Discrete Event Simulation Method Research Articles

Analysis and Modeling of Queuing System Simulation in Payment Process at Minimarket (Case Study of Minimarket X Yogyakarta)

The purpose of this study is to analyze the queuing system at Minimarket X so as to minimize the number of queues. The method in this writing uses a discrete event simulation method with the help of Anylogic software. Simulation is used as a method to analyze problems that exist in the payment system at the Minimarket, while Anylogic software as an illustration that can facilitate the queuing system so that it is clearer and easier to understand. Observations conducted for 20 days show that the queuing system applied by Minimarket X is Multi Channel – Single Phase and First Come First Served (FCFS) or First In First Out (FIFO) with 4 cashier officers (M/M/4). Each cashier is tasked with serving customers and processing the products purchased by scanning them and packing each grocery. The average number of Minimarket visitors every day is 22 customers per hour with a queue length of 10 to 11 customers per hour, and the average waiting time for customers to be served is around 27 to 30 minutes. The simulation used through anylogic software obtained error results within 67 minutes 43 seconds.

Strand Inventory Analysis of Post Tension in PT XYZ Using Discrete-Event Simulation Method

The development of connectivity infrastructure, particularly toll roads, is planned to continue expanding and reach 3,500 km by 2030. This signifies a positive outlook for the construction industry. This research conducted to analyse the management of post tension raw materials and determine the optimal policies using the discrete-event simulation method. Simulation scenarios were developed using the Arena application, taking into consideration the demand patterns for post tension work. The validation test results confirmed the capability of the base case model in accurately representing real-world conditions. Scenario development was carried out to influence the strand inventory pattern by incorporating reorder points and adjusting order sizes. Based on the scenario analysis, the most effective approach was found to be setting a reorder point of 3 tons and an order size of 5 tons. The implementation of this analysis successfully reduced the strand inventory by 58.78%. This research enables companies to optimize their inventory, reduce costs, and enhance operational efficiency. The findings contribute to the development of strategies that support sustainable growth in connectivity infrastructure in Indonesia.

Evaluating the supply chain resilience strategies using discrete event simulation and hybrid multi-criteria decision-making (case study: natural stone industry)

ABSTRACT One of the challenges that organisations face is the risk reduction through creating resilient supply chains. There are different strategies to reach the resiliency. In this paper, we analysed five important strategies: redundancy, backup supplier, reserved capacity, reserved inventory, and increasing work-shifts. These supply chain resilience (SCR) strategies are evaluated using discrete event simulation and hybrid multi-criteria decision-making methods. For this end, Bayesian best-worst method (BWM) and Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) were used for weighting the SCR evaluation criteria and ranking the SCR strategies, respectively. Six discrete event simulation models were generated and run for evaluating each strategy in order to achieve quantitative criteria. On the other hand, the ideas of experts were used for weighting qualitative criteria and also ranking the alternatives. Results showed that the increasing work-shift strategy has the first rank and the reserved capacity strategy has the lowest rank.

Simulation Modeling and Optimization of Traffic Flows in Local Sections of the Street Road Network Using AnyLogic

An approach to simulation modeling and optimization of traffic flows in local sections of the street road network using AnyLogic and heuristic algorithms is presented. A feature of this approach is the use of agent-based (ABM) and discrete-event simulation methods supported in the AnyLogic system. An original swarm optimization algorithm (PSO) is proposed, modified for the problem of optimizing road traffic on a real section of the road (near the Yugo-Zapadnaya metro station in Moscow). Numerical experiments were performed to confirm the possibility of optimizing the duration of traffic light phases in order to minimize the average travel time of vehicles on a simulated section of the street road network.

Using simulation modelling to transform hospital planning and management to address health inequalities

