Discrete-event Model Research Articles

Cost and environmental impact analysis of slag repurposing as alternative solution to bricks

The world’s leading producer of Platinum Group Metal is South Africa and accounts for 94 percent of world’s platinum. Platinum smelters produce around 10 525,90 tons of slag annual of which accumulates as stockpile, never utilised. More storage area is required for continuous operation of the smelters to store slag. The aim of the research is to investigate the cost and environmental impact of repurposing slag into bricks for low-cost housing. AnyLogic simulation model specifically the Discrete Event Modelling (DEM) was employed for the modelling and simulation because of its capabilities compared to other models such as Flexsim, Vensim, ANSYS. The results indicate that 92 human resources are required to have functioning business that is able to manufacture and deliver bricks enough to build 326 houses per month. The operation is labour intensive to ensure more job opportunities are created and a total 368 personnel are employed permanently on all sites. The smelter potentially saves over R1,5 billion per operation and R5,6 billion across 4 sites over a decade by giving the slag away for free. The environmental pollution is improved as five heavy plant equipment is removed from the operation. The operation has potential to generate R392,2 million profit over 10-years.

Cost and environmental impact analysis of slag repurposing as alternative solution to bricks

The world’s leading producer of Platinum Group Metal is South Africa and accounts for 94 percent of world’s platinum. Platinum smelters produce around 10 525,90 tons of slag annual of which accumulates as stockpile, never utilised. More storage area is required for continuous operation of the smelters to store slag. The aim of the research is to investigate the cost and environmental impact of repurposing slag into bricks for low-cost housing. AnyLogic simulation model specifically the Discrete Event Modelling (DEM) was employed for the modelling and simulation because of its capabilities compared to other models such as Flexsim, Vensim, ANSYS. The results indicate that 92 human resources are required to have functioning business that is able to manufacture and deliver bricks enough to build 326 houses per month. The operation is labour intensive to ensure more job opportunities are created and a total 368 personnel are employed permanently on all sites. The smelter potentially saves over R1,5 billion per operation and R5,6 billion across 4 sites over a decade by giving the slag away for free. The environmental pollution is improved as five heavy plant equipment is removed from the operation. The operation has potential to generate R392,2 million profit over 10-years.

A system model to repurpose slag into alternative brick solution for low-cost housing

The world’s leading producer of Platinum Group Metal is South Africa and accounts for 94 percent of world’s platinum. Platinum smelters produces around 10 525,90 tons of slag annual of which accumulates as stockpile, never utilised. More storage area is required for continuous operation of the smelters to store slag. The aim of the research is to repurpose the slag into bricks for low-cost housing. Any Logic simulation model specifically the Discrete Event Modelling (DEM) was employed for the modelling and simulation because of its capabilities compared to other models such as Flexsim, Vensim, ANSYS. The results indicate that 92 human resources are required to have functioning business that is able to manufacture and deliver bricks enough to build 326 houses per month. The operation is labour intensive to ensure more job opportunities are created and a total 368 personnel are employed permanently on all sites. The smelter potentially saves over R1,5 billion per operation and R5,6 billion across 4 sites over a decade by giving the slag away for free. The environmental pollution is improved as five heavy plant equipment is removed from the operation. The operation has potential to generate R392,2 million profit over 10-years.

Closed-loop workload input–output control of production systems: A hybrid simulation study

Workload Control (WLC) is a method of adjusting the overall workload in the production system by controlling the input of production orders and the output of produced items. It contributes to the predictability of lead times and more accurate delivery date commitments in make-to-order manufacturing. Input control relates to order release and output control to capacity adjustment. In a recent previous work, a novel approach to integrated input–output control has been proposed: a dynamic closed-loop model where automatic feedback control is added and where order release and capacity decisions are based on the observed shop floor state. In this paper, this approach is further investigated in a more realistic hybrid simulation setting, combining discrete-time controllers and a discrete-event model of a manufacturing shop. The hybrid model was applied to two different systems: an elementary 4-machine shop floor (as a proof of concept) and a more complex real system, a manufacturing cell of cylinder liners. The results show that simultaneous input–output control is effective and enables the systems to maintain WIP balance, absorb demand fluctuations, and effectively reject disturbances. The simulations of the complex system showed that it might be advantageous to provide more local or more global information to the controllers, depending on the variability of the processing times and on the managerial focus. Closed-loop discrete-event models for WLC production control are unprecedented. Also, a simultaneous input and output control setting is much less reported in WLC literature than settings with input control only (i.e., order release only). This paper contributes in both ways.

