Discrete Event Simulation Techniques Research Articles

A Study on Analysis of the Operational Risks Associated with the Manufacturing Plant & the Roles of Managers in Reducing the Risks

The goal of risk management for large corporations is identifying and effectively minimizing operational risk. This research focuses on the influence of internal and external factors on operational risk in manufacturing sites. Scholars, as well as practitioners, have recognized the importance of operational risk in the recent past. I also mentioned the operational risk approaches to measuring the risk of the manufacturing firms. This concept is further extended by considering the risk flow to evaluate operational risk. The application of the risk flow concept is discussed in the context of a case study carried out in a consumer products company. I applied Monte Carlo and discrete-event simulation techniques to develop the model. The simulation results are then compared across the experiments to see how the change in specific parameters led to a change in the level of operational risk exposure for this Company.

An effective strategy for stacking and reclaiming iron ore piles

ABSTRACT This study examines the impact of the size of homogenised ore stockpiles on the feed rate of concentration-ore plants and the inter-pile grade variability. An integrated simulation optimisation tool is used to incorporate the dynamic characteristics of this process. Applying the discrete event simulation technique in this study made it possible to analyse the losses due to the lack of homogenised piles in the ore yard. Three scenarios from a Brazilian mine were explored. The results report a production gain of 3.85% in the scenario with the smallest ore pile size compared to the scenario with the largest pile size.

Discovering supply chain operation towards sustainability using machine learning and DES techniques: a case study in Vietnam seafood

PurposeTo achieve the Sustainable Development Goals (SDGs) in the era of Logistics 4.0, machine learning (ML) techniques and simulations have emerged as highly optimized tools. This study examines the operational dynamics of a supply chain (SC) in Vietnam as a case study utilizing an ML simulation approach.Design/methodology/approachA robust fuel consumption estimation model is constructed by leveraging multiple linear regression (MLR) and artificial neural network (ANN). Subsequently, the proposed model is seamlessly integrated into a cutting-edge SC simulation framework.FindingsThis paper provides valuable insights and actionable recommendations, empowering SC practitioners to optimize operational efficiencies and fostering an avenue for further scholarly investigations and advancements in this field.Originality/valueThis study introduces a novel approach assessing sustainable SC performance by utilizing both traditional regression and ML models to estimate transportation costs, which are then inputted into the discrete event simulation (DES) model.

Capacity Utilization Assessment Using Discrete-Event Simulation: Case Study of a High-Density Crowd Visiting the Holy Rawda in the City of Madinah

Managing a high-density crowd is challenging, especially when space is limited, due to increased risk of crowd disasters. There are many considerations to lower disaster risk and ensure crowd safety such as infrastructure, facility, staff, emergency response plan, education and awareness. This paper investigates capacity management, which is the most critical consideration when space is limited. Discrete-event simulation (DES) technique is highly adept at reproducing complex real-world operations and can incorporate numerous stochastic variables, thereby improving its effectiveness in decision-making. The objective of this study is to assess capacity utilization of small space through formulating space features and crowd dynamic with a DES model. The model is intended to provide support for informed decision-making process related to capacity management. A real case study about a high-density crowd visiting the Holy Rawda was employed. The data related to the visiting parameters and visit experience KPIs were captured through a range of interviews and actual visits. Comparing the results of the simulation scenarios against the proposed KPIs had a surprising result, there is only 79% utilization of the area, although, intensive high-density crowd were featured during all the visits. It means better management of the crowd may allow 21% more visitors to visit the Holy Rawda.

Patient flow modeling and simulation to study HAI incidence in an Emergency Department

Healthcare-associated infections (HAIs), or nosocomial infections, refer to patients getting new infections while getting treatment for an existing condition in a healthcare facility. HAI poses a significant challenge in healthcare delivery that results in higher rates of mortality and morbidity as well as a longer duration of hospital stay. While the real cause of HAI in a hospital varies widely and in most cases untraceable, it is popularly believed that patient flow in a hospital—which hospital units patients visit and where they spend the most time since their admission into the hospital—can trace back to HAI incidence in the hospital. Based on this observation, we, in this paper, model and simulate patient flow in an emergency department of a hospital and then utilize the developed model to study HAI incidence therein. We obtain (a) a flowchart of patient movement (admission to discharge) and (b) anonymous patient data from University Health Medical Center for a duration of 11 months (Aug 2022–June 2023). Based on these data, we develop and validate the patient flow model. Our model captures patient movement in different areas of a typical emergency department, such as triage, waiting room, and minor procedure rooms. We employ the discrete-event simulation (DES) technique to model patient flow and associated HAI infections using the simulation software, Anylogic. Our simulation results show that the rates of HAI incidence are proportional to both the specific areas patients occupy and the duration of their stay. By utilizing our model, hospital administrators and infection control teams can implement targeted strategies to reduce the incidence of HAI and enhance patient safety, ultimately leading to improved healthcare outcomes and more efficient resource allocation.

