Discrete Event System Simulation Research Articles

Predicting weight dispersion in seabass aquaculture using Discrete Event System simulation and Machine Learning modeling

Marine aquaculture, particularly in the Mediterranean region, faces the challenge of minimizing growth dispersion, which has a direct impact on the production cycle, market value and sustainability of the sector. Conventional grading methods are resource intensive and potentially detrimental to fish health. The current study presented an innovative approach in predicting fish weight dispersion in European seabass (Dicentrarchus labrax) aquaculture. Seabass is one of the two major fish species cultivated on the Mediterranean coast, with a fattening cycle of 18–24 months. During this period, several grading operations are carried out to minimize growth dispersion. The intricate feed-fish-water system, characterized by complex interactions among feeding regimes, fish behavior, individual metabolism and environmental factors, is the focus of the study. The comprehensive, five-step methodology addresses this complexity. The process begins with a Discrete Event System (DES) model that simulates the feed-fish-water dynamics, taking into account individual fish metabolism. This is followed by the development of a surrogate machine learning (ML) regressor model, which is trained on DES simulation data to efficiently predict growth distribution. The model is then calibrated and customized for specific fish stocks and production tanks. The preliminary results from 21 tanks in two trials with European seabass (D. labrax) showed the effectiveness of the method. The results from the simulation models achieved a R2 of 99.9 % and a Mean Absolute Percentage Error (MAPE) of 1.1 % for the prediction of mean final weight and a R2 of 90.3 % with a MAPE of 8.1 % for the standard deviation of final weight. In summary, this study represents a significant advance in the planning and management of seabass aquaculture. Given the lack of effective prediction tools in the aquaculture industry, the proposed methodology has the potential to reduce risks and inefficiencies, thus possibly optimizing aquaculture practices by increasing sustainability and profitability.

Model Sharing and Scalability in the Real-Time Simulation and Intelligent Hierarchical Control of Discrete-Event Systems

Model Sharing and Scalability in the Real-Time Simulation and Intelligent Hierarchical Control of Discrete-Event Systems

Program Evaluation and Review Technique (PERT) Analysis to Predict Completion Time and Project Risk Using Discrete Event System Simulation Method

The prediction of project completion time, which is important in project management, is only based on an estimate of three numbers, namely the fastest, slowest, and presumably time. The common practice of applying normal distribution through Monte Carlo simulation in Program Evaluation and Review Technique (PERT) research often fails to accurately represent project activity durations, leading to potentially biased project completion prediction. Based on these problems, a different method is proposed, namely, Discrete Event Simulation (DES). The research aims to evaluate the effectiveness of the simmer package in R in conducting PERT analysis. Specifically, there are three objectives in the research: 1) develop a simulation model to predict how long a project will take and find the critical path, 2) create an R script to simulate discrete events on a PERT network, and 3) explore the simulation output using the simmer package in the form of summary statistics and estimation of project risk. Then, a library research with a descriptive and exploratory method is used for data collection. The hypothetical network is used to obtain the numerical results, which provide the predicted value of the project completion, the critical path, and the risk level. Simulation, including 100 replications, results in a predicted project completion time and a standard deviation of 20.7 and 2.2 weeks, respectively. The DES method has been proven highly effective in predicting the completion time of a project described by the PERT network. In addition, it offers increased flexibility.

Integrated remanufacturing scheduling of disassembly, reprocessing and reassembly considering energy efficiency and stochasticity through group teaching optimization and simulation approaches

The energy crisis and environmental pollution are receiving increasing attention from governments and communities. This study researches energy-aware remanufacturing systems. Remanufacturing aims to reuse valuable resources from end-of-life products and produce as-new products. Since remanufacturing systems involve a series of disassembly, processing and assembly operations, remanufacturing schedule integrates disassembly, processing and assembly shops. A multi-objective scheduling of remanufacturing systems is proposed, considering workstation use, energy consumption and customer satisfaction simultaneously. A chance-constrained programming model is established to minimize makespan and energy consumption while satisfying total tardiness requirements. A hybrid method is developed, using group teaching optimization and a discrete event simulation system, which can seek and evaluate potentially favourable solutions. The approach is validated on a group of test instances using well-known methods. The results reveal that this method can find non-dominated solutions with well-converged and well-diversified performance, verifying its advantages in providing informed decisions for managers and engineers.