Port Accessibility Depends on Cascading Interactions between Fleets, Policies, Infrastructure, and Hydrodynamics

Reducing waiting times is crucial for ports to be efficient and competitive. Important causes of waiting times are cascading interactions between realistic hydrodynamics, accessibility policies, vessel-priority rules, and detailed berth availability. The main challenges are determining the cause of waiting and finding rational solutions to reduce waiting time. In this study, we focus on the role of the design depth of a channel on the waiting times. We quantify the performance of channel depth for a representative fleet rather than the common approach of a single normative design vessel. The study relies on a mesoscale agent-based discrete-event model that can take processed Automatic Identification System and hydrodynamic data as its main input. The presented method’s validity is assessed by hindcasting one year of observed anchorage area laytimes for a liquid bulk terminal in the Port of Rotterdam. The hindcast demonstrates that the method predicts the causes of 73.4% of the non-excessive laytimes of vessels, thereby correctly modelling 60.7% of the vessels-of-call. Following a recent deepening of the access channel, cascading waiting times due to tidal restrictions were found to be limited. Nonetheless, the importance of our approach is demonstrated by testing alternative maintained bed level designs, revealing the method’s potential to support rational decision-making in coastal zones.

Development of a Proof-of-Concept Multi-Method Computer Simulation to Support Rural Healthcare Disaster Preparedness Planning

Due to a lack of resources, rural communities often face challenges when planning catastrophic events. This project involved applying systems thinking and model-based systems engineering to develop a proof-of-concept, multi-method computer simulation and then determining whether the simulation could be used to assess the efficacy of disaster planning approaches on health outcomes in rural communities, as a function of primary healthcare. The project focus was a rural or non-urban healthcare system experiencing a natural hazard. Both system dynamics and discrete event models were incorporated to represent subsystem operations, crucial disaster responses, as well as three key response systems: public health, emergency management, and healthcare. The subsystem models included several components: policies/procedures, communications, resources, exercises/drills/training, healthcare space and staff, and the flow of affected people into and through the system. The combined simulation can serve as a first step to a more comprehensive approach to helping rural communities achieve more efficient and effective healthcare planning for disaster responses.

Improve Ship Design Success by Utilising Proactive Elicitation to Enhance Communication Among Diverse Stakeholders

Ships serve as crucial tools for maritime transportation and account for over 80% of global trade. However, the boundary between naval architecture and the maritime industry has existed for 150 years since the first Industrial Revolution. Despite the current ship design processes ensuring compliance with standards, a significant gap exists in effectively communicating and incorporating diverse stakeholders' expectations and desires during the ship design process. This communication gap may lead to a potential risk for incidents and compromise safety. To tackle this issue, this paper proposes to use proactive elicitation as a means to enhance the communication between various stakeholders and naval architects. To verify the impact of proactive elicitation on ship design success, discrete event models were developed in the study to simulate the ship design process. The simulation results demonstrated that the earlier the proactive elicitation is applied to ship design, the higher the level of design success can be achieved. In the short term, this approach can assist shipyards in the timely delivery of ships, while in the long term, it fosters improved compatibility between stakeholders and ships, enabling effective adaptation to future complex design conditions.

Prospects for the use of Simulation Modeling for Managing Student Entrepreneurship at the University

The relevance of the article is in the choice and application of research methods for the decision-making process in a university. The level of complexity of management objects is growing due to the increasing level of business turbulence. Universities are no exception, based on their ongoing transformation and the process of becoming a player in the market economy. The use of simulation modeling is a promising topic and deserves more attention. The purpose of the study is to present and demonstrate the possibilities of using a simulation tool to manage university processes. Research hypothesis: despite the fact that simulation modeling is becoming an increasingly popular method of forecasting in social systems, it is not used in the field of university management. The paper presents a model of student entrepreneurship at IRNRTU (Irkutsk), built with the help of AnyLogic. It demonstrates its work from the point of view of the behavior of the model under normal conditions and conditions of stimulating a separate competition (Program “Umnik” (Foundation for Assistance to Small Innovative Enterprises), Program “START” (Foundation for Assistance to Small Innovative Enterprises) and the “Startup” program (Foundation for Assistance to Small Innovative Enterprises with the Federal project “University Technological Entre-preneurship Platform”). As the software, the AnyLogic program (Russia) was selected and applied. The group of researchers selected a discrete-event model for ease of collection statistics and selection of the winner. As a result, it was concluded that the model is able to predict the most likely outcome of the underlying actions or interests in a shorter period of time compared to other forecasting tools. The theoretical significance of the work is in expanding the list of issues under study for the use of simulation modeling in higher education. The practical significance is in the application of the results to assess the effectiveness of student entrepreneurship and it might be of interest to both university administrations and higher education researchers.