Optimizing Emergency Department Resource Allocation Using Discrete Event Simulation and Machine Learning Techniques

Background: Optimizing resource allocation in emergency departments (ED) is challenging due to limited resources and high costs. Objectives: The objective of this study was to utilize data mining algorithms and simulation modeling to predict the length of stay (LOS) of patients and compare scenarios for increasing bed productivity. Methods: Data mining algorithms, including Random Forest (RF) regression and CatBoost (CB) regression models, were used to predict the LOS based on patient demographic information and vital signs. The process of admission to discharge in the ED was simulated, and different scenarios were compared to identify strategies for increasing bed productivity. Results: The combination of RF regression and CB regression models performed better than other methods in predicting the LOS of patients. Simulation modeling demonstrated that optimal resource allocation and increased bed productivity could be achieved using predicted LOS values. Conclusions: This study demonstrates that a combined approach of data mining and simulation can effectively manage ED resources and reduce congestion. The findings highlight the potential of advanced analytical techniques for improving healthcare service delivery and patient outcomes.

Discrete‐event simulation is still alive and strong: evidence from bibliometric performance evaluation of research during COVID‐19 global health pandemic

AbstractDuring the Annual Operational Research Society Simulation Workshop in Worchester, UK, in 2010, a panel discussion entitled “discrete‐event simulation (DES) is dead, long live agent‐based simulation (ABS)!” was held. Subsequent debates on the topic have captured the interest of many simulation professionals and researchers. This study lends credence to the discourse with evidence. In January 2020, a global health crisis named coronavirus disease (COVID‐19) occurred, infecting and killing millions of people globally, leading to community lockdowns and travel restrictions, and bringing the world to a standstill. Scientists scrambled for quick, practical solutions to stem the tide. Operational research (OR) and management science (MS) techniques, including modeling and simulation, provide effective methods to make COVID‐19 infection forecasts, assign/optimize intensive care unit beds, initiate healthcare management/control strategies, and decision‐making. The global pandemic problems offer an opportunity to evaluate the discourse on DES's continuous relevance and potency as an OR/MS technique. Utilizing bibliometric performance evaluation and text analytics of publications addressing various COVID‐19 pandemic problems, the results show an extensive use, relevance, and impact of the DES technique across multidisciplinary domains, including medical/healthcare, social and behavioral sciences, business, molecular biosciences, and more. The intellectual structure highlights strong multidisciplinary collaboration among sources in decision sciences, healthcare, natural, behavioral, and social sciences. The social network analysis of the publications shows the contributions and collaborations among authors, institutions, and countries. The study demonstrates that DES is still alive and robust, notwithstanding the hype about ABS.

Prediction of Ore Production in a Limestone Underground Mine by Combining Machine Learning and Discrete Event Simulation Techniques

This study proposes a novel approach for enhancing the productivity of mining haulage systems by developing a hybrid model that combines machine learning (ML) and discrete event simulation (DES) techniques to predict ore production. This study utilized time data collected from a limestone underground mine using tablet computers and Bluetooth beacons for 15 weeks. The collected data were used to train an ML model to predict truck cycle time, and the support vector regression with particle swarm optimization (PSO–SVM) model demonstrated the best performance. The PSO–SVM model accurately predicted cycle time with a mean absolute error (MAE) of 2.79 min, mean squared error (MSE) of 14.29 min2, root mean square error (RMSE) of 3.79 min, and coefficient of determination (R2) of 0.68. The output of the ML model was linked to the DES model to predict ore production for each truck, section, and time period. Verification of the DES model demonstrated its ability to accurately simulate the haulage system in the study area by comparing production logs with the simulation results. This study’s novel approach offers a new method for predicting ore production and determining the optimal equipment combination for each workplace, thus enhancing productivity in mining haulage systems.