Improving Warehouse Layout Effectiveness and Process Picking Efficiency with the Discrete Event System Simulation Approach

Field conditions show process picking inefficiencies caused by the layout and arrangement of products in the warehouse that are not optimal. The objective of this research is to estimate the actual system performance and propose a new design so that the system becomes more effective and efficient. The research methodology combines Discrete-Event System (DES) Simulation to estimate the performance of the system and Double ABC Analysis to determine the re-arrangement of the item's position based on the pickup frequency and outer volume of the item. The statistical results identified the moving time has a significant difference between the actual and proposed layout. This study concludes that the re-arrangement of items can reduce the picking time of operators in the operational warehouse process.

Leveraging Auto-generative Simulation for Decision Support in Engineer-to-Order Manufacturing

In the sphere of Engineer-to-Order (ETO) manufacturing, the demand for highly customized and rapidly adaptable solutions is escalating, particularly in the context of Industry 4.0 and the burgeoning focus on hyper-customization in Industry 5.0. Traditional, rigid simulation structures often fall short in adapting to these dynamic requirements, creating a need for more flexible simulation frameworks. This paper introduces a novel auto-generative Discrete Event Simulation (DES) system specifically designed to address the unique challenges of ETO manufacturing. Unlike traditional rigid simulation architectures, the proposed system offers substantial flexibility and modularity, significantly easing the development effort. Developed in MATLAB and Simulink, this paper not only presents the foundational logic and architecture of the system but also provides a practical implementation guide. By aligning with the imperatives of Industry 4.0 and 5.0, this research fills a significant gap in the literature and offers a scalable, adaptable simulation-based Decision Support System (DSS) for ETO manufacturing.

Delivery service order policy with the sharing economy concept using a discrete event simulation system

People's lifestyle, especially in shopping, has shifted from offline shopping, such as in supermarkets, traditional markets, stalls, and so on, to online shopping. Therefore, online business (e-commerce) has increased, resulting in a surge in the business potential of freight forwarder companies. When there is an increase in demand for the delivery of goods by sellers to consumers, companies need to make adjustments to improve their performance. This paper proposed the sequence of goods delivery services for XYZ companies by considering dynamic requests and conditions that vary discretely over time. This paper is based on a case company in Bandung, Indonesia. The method employed queuing models and discrete event simulations using hypothetical data with performance criteria to minimize the total cost of shipping goods. Simulations are carried out using one courier and one zone to compare customer service determination algorithms, namely first-come, first-served, proximity, and predictive control models. The simulation results show that the proximity algorithm produces a minimum total cost of Rp 1,785,749, the smallest cost compared to using first-come, first-served and predictive control models, respectively IDR 2,782,389, and IDR 2,639,291. Then, the Annova test was conducted, which provided information that one policy differed significantly from another, and a Turkey test was carried out, showing that the proximity algorithm produces better performance than other algorithms. The contribution of this paper is to present that the delivery service employed by the company provided a minimum total cost.

Discrete Event Simulation modelling as an improvement validation tool on Printed Circuit Board Mechanical Assembly process

Although the application of Discrete Event Simulation (DES) as a validating tool may seem counterproductive, routinely reviewing an improvement that has been installed is crucial for industries to decide whether a process is optimal, requires further refinement, or must be discontinued and replaced with a better alternative. The objective of this paper is to demonstrate DES as a decision-making tool by validating the improvements of a mechanical assembly line for Printed Circuit Board Assembly (PCBA) manufacturing. As a case study, parameters connected to Overall Equipment Effectiveness (OEE) namely availability, performance, and quality were gathered from a PCBA mechanical assembly line in Company A that has shifted their primary assembly tools from conventional electrical torque screwdrivers into programmable torque screwdrivers. Through the data collection step, it was observed that the OEE improved upon implementing a programmable torque screwdriver. The assembly lines were then modelled in the DES system and information pertaining to OEE was gathered and analyzed to aid in validating the enhancement in the manufacturing line. Based on the simulation, the results obtained confirm that the OEE improves through the implementation of a programmable torque screwdriver, signifying that DES is a suitable tool to help validate an improvement in a process.