A decision-making framework for automating distribution centers in the Retail supply

Warehouse/distribution center (DC) automation technology for the retail industry promises to reduce operational costs, improve flexibility and response time for customers, and help improve network productivity, thus making it very relevant for omni/multichannel (OC/MC) settings. However, the investment required to acquire the DC automation technology is high, and hence, the investment decision must be operationally and financially comprehensive. In fact, an automated DC has a network-wide impact: it can benefit players in the network, but in turn is exposed to network risks and the investment must be safeguarded. While the need for a comprehensive decision-making framework and safeguarding strategy is stressed by scholars, such a framework is lacking. Further, corresponding integrated sub-frameworks for key elements in the OC/MC value chain are also missing. In this paper, we address these gaps and contribute by providing a) generalized and integrated three-part framework, b) corresponding sub-frameworks, c) discrete event, economic, and math programming models, d) rapid-sizing/analysis tools based on: i) analysis at the DC-level, ii) network level, iii) economic/business level, and iv) contract level (sustainable supplier/distribution relationship). In this reference, we investigate a new generation ‘full-case’ technology that has been recognized as a key to warehouse automation. The insights from our research inform several strategic tradeoffs (extent of automation, investment in labor vs. capital, response vs. efficiency, and sustainable supplier management) relevant for decision-making and safeguarding an expensive asset such as an automated DC. Our analysis is based on interviews (retailers, automated and conventional DCs, and DC equipment suppliers), on-site observations, secondary data, and learning from analytical models. We also present an illustrative real-life application/case study of the framework and the modeling details in the E-component.

Two-time-scale stochastic functional differential equations with wideband noises and jumps

This work examines a class of path-dependent stochastic systems which are hybrid with wideband noise, Poisson jumps and a singularly perturbed Markov chain. The addition of multi-scale Markov chain allows for modeling of discrete events with both fast and slow fluctuation. While this more realistic approach presents analytical challenges due to the non-Markovian formulation resulting from the wideband noise and the singularly perturbed Markov chain. By virtue of the weak convergence method and Itô functional formula, we prove that as ɛ→0, we obtain a Markovian switching jump diffusion. Finally, we offer several examples to illustrate our findings.

Model validation and Robust State Feedback control for nonlinear subway traffic networks

This paper develops a nonlinear Discrete-Event (DE) model for Intersecting Lines (ILs) of a Subway Network (SN). A class of Monte Carlo (MC) methods based on repeated random values is employed to validate the model. Model validation is performed by generating random values for delay rates and external disturbances. After model validation, a Robust State Feedback (RSF) controller is designed to compensate the delays caused by disturbances. Simulations are performed based on the characteristics and actual data of lines 2 and 4 of the Tehran subway to demonstrate the effectiveness of the proposed approach. Simulation results show the accuracy and proper performance of the proposed model. In addition, the closed-loop responses demonstrate the effectiveness of the proposed RSF controller for centralized and decentralized control configurations.

A discrete event approach to micro-scale traffic modeling in urban environment

In this work, we present a new approach based on a discrete event formalism to model and simulate micro-scale urban traffic systems. The formalism is a coupling between the P-DEVS (Parallel-Discrete Event System Specification) formalism and UML (Unified Modeling Language) state machines. A system is represented by a set of coupled components. Each component supports the dynamics and logic of a system element. The models presented include the streets, intersections and traffic signs, all of which can be synchronized together through specific mechanisms. These models can be applied to real-world OpenStreetMap networks. A discrete event-driven adaptation of the simplified Gipps car-following model is introduced, and subsequently compared to its discrete time counterpart. The results show that our discrete event model follows dynamics which are similar to those of a discrete time model with a low update time step of 0.1s, despite not taking certain non-linearities of the latter into account. In terms of vehicle state changes and computation time, our approach outperforms the discrete time one with an update time step of 1s, both on a simple case study and on a real network.