Generalised Performance Estimation in Novel Hybrid MPC Architectures: Modeling the CONWIP Flow-Shop System

The ability to supply increasingly individualized market demand in a short period of time while maintaining costs to a bare minimum might be considered a vital factor for industrialized countries’ competitive revival. Despite significant advances in the field of Industry 4.0, there is still an open gap in the literature regarding advanced methodologies for production planning and control. Among different production and control approaches, hybrid architectures are gaining huge interest in the literature. For such architectures to operate at their best, reliable models for performance prediction of the supervised production system are required. In an effort to advance the development of hybrid architecture, this paper develops a model able to predict the performance of the controlled system when it is structured as a controlled work-in-progress (CONWIP) flow-shop with generalized stochastic processing times. To achieve this, we employed a simulation tool using both discrete-event and agent-based simulation techniques, which was then utilized to generate data for training a deep learning neural network. This network was proposed for estimating the throughput of a balanced system, together with a normalization method to generalize the approach. The results showed that the developed estimation tool outperforms the best-known approximated mathematical models while allowing one-shot training of the network. Finally, the paper develops preliminary insights about generalized performance estimation for unbalanced lines.

Modeling Collaborative Behaviors in Energy Ecosystems

The notions of a collaborative virtual power plant ecosystem (CVPP-E) and a cognitive household digital twin (CHDT) have been proposed as contributions to the efficient organization and management of households within renewable energy communities (RECs). CHDTs can be modeled as software agents that are designed to possess some cognitive capabilities, enabling them to make autonomous decisions on behalf of their human owners based on the value system of their physical twin. Due to their cognitive and decision-making capabilities, these agents can exhibit some behavioral attributes, such as engaging in diverse collaborative actions aimed at achieving some common goals. These behavioral attributes can be directed to the promotion of sustainable energy consumption in the ecosystem. Along this line, this work demonstrates various collaborative practices that include: (1) collaborative roles played by the CVPP manager such as (a) opportunity seeking and goal formulation, (b) goal proposition/invitation to form a coalition or virtual organization, and (c) formation and dissolution of coalitions; and (2) collaborative roles played by CHDTs which include (a) acceptance or decline of an invitation based on (i) delegation/non-delegation and (ii) value system compatibility/non-compatibility, and (b) the sharing of common resources. This study adopts a simulation technique that involves the integration of multiple simulation methods such as system dynamics, agent-based, and discrete event simulation techniques in a single simulation environment. The outcome of this study confirms the potential of adding cognitive capabilities to CHDTs and further shows that these agents could exhibit certain collaborative attributes, enabling them to become suitable as rational decision-making agents in households.

Evaluation of the overall effectiveness (OEE) of autonomous transportation system (AHS) equipment and its impact on mine design. Open pit mine case study

In recent years, the increasing demand for autonomous vehicles in the mining industry has led to a greater focus on integrating these solutions with mine design. As technology advances, mining companies are looking to maximize the benefits of automation in terms of efficiency, productivity, and safety. The adoption of autonomous vehicles in mine design allows for the optimization of planning and operation of mining activities. Autonomous equipment can work continuously, reducing downtime and improving resource utilization. This has a direct impact on the overall equipment effectiveness (OEE), as availability and performance are significantly enhanced.In this research, the overall equipment effectiveness (OEE) of an Autonomous Haulage System (AHS) and its impact on the design of an open-pit mine were evaluated at the engineering level in Chile. Additionally, a representative month of operation (January 2034) was modeled using discrete event simulation technique with current operation and AHS philosophies. The results of this model, which were validated and calibrated based on historical information, meetings with different work groups, and understanding of the engineering level of the study, indicate that the OEE of manual operation is 14.4% lower than that of AHS operation, demonstrating an opportunity for the implementation of this technology.

Modelling the operations of a circular economy fashion start-up

This paper presents a case study of Pantala, a fashion start-up, intending to operationalise a circular supply of luxury womenswear through a monthly subscription-fee model. To optimize the company's cashflow and ensure success, the operational flows were modelled and analysed through discrete-event simulation techniques, with a focus on inventory management and minimizing operational costs. Simulation results demonstrated a high level of accuracy in predicting system performance, with an average relative error of less than 5% for most performance indicators. Insights are provided into the benefits of using simulation modelling for operational planning and optimization of complex systems, especially in the case of start-ups.