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.

Имитационное моделирование систем массового обслуживания на основе составных распределений – вероятностных смесей

This article presents the results obtained on the development of software pseudo-random sequence generators for QS simulation in the GPSS WORLD discrete-event simulation system with hyper-Erlang (HE2) and hyper-exponential (H2) input distributions. Data in this subject area, neither in foreign nor in domestic scientific literature, were found by the authors. There are no such generators in the GPSS WORLD library either. It is known that the distributions of HE2 and H2 are the most general and provide a large range of the coefficient of variation. The latter play an important role in estimating the delay of requests in the queue in queuing systems, since the average queue delay is directly proportional to their squares. For the distributions of HE2 and H2, the authors previously obtained numerical-analytical results based on the method of spectral solution of the Lindley integral equation. The article presents the obtained algorithms and programs on GPSS WORLD to simulate the functioning of the QS with the specified input distributions. The adequacy of the obtained results was confirmed by comparing the simulation results with the results of numerical simulation in the Mathcad environment. The authors hope that the presented results will be in demand by specialists in the field of simulation modeling in the GPSS WORLD environment.

XDEVS: A toolkit for interoperable modeling and simulation of formal discrete event systems

AbstractEmploying Modeling and Simulation (M&S) extensively to analyze and develop complex systems is the norm today. The use of robust M&S formalisms and rigorous methodologies is essential to deal with complexity. Among them, the Discrete Event System Specification (DEVS) provides a solid framework for modeling structural, behavior and information aspects of any complex system. This gives several advantages to analyze and design complex systems: completeness, verifiability, extensibility, and maintainability. DEVS formalism has been implemented in many programming languages and executable on multiple platforms. In this paper, we describe the features of an M&S framework called xDEVS that builds upon the prevalent DEVS Application Programming Interface (API) for both modeling and simulation layers, promoting interoperability between the existing platform‐specific (C++, Java, Python) DEVS implementations. Additionally, the framework can simulate the same model using sequential, parallel, or distributed architectures. The M&S engine has been reinforced with several strategies to improve performance, as well as tools to perform model analysis and verification. Finally, xDEVS also facilitates systems engineers to apply the vision of model‐based systems engineering (MBSE), model‐driven engineering (MDE), and model‐driven systems engineering (MDSE) paradigms. We highlight the features of the proposed xDEVS framework with multiple examples and case studies illustrating the rigor and diversity of application domains it can support.

Improved event-driven simulation method for fuel transport in a mesh-like pipeline network

One of the tasks of an oil company is to fulfil site demands. Since not all sites have production, it is essential to transport products between sites. The order of pumping operations is determined by the scheduling plan. The scheduling plan includes the type and quantity of products to be delivered, the start of deliveries, and routes. The type and quantity of products to be delivered, the start of deliveries and routes are included in the scheduling plan. A scheduling plan is feasible if it does not contain inconsistencies. Checking a one-month schedule is a complex task in real multi-source mesh-like pipeline networks, thus it is advisable to use a discrete event simulation system. This work presents improvements on our previous simulation model. In the improved simulation model, it is possible to reroute, split, and reverse the flow direction of the products in the pipe, amongst other things.