Health inequalities are a perennial concern for policymakers and in service delivery to ensure fair and equitable access and outcomes. As health inequalities are socially influenced by employment, income, and education, this impacts healthcare services among socio-economically disadvantaged groups, making it a pertinent area for investigation in seeking to promote equitable access. Researchers widely acknowledge that health equity is a multi-faceted problem requiring approaches to understand the complexity and interconnections in hospital planning as a precursor to healthcare delivery. Operations research offers the potential to develop analytical models and frameworks to aid in complex decision-making that has both a strategic and operational function in problem-solving. This paper develops a simulation-based modelling framework (SimulEQUITY) to model the complexities in addressing health inequalities at a hospital level. The model encompasses an entire hospital operation (including inpatient, outpatient, and emergency department services) using the discrete-event simulation method to simulate the behaviour and performance of real-world systems, processes, or organisations. The paper makes a sustained contribution to knowledge by challenging the existing population-level planning approaches in healthcare that often overlook individual patient needs, especially within disadvantaged groups. By holistically modelling an entire hospital, socio-economic variations in patients' pathways are developed by incorporating individual patient attributes and variables. This innovative framework facilitates the exploration of diverse scenarios, from processes to resources and environmental factors, enabling key decision-makers to evaluate what intervention strategies to adopt as well as the likely scenarios for future patterns of healthcare inequality. The paper outlines the decision-support toolkit developed and the practical application of the SimulEQUITY model through to implementation within a hospital in the UK. This moves hospital management and strategic planning to a more dynamic position where a software-based approach, incorporating complexity, is implicit in the modelling rather than simplification and generalisation arising from the use of population-based models.

Multi-stage and multi-objective design optimization for improving resilience of base-isolated hospital buildings

The hospital is a critical infrastructure for treating the injured after the earthquake. The hospital is a complex system composed of structure, non-structural components, and medical equipment that are located on different floors, have different importance and seismic performance, and need different control objectives, enhancing any component could improve the hospital's functionality. It is imperative to economically and rationally choose enhancement measures to minimize initial functionality losses and improve hospital resilience. This study provides a hospital functional evaluation method that takes into account the physical dimension of the damage to the structure, non-structural components and medical equipment, as well as the organizational dimension of the treatment process of earthquake casualties. Bayesian networks and discrete event simulation methods are used to calculate the availability probability of medical rooms and the functional indicator of the number of earthquake casualties that can be treated. Then, a two-stage and multi-objective functionality improvement method was proposed to improve hospital functionality. The first stage is the design optimization of the isolation system with the objectives of minimizing the functionality loss and displacement of the isolation layer. The second stage optimizing the enhancement strategies for non-structural components and medical equipment aims at minimizing the functionality loss and enhancement budget. The proposed method uses a fast and elitist non-dominated sorting genetic algorithm (NSGA-II) approach to optimize the design of the isolation system and the enhancement of non-structural components and medical equipment. The method is applied to a hypothetical hospital to highlight the proposed method's capability of improving the hospital's functionality.

Discrete event simulation for dynamic thermal modelling of district heating pipe

Optimizing district heating (DH) systems in a holistic manner is often impeded by the computational complexities associated with network modeling. This study introduces a novel, efficient and theoretically accurate method for dynamic thermal modelling of DH pipes. The approach is to track water frontiers traveling along the pipe using discrete event simulation (DES) paradigm. As the DES method is based on variable time steps, the computational effort is significantly reduced compared to earlier methods. The proposed model can compute outlet water temperature, temperature profile along the pipe, and heat loss based on variable inlet temperature and flow rate. The DES model was validated by comparison with real measurements of a long DH pipe. Four variants of the model with different temperature profile assumptions and interpolation methods were compared. Numerical results show that the DES model can accurately predict outlet water temperature with a maximum discrepancy of 0.52 °C. The mean error of simulated outlet temperature was −0.01 ± 0.02 °C. Average computation time for 24-h simulation was 59 μs. Overall, this study shows that the DES method is appropriate for variable time step simulation for DH pipe, potentially, for network simulation. Our study may also inspire variable time step implementation in other energy applications.

Integration of Discrete-Event Simulation and Statistical Process Control Methods

This study aimed to integrate the discrete-event simulation system with the statistical processcontrol method to test whether the discrete-event simulation system is under control using the discreteevent simulation method. This study created a three-dimensional discrete-event simulation model of theemergency service unit. Using the data of patient stay (or length of stay) and waiting times obtained in thesimulation model, analyses were made with Xbar-R and Xbar-S control charts in statistical process controlmethods. Determining which values of the simulation data are out of control was carried out with statisticalprocess control charts. For Xbar-R and Xbar-S graphics, 901 data were analyzed, with 30 subgroups. Thisstudy determined that the patient was under control according to Xbar-R and Xbar-S control charts ofpatient stay and waiting times, but the system was out of control according to R and S control charts. As aresult, it has been determined that the system in the 3D simulation model is out of control according to thestatistical process control charts. It is recommended to take the system under control by observing the datathat is out of control. This study emphasized that the results obtained from the systems performed in thecomputer environment should be integrated with statistical process control charts to verify their validity.