Stochastic Simulation Modelling and Analysis of Out-Patient flow: A Case Study of General Hospital Hadejia, North-western Nigeria

Increasing demand for health care coupled with resource constraints may lead to overcrowding in health care center. Generally, patient flow into health care center is characterized by variability due to its stochastic behavior. In recent years, there has been increasing interest in Discrete Event Simulation (DES) technique in healthcare systems due to its sensitivity to systems’ variability. Discrete Event Simulation (DES) technique is used in this study to examine patient flow in an urban health care center, the General Hospital Hadejia (GHH), with a view to analyzing resource utilization, minimizing waiting time, and improving efficiency and effectiveness. Data relating to waiting time, service time, and movements of patients between departments within the health center were collected and analyzed. The influence of operational policies on the efficient movement of patients during peak and off-peak demand periods is studied. Results of the discrete event simulation experiment show an average increase of 41% of patients’ visits to the hospital when the existing layout of the hospital is modified.

A hybrid model to support decision making in the stroke clinical pathway

Health processes are highly complex and sensitive, requiring continued improvement. Several methodologies have been developed to support these improvement processes, many of which apply Process Mining (PM) to investigate the current states of processes. Some even use Discrete Event Simulation (DES) techniques to explore alternative scenarios. Among the ones using DES, some rely on Multicriteria Decision-Making Methods (MCDM) to guide them in choosing the best scenario, especially in complex decision-making environments. However, no author presents a single framework combining all three techniques mentioned above, to wit, PM, DES, and MCDM. This paper targets developing this combination and exploring its usefulness in guiding process improvement in the health services area. The clinical path for stroke patients, covering from onset of symptoms to hospital discharge, was used as a test case. The research demonstrated that this proposal provided an innovative diagnostic of the process, identifying the activities that should be improved to obtain the most significant results in the process management indicators. Identifying the activities ensures that the impacts of these improvements are indicated with statistical accuracy and eliminating the need to perform on-site testing to test for the best solutions. The research does not address root cause diagnosis of activities to be improved. Therefore, further research needs to be undertaken on an extension of the model to enhance root cause analysis capabilities with a view to validating the framework proposed in other environments and, in this way, assess its replicability.

Quantification of Particulate Emission from Construction Activities Using Discrete-Event Simulation

Construction activities generate a significant number of airborne pollutants. Particulate matter (PM) is one of the important emissions arising throughout construction, especially in large-scale projects due to the utilization of heavy-duty equipment. Nevertheless, there is no competent approach that is capable of quantifying PM emissions from construction equipment operations at the pre-planning stage. Therefore, the development of a sufficiently accurate and simple quantification model is pressingly needed as the first step to help construction planners in identifying potential mitigation opportunities toward sustainable construction. This paper introduces a PM emission estimation model in an integrated application with a discrete-event simulation (DES) technique, namely EZStrobe. The model is developed with the aim of improving the current body of knowledge in the aspects of simplicity and practicality to address the restriction of input data availability in the initial phase of construction projects. A case study of a real highway project in Thailand is used to demonstrate the use and performance of the developed model in evaluating the environmental impacts in terms of diesel exhaust emissions. The findings identify critical construction activities and factors potentially affecting the environment. This approach enables construction planners to obtain a holistic, sustainable viewpoint in efficiently managing existing construction resources.

Agent-based modelling in capacitated lot sizing problem with sequence dependent setup time

Setups are indispensable in production, but consume substantial amount of productive time. It is vital to consider sequence dependent setup times for determining production lots to satisfy demand for diversified product types on parallel production lines. Optimisation is the most used technique to generate detailed schedules for such sequencing problems. But the feasibility of the solution is not guaranteed under uncertainty conditions. Thus, evaluating the solution configurations under uncertainty is necessary to confirm feasibility. In this paper, an agent-based, discrete event simulation technique is used to develop a flow-shop model which faces demand for multiple product varieties, and has sequence dependent setup time. The simulation aims at evaluating various results in the solution space of an optimisation model to check their feasibility under various uncertain conditions in the form of setup time, processing time, and demand. Based on the results obtained from this optimisation-simulation model, it was observed that uncertainty influences cost parameters such as overtime cost, holding cost, and lost sales.