Analysis of a closed-loop digital twin using discrete event simulation

Given recent advancements in technology and recognizing the evolution of smart manufacturing, the implementation of digital twins for factories and processes is becoming more common and more useful. Additionally, expansion in connectivity, growth in data storage, and the implementation of the industrial internet of things (IIoT) allow for greater opportunities not only with digital twins but with closed loop analytics. Discrete event simulation (DES) has been used to create digital twins and, in some instances, fitted with live connections to closely monitor factory operations. However, the benefits of a connected digital twin are not easily quantified. Therefore, a test bed demonstration factory was used, which implements smart technologies, to evaluate the effectiveness of a closed-loop digital twin in identifying and reacting to trends in production. This involves a digital twin of a factory process using DES. Although traditional DES is typically modeled using historical data, a DES system was developed which made use of live data to improve predictions. This model had live data updated directly to the DES model without user interaction, creating an adaptive and dynamic model. It was found that this DES with live data typically provided more accurate predictions of future performance and unforeseen near future problems when compared to the predictions of a traditional DES using only historic data, resulting in smarter decisions and implementation of more timely solutions.

An improved EMS simulation-optimization model with Poisson mixture distribution

A Discrete Event System (DES) simulation and optimization model is proposed for the ambulance allocation problem in an Emergency Medical Service (EMS) system. The accuracy of the proposed model was improved by estimation of input parameters using the Poisson mixture model and the Expectation Maximization Algorithm. The results were proven by Mann–Whitney and Kolmogorov–Smirnov tests. The DES model was run by the OptQuest program embedded in Arena software. The response times of the cases within the urban and rural regions were improved by 40% and 45%, respectively, and the station success rates in urban and rural areas were raised to 95% and 96%, respectively. Our results indicated that the number of ambulances must be revised, and their allocations to stations in the real system need to be reorganized to achieve the required EMS system performance standards.

The DEVStone Metric: Performance Analysis of DEVS Simulation Engines

The DEVStone benchmark allows us to evaluate the performance of discrete-event simulators based on the Discrete Event System (DEVS) formalism. It provides model sets with different characteristics, enabling the analysis of specific issues of simulation engines. However, this heterogeneity hinders the comparison of the results among studies, as the results obtained on each research work depend on the chosen subset of DEVStone models. We define the DEVStone metric based on the DEVStone synthetic benchmark and provide a mechanism for specifying objective ratings for DEVS-based simulators. This metric corresponds to the average number of times that a simulator can execute a selection of 12 DEVStone models in 1 minute. The variety of the chosen models ensures that we measure different particularities provided by DEVStone. The proposed metric allows us to compare various simulators and to assess the impact of new features on their performance. We use the DEVStone metric to compare some popular DEVS-based simulators.

An efficient simulation model for layout and mode performance evaluation of robotic mobile fulfillment systems

The robotic mobile fulfillment system (RMFS) is a “goods to man” picking system that employs numerous automatic guided vehicles (AGVs) instead of workers to transport goods from the storage station to the picking station. Using smarter AGVs enhances the performance but also leads to layout problems and the challenge of efficiently estimating the system performance with various layouts. With the same capability of storage, the different row and column of storage position area can lead to different system performance. The number of combinations of rows and columns is huge which makes it time-consuming to compare all the combinations to find the best one, and different path modes (unidirectional path mode and bidirectional path mode) and different number of AGVs further complicate the problem. Quickly modeling and performance estimating of RMFS become the biggest challenge for logistics enterprise. In this paper, according to the modular simulation theory, an efficient model based on discrete-event system simulation is proposed for quickly constructing different RMFS layouts and estimating their performance. The model contains two independent components: a physical component and a control component. The nuclear module for physical component is the track module that forms all tracks and can be temporarily or permanently blocked to satisfy the requirements of both unidirectional and bidirectional path mode models. The main functions of the control component are routing, collision prediction, and collision avoidance. The physical and control components are independent but work together to construct and run a certain model according to the input parameters; thus, an RMFS model can be efficiently built by simply entering parameters. A series of experiments were conducted to evaluate the performance of the RMFS in different layouts with various numbers of AGVs in both the unidirectional and bidirectional path modes. The results indicated that in the same layout, the bidirectional path mode can reach the summit output with fewer AGVs. The experimental results also indicated that in a large-scale RMFS, vertically arranging the storage station on the block island and then horizontally positioning the block island can result in a higher output than other layouts. In addition, the experimental results indicated that the proposed model can quickly construct different RMFS layouts and efficiently determine the best scheme.