Research on the effect of number of yard trucks on container terminal throughput

Container terminal productivity is a benchmark for the level of effectiveness of a container port. One indicator of container terminal productivity level can be calculated through yard throughput. Several factors affect yard throughput, one of which is the number of truck terminals. This study aims to investigate the effect of the number of truck terminals on yard throughput at a container terminal. The investigation was conducted using a container terminal simulation which was based on the discrete event simulation method. As a case study, a simulation of a container terminal with a dock length of 300 m with several quay cranes was two units. The simulation was done by parameters adding the number of truck terminals and variations in the capacity of ships coming. The results of the simulation show that an increase in the number of truck terminals affects the number of outputs, depending on the size of the loading and unloading vessels. From the simulation results obtained, the largest yard throughput capacity of 809 containers with the number of truck terminals is 8 units on a shipload of 1,000 twenty-foot equivalent units. The results of this study provide an overview of the needs of the truck terminal in the container terminal. Highlights The yard throughput of the container terminal has been investigated by a simulation model built based on a discrete event simulation The discrete event simulation has obtained the effect of increasing the number of truck terminals on the yard throughputs with container capacity variations from 200 - 1,000 TEUs The increase in speed at the truck terminal is insignificant to increase yard throughput and necessary to do integrated scheduling between the truck terminals, quay cranes, and gantry cranes The increase in the quay crane operation in container terminals will decrease the dwell time of container throughput

Simulating and optimizing precast wall lifting in prefabricated building construction

PurposePrecast wall lifting during prefabricated building construction faces multiple non-lean problems, such as inaccurate lifting-time estimation, unreasonable resource allocation and improper process design. This study aims to identify the pathways for improving lifting performance to advance lean construction of prefabricated buildings.Design/methodology/approachThis study developed a methodological framework that integrates the discrete event simulation method, the elimination, combination, rearrangement and simplification (ECRS) technique and intelligent optimization tool. Two schemes of precast wall lifting, namely, the enterprise's business as usual (BAU) and enterprise-leading (EL) schemes, were set to benchmark lifting performance. Furthermore, a best-practice (BP) scheme was modeled from the perspective of lifting activity ECRS and resource allocation for performance optimization.FindingsA real project was selected to test the effect of the methodological framework. The results showed that compared with the EL scheme, the BP scheme reduced the total lifting time (TLT) by 6.3% and mitigated the TLT uncertainty (the gap between the maximum and minimum time values) by 20.6%. Under the BP scheme, increasing the resource inputs produces an insignificant effect in reducing TLT, i.e. increasing the number of component operators in the caulking subprocess from one to two only shortened the TLT by 3.6%, and no further time reduction was achieved as more component operators were added.Originality/valueTo solve non-lean problems associated with prefabricated building construction, this study provides a methodological framework that can separate a typical precast wall lifting process into fine-level activities. Besides, it also identifies the pathways (including the learning effect mitigation, labor and machinery resource adjustment and activities’ improvement) to reducing TLT and its uncertainty.

Modeling Cyberattack Propagation and Impacts on Cyber-Physical System Safety: An Experiment

In this article, we present an experiment we conducted with discrete event simulations to analyze the effects of multi-step cyberattacks on the safety of cyber-physical systems. We show how to represent systems, their components (either software and/or hardware), communication links, security measures, and attacks from a malicious intruder. The latter are typically taken from the MITRE ATT&CK knowledge base. The discrete event simulation method makes it possible to represent any event affecting the system. We illustrate our approach by means of an illustrative example involving cyberattacks against the navigation system of an autonomous ship. We show how the formal modeling language AltaRica, primarily dedicated to safety analyses, can assess this illustrative example by representing the system and automatically extracting sequences of attacks, leading to a safety-critical situation, namely the deviation of the ship by the attacker. This article aims to discuss this approach and to outline the lessons learned from our experience.