Integration of discrete event simulation with other modeling techniques to simulate construction engineering and management: an overview

Although Discrete Event Simulation (DES) has been the preferred simulation technique in construction operation studies, it suffers from limitations, such as narrowed focus at the operational level. To minimize the effect of DES limitations, researchers have proposed the integration of DES with other simulation techniques, such as agent-based modeling (ABM), system dynamics (SD), and virtual environments (VE). However, limited studies have discussed whether this integration process minimizes DES’ limitations and to what extent. This study summarizes 99 journal manuscripts in the existing literature published between 2010-2020, focusing on integrating DES with ABM, SD, and VE. This study found that the integration of DES with ABM, SD, and VE addressed multiple of DES’ limitations, namely, the lack of human behaviors in process-oriented modeling, the limited strategic perspective, and challenges related to the verification and validation of DES models’ outputs. Ultimately, this study calls for future studies to evaluate the simultaneous integration of DES, ABM, and SD modeling techniques so the complexity of construction projects can be truly accounted for, as comprehensive simulation tools will require the integration of multiple methods to counterbalance their limitations.

Application of Discrete Event Simulation in Estimating Productivity of Shotcrete Method in Divider Wall Construction

Discrete-event simulation (DES) techniques are widely used in modeling complex environments where interaction between time-dependent resources and system processes occur in sequences, such as manufacturing ecosystems or production lifecycles, providing an alternative to traditional work measurement. In this study, labor productivity of using shotcrete process in constructing wall dividers for a simple construction project is estimated using a discrete-event simulation (DES) software incorporating project data on process durations, setup time, and machine mean time to failure (MTTF) as model inputs. The simulated productivity rate of using shotcrete to construct a 1000-sq.m. wall in replications of 200, 500, and 1000 is 1.59 man-hours/sq.m., compared to computed rate of 1.33 man-hours/sq.m. using the Program Evaluation and Review Technique (PERT). The total waiting time with respect to the simulation time considering setup, process-in-waiting, and downtime is 48.9% for a single 8-hour shift per workday arrangement. It is shown that by doing multiple 8-hour shifts the resource utilization of the shotcrete equipment can be improved by an average of 43.8% per additional shift bringing the total waiting time to 9.21% for a continuous 8-hour shifts in a single workday. The methodology aims to provide a better baseline estimate of productivity as it takes in to account historical data as well as waiting time arising from resource limitations.

Resources Allocation in Service Planning Using Discrete-Event Simulation

Objective: Calculate the required personnel and resources needed to fulfill the service promise agreed with the customer. Methods and materials: This paper presents a discrete event simulation (DES) model developed to select and implement a Point of Sale (POS) for a company providing financial products. First, the paper shows the characterization of the system components and times per process. Then, hypothesis testing and goodness-of-fit statistics are estimated. Subsequently, the simulation scenarios assess the times between arrivals and the number of commercial advisers. Results and discussion: This model allows us to assess the allocation of resources to fulfill the service promise, which is that 80 % of customers must be served within one hour or less. This paper provided the service isoquants allowing us to observe the behavior of the performance metrics (service promise fulfillment) among different scenarios. Conclusions: The use of DES techniques allows for the evaluation of the assignment of personnel to achieve the fulfillment of the service promise, including facilities, equipment, and the evaluation of related processes. These methods can be extended to the analysis of resource allocation in the development of other processes, observing the relationship between service quality and operating costs.

Productivity Improvement of BOB T-shirt through Line Balancing Using Control Limit analysis and discrete event simulation (Case study: - MAA Garment and Textile Factory)

This study deals with line balancing of BOB T-shirt model with the help of control limit analysis and discrete event simulation of the assembly lines. In this study control limit analysis is used to measure the performance of the assembly line and used to show the bottleneck operations of the assembly line and line balancing technique improves the productivity of the sewing line of the model. BOB T-shirt model has 16 main operations and each operation’s time is analyzed as standard minute value (SMV). The main bottleneck operations are analyzed using the control limit analysis and simulation modeling. Based on the SMV of each operation, those operations which are out of lower control limit and upper control limit is called us bottlenecks of the sewing lines of the garment section and the 1st bottleneck operation is tread trimming operation. When we apply control limit analysis and discrete event simulation technique for the line balancing; the daily output has been increased from 1032 pieces to 1289 pieces. And labor productivity and machine productivity are increased from 46.9 and 54.32 to 58.59 and 71.61 respectively. And then finally, the profit that the line generated also increased from 22704 to 28358birr.