Construction of block diagrams of a simulation model of the customer service process

The article, which is part of the author’s dissertation research, is devoted to the construction of a simulation model, namely a discrete-event model of the customer service process «as is» and «as will be». The introduction substantiates the relevance of the research topic, highlighting the object and subject of the study. A hierarchical structure of the system of quality indicators for infocommunication services has been constructed. There are 4 levels of indicators, at each level quality indicators are highlighted, namely: service speed indicators (average service time for a technical fault, average response time for reference services); satisfaction indicators (percentage of calls regarding technical aspects of service, percentage of calls regarding organizational aspects of service) and others. A hypothesis has been put forward about a change in the state of the service process after the introduction of a functional dialog agent into the process, which involves a reduction in customer service time. To test the hypothesis about reducing customer service time, discrete-event simulation models of the process «as is» and «as will be» were built, since modeling in terms of queuing theory not only improves the understanding of existing processes in the company, but also helps to identify bottlenecks and identify areas for further optimization, which ultimately leads to increased customer satisfaction and the efficiency of the company as a whole. Within the framework of the developed discrete-event model, the main attention is paid to two main stages: modeling the receipt of requests and modeling the processing of requests. Process optimization includes the introduction of an automated diagnostic system, namely a functional conversational agent to reduce wait time and increase request processing throughput. Block diagrams of discrete-event simulation models of the service process were also constructed in terms of the AnyLogic environment, and the advantages of its use were highlighted. The main objects used to create discrete-event models that describe the mathematical state of the simulated system are presented.

DECA: Discrete Event inspired Cellular Automata for grain structure prediction in additive manufacturing

Microstructure largely dictates macroscopic material properties and is strongly affected by processing. Therefore, the simulation of microstructure evolution in response to thermal fields during processing is of significant interest within the computational materials science community. Additive manufacturing (AM) has emerged as a technique for producing complex geometries and unique microstructures. Yet, complex and rapid thermal cycles in AM pose computational challenges for existing microstructure models. This work proposes a discrete event inspired cellular automata (CA) approach, titled DECA, to accelerate simulation of grain structure evolution in AM. In contrast to conventional time-stepped CA models, this model directly solves the times capture events would take place allowing for stepping in events rather than time (a technique also found in the field of discrete-event simulation). In comparison to purely serial discrete-event models, DECA allows for temporary violation of the causality constraint, but detects and corrects these violations, leading to an emergent phenomenon dubbed causality rippling, in which previously calculated capture events are overwritten. The amount of repeated calculations, defined by the capture ratio, is taken as a measure of computational inefficiency, and the model parameters that affect this ratio are evaluated. The new DECA approach was found to be more computationally efficient than conventional time-stepped CA models while guaranteeing an accurate solution, which can only be achieved in the conventional models for vanishingly small time steps. Finally, opportunities for parallelization and scaling of the new approach are discussed.

Hyperparameter Tuning with Gaussian Processes for Optimal Abstraction Control in Simulation-based Optimization of Smart Semiconductor Manufacturing Systems

Smart manufacturing utilizes digital twins that are virtual forms of their production plants for analyzing and optimizing decisions. Digital twins have been mainly developed as discrete-event models (DEMs) to represent the detailed and stochastic dynamics of productions in the plants. The optimum decision is achieved after simulating the DEM-based digital twins under various what-if decision candidates; thus, simulation acceleration is crucial for rapid optimum determination for given problems. For the acceleration of discrete-event simulations, adaptive abstraction-level conversion approaches have been previously proposed to switch active models of each machine group between a set of DEM components and a corresponding lookup table-based mean-delay model during runtime. The switching is decided by detecting the machine group’s convergence into (or divergence from) a steady state. However, there is a tradeoff between speedup and accuracy loss in the adaptive abstraction convertible simulation (AACS), and inaccurate simulation can degrade the quality of the optimum (i.e., the distance between the calculated optimum and the actual optimum). In this paper, we propose a simulation-based optimization (SBO) that optimizes the problem based on a genetic algorithm (GA) while tuning specific hyperparameters (related to the tradeoff control) to maximize the speedup of AACS under a specified accuracy constraint. For each individual, the proposed method distributes the overall computing budget for multiple simulation runs (considering the digital twin’s probabilistic property) into the hyperparameter optimization (HPO) and fitness evaluation. We propose an efficient HPO method that manages multiple Gaussian process models (as speedup-estimation models) to acquire promising optimal hyperparameter candidates (that maximize the simulation speedups) with few attempts. The method also reduces each individual’s exploration overhead (as the population evolves) by estimating each hyperparameter’s expected speedup using previous exploration results of neighboring individuals without actual simulation executions. The proposed method was applied to optimize raw-material releases of a large-scale manufacturing system to prove the concept and evaluate the performance under various situations.