Performance simulation analysis of new train control system safety communication protocol based on Matlab/SimEvents

The vehicle-ground safety communication of the New Train Control System adopts a brand-new underlying network and a communication mechanism of safe and reliable transmission. Based on the GPRS packet switching network, the data link layer adopts the PPP protocol, and the TCP protocol is adopted in the transmission layer to provide reliable transmission services. According to the GPRS network service quality index and the retransmission mechanism of the TCP protocol in the transport layer, this paper adopts the Matlab/SimEvents discrete event simulation method and uses the entity as the communication carrier to conduct a hierarchical and modular system-level modeling of New Train Control System vehicle-ground communication protocol. It simulates the communication process of the message from the application layer, the security layer to the transport layer, and finally transmitted on the GPRS network channel with random delay and packet loss probability. From the quantitative point of view, this paper analyzes the safety connection establishment time and success rate of the new train control system vehicle-ground wireless communication under different channel conditions, the timeliness of the non-periodic message receiving a reply to complete the specific scene function, and the reliability of the periodic message receiving delay. The analysis results show that the new train control system vehicle-ground wireless communication adopts GPRS network and TCP protocol retransmission mechanism to improve the real-time and reliability of communication.

HLA-based time management and synchronization framework for lean manufacturing tools evaluation

Discrete event simulation (DES) is a method for digitally replicating the behavior and performance of real-world processes, systems, and facilities. DES is widely applied in manufacturing, logistics, healthcare, and military domains. In some cases, DES method is insufficient. On one hand, the simulation must be disassembled into subsystems and then distributed on a multiprocessing environment to enhance the simulation performance. On the other hand, to expand current functionality and prevent future application development, a set of interacting simulations is required. In this project, the IEEE high-level architecture (HLA) standard mechanisms are adopted to solve the interoperability problems between heterogeneous components. Time synchronization between federates is essential to have all DESs running in parallel. In this paper, we present a distributed simulation framework designed to assist decision-makers in making the best decisions and prioritizing the adoption of Lean tools and techniques.

Simulation optimization in healthcare resource planning: A literature review

In healthcare, the planning and the management of resources are challenging as there are always many complex and stochastic factors in both demand and supply. Simulation Optimization (SO) that combines simulation analysis and optimization techniques is well suited for solving complicated, stochastic, and mathematically intractable decision problems. In order to comprehensively unveil the degree to which SO has been used to solve healthcare resource planning problems, this article reviews the academic articles published until 2021 and categorizes them into multiple classification fields that are related to either problem perspectives (i.e., healthcare services, planning decisions, and objectives) or methodology perspectives (i.e., SO approaches and applications). We also examine the relations between the individual fields. We find that emergency care services are the most applied domain of SO, and that discrete-event simulation and random search methods (especially genetic algorithms) are the most frequently used methods. The literature classification can help researchers quickly learn this research area and identify the publications of interest. Finally, we identify major trends, insights and conclusions that deserve special attention when studying this area. We suggest many avenues for further research that provide opportunities for expanding existing methodologies and for narrowing the gap between theory and practice.

Evolutionary digital twin model with an agent-based discrete-event simulation method

Evolutionary digital twin model with an agent-based discrete-event simulation method

A cost analysis with the discrete-event simulation application in nurse and doctor employment management.

This study aimed to analyse the treatment cost of a patient, depending on the number of patients treated, patient waiting times, and the number of nurses and doctors employed in an emergency department of a private hospital. Within health systems, changes in health care resources can be very costly, especially if these changes are long-term. The discrete-event simulation method described in this paper allows for the monitoring and analysis of complicated changes in real systems by using computer-based modelling. The discrete event simulation model was derived from nine scenarios according to the number of nurses and doctors, and a comparison was made between the results of the scenarios and the actual results. Among the scenarios, scenario 6 provided the lowest treatment cost for a patient by employing three doctors and two nurses with the best performance. The cost of treatment for a patient varies between ŧ9.00 and ŧ11.00 depending on the value of δ, and the daily cost of these resources to the hospital is ŧ1300.77. This study provides a clear picture of a cost analysis comparison based on changes made about the actual health system in the computer-based simulated environment. The workforce data of nurses and doctors offers enough detail for cost analysis in health care settings to calculate the cost of treatment for a patient.

