV resilient sows is always a profitable approach for producers supporting the control of this disease.The selection of porcine reproductive and respiratory syndrome (PRRS) resilient sows has been proposed as a strategy to control this disease. A discrete event-based simulation model was developed to mimic the outcome of farms with resilient or susceptible sows suffering recurrent PRRSV outbreaks. Records of both phenotypes were registered in a PRRSV-positive farm of 1500 sows during three years. The information was split in the whole period of observation to include a PRRSV outbreak that lasted 24 weeks (endemic/epidemic or En/Ep) or only the endemic phase (En). Twenty simulations were modeled for each farm: Resilient/En, Resilient/En_Ep, Susceptible/En, and Susceptible/En_Ep during twelve years and analyzed for the productive performance and economic outcome, using reference values. The reproductive parameters were generally better for resilient than for susceptible sows in the PRRSV En/Ep scenario, and the contrary was observed in the endemic case. The piglet production cost was always lower for resilient than for susceptible sows but showed only significant differences in the PRRSV En/Ep scenario. Finally, the annual gross margin by sow is significantly better for resilient than for susceptible sows for the PRRSV endemic (12%) and endemic/epidemic scenarios (17%). Thus, the selection of PRRSV resilient sows is a profitable approach for producers to improve disease control.

Analysis of an assembly line by mixed integer programming and discrete event-based simulation

Since the appearance of computers, engineers recognized their potential. Due to this fact, engineers are utilizing computer usage during their daily tasks. When it comes to analysis, simulation is one of the most valuable tools an engineer can apply. In this paper a mixed integer programming-based description and a discrete event-based model is presented. The mathematical approach is used for rough checking, while the more detailed event-based simulation is used to examine the effects of certain parameters of the assembly cell. Based on the results, the bottleneck of the line and the possibly over-capacitated buffers were identified. Furthermore, a worker regrouping strategy is determined with the help of the models.

Modelling elevator traffic with social distancing in a university classroom building

Social distancing standards implemented during the COVID-19 pandemic may have negative effects on vertical traffic. We describe a model and use it to predict the elevator traffic under social distancing in a university classroom building, and study the effects of four interventions aimed at improving this traffic. Discrete event-based simulation is used to study whether the lift group meets the forecasted demand when the car capacity is restricted far below its ordinary value to accommodate social distancing. Four low-cost interventions are simulated alone and in combination to quantify the improvements they offer. All four interventions show some improvement, and the combination of all four interventions gives the greatest improvement. Practical application: Implementing social distancing standards may disproportionately lower the car capacity relative to the building population and thus negatively affect vertical traffic. Building managers seeking to implement low-cost measures to improve elevator traffic under these conditions may look to the percentage improvements described here to aid them in selecting interventions appropriate to their buildings.

Selecting Efficient and Reliable Preservation Strategies

This article addresses the problem of formulating efficient and reliable operational preservation policies that ensure bit-level information integrity over long periods, and in the presence of a diverse range of real-world technical, legal, organizational, and economic threats. We develop a systematic, quantitative prediction framework that combines formal modeling, discrete-event-based simulation, hierarchical modeling, and then use empirically calibrated sensitivity analysis to identify effective strategies.
 Specifically, the framework formally defines an objective function for preservation that maps a set of preservation policies and a risk profile to a set of preservation costs, and an expected collection loss distribution. In this framework, a curator’s objective is to select optimal policies that minimize expected loss subject to budget constraints. To estimate preservation loss under different policy conditions optimal policies, we develop a statistical hierarchical risk model that includes four sources of risk: the storage hardware; the physical environment; the curating institution; and the global environment. We then employ a general discrete event-based simulation framework to evaluate the expected loss and the cost of employing varying preservation strategies under specific parameterization of risks.
 The framework offers flexibility for the modeling of a wide range of preservation policies and threats. Since this framework is open source and easily deployed in a cloud computing environment, it can be used to produce analysis based on independent estimates of scenario-specific costs, reliability, and risks.
 We present results summarizing hundreds of thousands of simulations using this framework. This exploratory analysis points to a number of robust and broadly applicable preservation strategies, provides novel insights into specific preservation tactics, and provides evidence that challenges received wisdom.

The impacts of shared autonomous vehicles on car parking space

It is anticipated that Shared Autonomous Vehicles (SAV) may revolutionise the way people travel as it may lead to less ownership of vehicles and a gradual increase in demand responsive mobility. These vehicles are likely to able to park far away from popular destinations after they drop off their clients. It is, therefore, capable of releasing parking spaces around central business districts while cutting down parking search time and cost. But the magnitude at which these spaces will be released in relation to SAV fleet size is not currently known. This work therefore seeks to understand this relationship using the University of the West of England, Frenchay campus characterised by 23 carparks of varying size – as a case study – to quantitatively understand these impacts. This was done by simulating 2181 parking slot within the campus; motor traffic (SAVs and non-SAVs); and the route networks from the 3 entry stations – East, North and Longmead entrances – to each parking slot. Carpark preference data was obtained and analysed. It formed the context on which drivers choose a parking space within the system. These data alongside with the graphical features of the Frenchay campus were imported into ARENA simulation toolkit: a discrete event-based simulation software. Results indicated the obvious that as SAV fleet size increases, spaces released also increases but in addition, individual carpark reacted dynamically in response to the demand on neighbouring carparks. It also showed that there is currently an excess of parking supply compared to the number of vehicles entering the campus daily. It further discusses the potential of multiple occupancy SAVs to further release all parking spaces on the Frenchay campus. The work concludes with the potentials for this research to be extended to a citywide approach with a special interest in the ability for SAV to release kerb parking spaces.

Efficient discrete-event based particle tracking simulation for high energy physics

This work presents novel discrete event-based simulation algorithms based on the Quantized State System (QSS) numerical methods. QSS provides attractive features for particle transportation processes, in particular a very efficient handling of discontinuities in the simulation of continuous systems. We focus on High Energy Physics (HEP) particle tracking applications that typically rely on discrete time-based methods, and study the advantages of adopting a discrete event-based numerical approach that resolves efficiently the crossing of geometry boundaries by a traveling particle. For this purpose we follow two complementary strategies. First, a new co-simulation technique connects the Geant4 simulation toolkit with a standalone QSS solver. Second, a new native QSS numerical stepper is embedded into Geant4. We compare both approaches against the latest Geant4 default steppers in different HEP setups, including a complex realistic scenario (the CMS particle detector at CERN). Our techniques achieve relevant simulation speedups in a wide range of scenarios, particularly when the intensity of discrete-event handling dominates performance in the solving of the continuous laws of particle motion.

Evaluation of Actual Timetables and Utilization Levels of West Midlands Metro Using Event-Based Simulations

The performance of the West Midlands Metro in the United Kingdom is analyzed in the present study by evaluating the existing timetables of the metro system. Using SIMUL8 computer software, a discrete event-based simulation prototype modeling the metro system is developed and implemented. The model adequately describes the performance of the West Midlands Metro system. By running simulations, the overall utilization level of the metro system is calculated. The results of the simulation model indicate that the metro system is being underutilized. The low utilization rates indicate a potential for the introduction of new services capable of exploiting the existing infrastructure and improving the utilization levels of the existing metro system; For example, the potential of using the current metro system for urban freight transport could be a new service of interest and provide scope for further research.