Optimization of Overcast Stripping Technology Parameters Based on Discrete Event System Simulation

In the process of open‐pit mining, the system parameters determine the economic benefit and production efficiency of the mine. Conventional optimization involves building a system model for the process parameters. However, complex large‐scale systems such as open‐pit mining are difficult to model, resulting in a failure to obtain effective solutions. This paper describes a system simulation method for the process parameters involved in open‐pit mining. The nature and interaction of each component of the system are analyzed in detail, and the logical flow of each layer of the system is determined. Taking the basic operational linkages of the equipment as the system drivers, we obtained the operational flow of dragline information. The barycentric circular projection method is used to simplify the control logic of the system, and a system storage state model is constructed to identify dynamic changes in the system and obtain the operation parameters of the dragline. A discrete event system is used for quantitative modeling, and the event step method is employed to advance the simulation process and obtain decision information. Finally, simulations are performed using various system parameters. The simulation results show that the maximum efficiency is achieved when the dragline height is ∼13 m, giving a capacity of 4276.52 m3/h. Error analysis indicates that the modeling error is minimized using a simulation correction coefficient of α = 0.94.

Lean Maintenance 4.0: implementation for aviation industry

Nowadays, for staying competitive on the aviation market the aircraft manufacturers face the need to improve the quality of aviation after-sales services. For achieving this goal, it is needed to improve the aircraft maintenance organization through various methodologies, such Lean and Industry 4.0. In this paper it was suggested to integrate Lean and Industry 4.0 approaches for aircraft MRO processes and simulate its implementation in discrete-event simulation system AnyLogic. The simulation process methodology and aircraft maintenance process flow chart are proposed in this article. The results show that Lean Maintenance 4.0 methodology could be implemented for aviation industry and increase the efficiency of aircraft maintenance system organization. Moreover, the simulation methods could help to identify the bottlenecks in this process and make a right decision about new technologies, such as Lean and Industry 4.0, implementation.

Event-driven simulation method for fuel transport in a mesh-like pipeline network

An oil company produces a variety of products in refineries and often transports them in pipelines. Pipelines allow huge amounts of refined oil products to be transported between sites. Delivery scheduling plans determine the sequences of the pumping operations. Specifically, these plans specify the types and quantities of products to be transported, the start and end times of shipments, and the routes. A scheduling plan is feasible, if its implementation is free of any obstacles. Schedules can be validated manually only in case of short time intervals (3, 4 days) and only for a moderately complex pipeline network. If the scheduling plan is longer or the pipeline network is larger, then a simulation tool is required for the validation. This work presents a novel discrete-event simulation system, which helps to validate pipeline-schedule for medium time intervals in multi-source mesh-like pipeline networks.

Computational challenges to test and revitalize Claude Lévi-Strauss transformational methodology

The ambition and proposal for data modeling of myths presented in this paper is to link contemporary technical affordances to some canonical projects developed in structural anthropology. To articulate the theoretical promise and innovation of this proposal, we present a discrete-event system specification modeling and simulation approach in order to perform a generative analysis and a dynamic visualization of selected narratives, aimed at validating and revitalizing the transformational and morphodynamic theory and methodology proposed by Claude Lévi-Strauss in his structural analysis of myth. After an analysis of Lévi-Strauss’s transformational methodology, we describe in detail how discrete-event system specification models are implemented and developed in the framework of a DEVSimPy software environment. The validation of the method involves a discrete-event system specification simulation based on the extension of discrete-event system specification models dedicated to provide a dynamic Google Earth visualization of the selected myth. Future work around the discrete-event system specification formalism in anthropology is described as well as future applications regarding the impact of computational models (discrete-event system specification formalism, Bayesian inferences, and object-oriented features) to new contemporary anthropological domains.