Multi-method simulation modelling of circular manufacturing systems for enhanced decision-making

Circular manufacturing systems (CMS) constitute complex value networks comprising a large and diverse set of stakeholders that collaborate to close the loop of products through multiple lifecycles. Complex systems modelling and simulation play a crucial role in providing quantitative and qualitative insights into the behaviour of such systems. In particular, multi-method simulation modelling that combines agent-based, discrete-event, and system dynamics simulation methods is considered more suitable to model and simulate CMS as it allows to capture their complex and dynamic nature. This paper provides a step-by-step approach on how to build a CMS multi-method simulation model in order to assess their economic, environmental, and technical performance for enhanced decision-making. To model and simulate CMS three main elements need to be considered:•A multi-method model architecture where the CMS stakeholders with heterogeneous characteristics are modelled individually as autonomous agents using agent-based, discrete-event, and system dynamics.•An agent environment defined by a Geographic Information System (GIS) to establish connections based on agents’ geographic location.•The product journey resulting from the product's interaction with various CMS stakeholders in the circular value network is traced throughout its multiple lifecycles.

Incorporation of human factors into a Discrete Event Simulation Model for human centred assembly performance evaluation


 
 
 Discrete Event Simulation (DES) tool is commonly used for the design, analysis, and evaluation of manufacturing systems. Human centred assembly systems offer better system flexibility and responsiveness due to inherent human intelligence and problem-solving abilities; human can deal with product variations and production volumes; and can always adapt themselves to multiple tasks after learning process. Nevertheless, human performance can be unpredictable, and may alter over time due to varying psychological and physiological states, these are often overlooked by researchers when designing, implementing, or evaluating a manufacturing system. In this paper a user-friendly integrated DES method was proposed to enable manufacturing system designers to investigate overall performance of human centred system considering effects of selected human factors. the method can permit manufacturing system designers to evaluate overall manufacturing system performance with considerations of parameters of human factors at early design stage. A case study was carried out using integrated approach; simulation results demonstrate the applicability of this approach.
 
 

A discrete event simulation method for performance analysis of an additive manufacturing in the dental clinic.

Three-dimensional printing (3DP) is an evolutionary solution for making customize items for all sectors, but it has become more prominent in the healthcare sector. In this field, some solutions have to be adapted to patients. This is especially true for dentistry, where all the patients have their own unique mouth and tooth structure. It is now possible to provide an accurate model of the patient's mouth and teeth with solutions that are perfectly compatible with them, leading to the provision of a dental service with a high success rate. Even if there is a problem, it is enough to change the three-dimensional design. Therefore, it is a time-saving method, too. The purpose of this study is to investigate the role of 3DP in dentistry and to identify the processes and procedures resulting from the use of this technology. To do so, with the help of a case study, a 3DP-based dental clinic that provides implant, orthodontics, restoration and dentures services is simulated in Arena software. The current state of the system is assessed by defining appropriate evaluation criteria including net profit, utilization, waiting time, patients makespan and laboratory makespan. The simulation model is then developed with innovations such as adding an inventory control policy, creating rest time for resources and controlling the policy of sending products from laboratory to the clinic. After an extensive sensitivity analysis, improving the performance of the system is on the agenda of this paper by examining various scenarios. Results show that scenarios such as reducing some resources of the system or considering rest time in exchange for increasing the duration of the work shift can have a significant impact on clinic performance.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00170-021-08135-7.

Optimizing planning and design of COVID-19 drive-through mass vaccination clinics by simulation.

Drive-through clinics have previously been utilized in vaccination efforts and are now being more widely adopted for COVID-19 vaccination in different parts of the world by offering many advantages including utilizing existing infrastructure, large daily throughput and enforcing social distancing by default. Successful, effective, and efficient drive-through facilities require a suitable site and keen focus on layout and process design. To demonstrate the role that high fidelity computer simulation can play in planning and design of drive-through mass vaccination clinics, we used multiple integrated discrete event simulation (DES) and agent-based modelling methods. This method using AnyLogic simulation software to aid in planning, design, and implementation of one of the largest and most successful early COVID-19 mass vaccination clinics operated by UCHealth in Denver, Colorado. Simulations proved to be helpful in aiding the optimization of UCHealth drive through mass vaccination clinic design and operations by exposing potential bottlenecks, overflows, and queueing, and clarifying the necessary number of supporting staff. Simulation results informed the target number of vaccinations and necessary processing times for different drive through station set ups and clinic formats. We found that modern simulation tools with advanced visual and analytical capabilities to be very useful for effective planning, design, and operations management of mass vaccination facilities.