Simulation Modeling of QS with Hyper-Erlang and Erlang Distributions

The article is devoted to the construction of simulation models of customer delay in the queue in the form of a queuing system (QS) described by both ordinary and right-shifted hyper-Erlang and second-order Erlang distributions. This article is a logical continuation of previous works devoted to the construction of numerical-analytical QS models with shifted distribution laws. In the article, the Erlang distribution is considered as a special case of the more general Gamma distribution law, in contrast to the normalized Erlang distribution. These two forms of the Erlang distribution differ in numerical characteristics, except for the coefficient of variation. To solve the problem, the system of discrete-event modeling GPSS WORLD was used.

Technology of simulation modeling in application to tasks of research of economic processes

The article discusses the effectiveness of using computer simulation modeling in problems of studying economic processes. The main methodological approaches are considered: system dynamics, discrete-event modeling and agent-based modeling. Elements of simulation modeling technology are proposed to improve the accuracy of solving managerial eco- nomic problems. In the AnyLogic simulation environment based on the Lotka-Volterra model, a model of interaction between three actors was built: startups, corporations and the state. It is shown that the selection of regulatory influences by simulating the behavior of the model of the system being studied is effective for making management decisions.

Optimization of Electric Bus Charging: Integrating Discrete Event Modeling in Public Transportation Systems

The transition to electric transportation is gaining more and more importance in the last years leading to new challenges to be addressed. A key role in this framework is represented by public transportation systems. This paper introduces a Discrete Event (DE) optimization model to optimize the charging scheduling of electric buses (EBs). By accounting for time tabling constraints, energy demand satisfaction, and the capability for simultaneous multi-socket charging, our model addresses the complexities inherent in managing EB fleets. Utilizing a periodic model to capture cyclical system behavior, we incorporate a detailed battery model reflecting nonlinear charging profiles to enhance accuracy in power requirement prediction and charging duration estimation. Validation of our model is performed using real-world data, demonstrating its practical applicability.

Interoperability performance evaluation for discrete event simulation models: A step towards multi-level data exchange

According to use scenarios, 3D-based virtual commissioning can be divided into three levels: machine, cell and plant. As virtual commissioning at each level focuses on different contents, corresponding applications are developed in different data models. The resulting 3D emulation and simulation models are thus not easily exchangeable or reusable. At machine level, a physics-based model is used to conduct emulation. While at plant level discrete event simulation models are quite common. A framework for exchanging 3D emulation and simulation models between software at different levels would stimulate model reuse and lower the effort for model creation. To date, no interoperability performance evaluation for discrete event simulation models has been presented. This paper investigates industrial standards and related work on discrete event model generation and data exchange. The interoperability performance of discrete event simulation tools is evaluated by the example case of exchanging models between two commercial tools (i.e., FlexSim and Visual Components) via Core Manufacturing Simulation Data and AutomationML. The result shows the former converts 7% more attributes than the latter. Furthermore, an element-based comparison is conducted to reveal overlaps and assess the potential of (partial) model reuse during the data exchange between 3D emulation and simulation models.

Simuleau: a tool for hybrid and batches Petri nets

Simuleau is a modeling, analysis and simulation tool for discrete event models expressed by batches Petri nets, a formalism that enriches hybrid Petri nets of David and Alla. Batches PNs incorporate timed-transition discrete PNs, constant continuous PNs, and new types of nodes called batch nodes. This formalism and Simuleau have been exploited among diverse application areas such as manufacturing systems, communication networks, and traffic road. After presenting batches PN formalisms and their application domains, this paper focuses on the main characteristics of Simuleau, including the tool structure, its menu interface, the input model description and the outputs obtained after simulation.