Discrete Event Simulation Tool Research Articles

Development and verification of a discrete event simulation tool for high-fidelity modelling of offshore wind and solar farm decommissioning campaigns

The number of offshore wind turbines to be decommissioned is expected to grow in the next decade but some of the oldest wind farms in the North Sea region are already approaching the end of their service lives. This means that several detailed decommissioning plans will need to be prepared in the coming 2-5 years. So far, only small-scale wind farms consisting of relatively small turbines located in shallow, near-shore, or confined waters have been decommissioned. The limited experience and data available yield high uncertainty in the estimation of costs, duration, and environmental footprint of upcoming offshore wind farm decommissioning campaigns. High-fidelity logistic simulation tools as well as high-level cost models are both needed to support end-of-life decision-making. While several holistic cost models have been proposed, the literature lacks high-fidelity models to support the detailed planning and execution of offshore wind farm decommissioning projects. Given that offshore operations are the greatest source of costs, emissions, and safety risks in decommissioning campaigns, these require special attention when seeking optimization opportunities. In this work, a high-fidelity logistic simulation module for the decommissioning of offshore wind and solar farms has been developed within the discrete event simulation platform UWiSE. The new UWiSE Decommission module enables detailed assessment of alternative decommissioning scenarios taking into account offshore operation complexities like weather and resource dependencies, which are not included or heavily approximated in holistic cost models. The new tool has been verified against publicly available analytical cost model results from recent literature. A short assessment of the impact of weather delays on cost and duration estimates for the considered offshore wind farm decommissioning campaign has also been performed as an example of how UWiSE Decommission may bring value to the planning of offshore decommissioning projects.

Optimizing the management of electrophysiology labs in Chinese hospitals using a discrete event simulation tool

BackgroundThe growing demand for electrophysiology (EP) treatment in China presents a challenge for current EP care delivery systems. This study constructed a discrete event simulation (DES) model of an inpatient EP care delivery process, simulating a generalized inpatient journey of EP patients from admission to discharge in the cardiology department of a tertiary hospital in China. The model shows how many more patients the system can serve under different resource constraints by optimizing various phases of the care delivery process.MethodsModel inputs were based on and validated using real-world data, simulating the scheduling of limited resources among competing demands from different patient types. The patient stay consists of three stages, namely: the pre-operative stay, the EP procedure, and the post-operative stay. The model outcome was the total number of discharges during the simulation period. The scenario analysis presented in this paper covers two capacity-limiting scenarios (CLS): (1) fully occupied ward beds and (2) fully occupied electrophysiology laboratories (EP labs). Within each CLS, we investigated potential throughput when the length of stay or operative time was reduced by 10%, 20%, and 30%. The reductions were applied to patients with atrial fibrillation, the most common indication accounting for almost 30% of patients.ResultsModel validation showed simulation results approximated actual data (137.2 discharges calculated vs. 137 observed). With fully occupied wards, reducing pre- and/or post-operative stay time resulted in a 1–7% increased throughput. With fully occupied EP labs, reduced operative time increased throughput by 3–12%.ConclusionsModel validation and scenario analyses demonstrated that the DES model reliably reflects the EP care delivery process. Simulations identified which phases of the process should be optimized under different resource constraints, and the expected increases in patients served.

Manufacturing system reconfiguration towards sustainable production: a novel hybrid optimization methodology.

Developing a sustainable manufacturing system is a progressively challenging issue as governments across the world have been enforcing increasingly severe regulations by promoting the reduction of environmental waste for manufacturing and energy-saving production activities. Thus, there is a need for developing a sustainable manufacturing system that can be fully examined by incorporating ecological aspects (e.g., consumed energy) for related operations of a manufacturing system using computer-based discrete event simulation tools. In this study, a combined framework of a novel hybrid fuzzy multi-objective optimization and discrete event simulation approach is presented. We combine ecological and economic data and optimization techniques that are aimed at minimizing economic and ecological objectives in a manufacturing system at an early design phase. Hence, the fuzzy multi-objective optimization model is formulated by incorporating economic and ecological parameters. Again, the discrete event simulation model is established based on a comprehensive performance evaluation of the production system. This study also supports design decisions in determining optimum machine numbers, lighting, and cooling equipment required for the production processes within the sustainable manufacturing in conjunction with the most effective level of material flows. In addition, an integrated Decision-Making Trial and Evaluation Laboratory (DEMATEL)-epsilon constraint approach is applied to handle the multiple-objective optimization problem towards a set of trade-offs among the optimization objectives. A real-life application is carried out for investigating the applicability of the created hybrid framework. The findings of this study demonstrate that this framework is useful as a decision-making tool since it can develop a sustainable manufacturing system design considering an optimal solution associated with amounts of energy usage and CO2 emissions under economic constraints.

Review of simulation software for cyber-physical production systems with intelligent distributed production control

ABSTRACT Many intelligent distributed production control architectures have been developed for cyber-physical production systems (CPPSs), but the difficulty in predicting performance has hindered acceptance by industry. Performance predictions for systems with conventional control can be made by simulating product flow using discrete-event simulation (DES) software. However, DES is inadequate for capturing the intricacies of intelligent distributed production control architectures. Alternatively, agent-based simulation (ABS) software is more effective for capturing distributed intelligence. A hybrid discrete-event and agent-based simulation tool combines the strengths of both approaches, making it effective for capturing the intertwined physical and cyber layers of a CPPS. In this paper, a review is carried out to determine which off-the-shelf simulation tools are capable of using hybrid discrete-event and agent-based simulation for performance predictions in the conceptual design phase of a CPPS. This review is carried out using the following structured steps. First, the scope and evaluation criteria are identified. Next, a selection of simulation tools is collected. The selected tools are then evaluated and classified. Finally, the most promising simulation tool according to this evaluation – Anylogic – is subjected to a case study to assess if hybrid simulation can be used to predict the performance of a CPPS.

Transport of pilgrims during Hajj: Evidence from a discrete event simulation study.

Hajj, the Muslim pilgrimage, is a large mass gathering event that involves performing rituals at several sites on specific days and times in a fixed order, thereby requiring transport of pilgrims between sites. For the past two decades, Hajj transport has relied on conventional and shuttle buses, train services, and pilgrims walking along pedestrian routes that link these sites. To ensure smooth and efficient transport during Hajj, specific groups of pilgrims are allocated with the cooperation of Hajj authorities to specific time windows, modes, and routes. However, the large number of pilgrims, delays and changes in bus schedules/timetables, and occasional lack of coordination between transport modes have often caused congestion or delays in pilgrim transfer between sites, with a cascading effect on transport management. This study focuses on modelling and simulating the transport of pilgrims between the sites using a discrete event simulation tool called "ExtendSim". Three transport modules were validated, and different scenarios were developed. These scenarios consider changes in the percentages of pilgrims allocated to each transport mode and the scheduling of various modes. The results can aid authorities to make informed decisions regarding transport strategies for managing the transport infrastructure and fleets. The proposed solutions could be implemented with judicious allocation of resources, through pre-event planning and real-time monitoring during the event.

Modeling Productivity Reduction and Fuel Consumption in Open-Pit Mining Trucks by Considering the Temporary Deterioration of Mining Roads through Discrete-Event Simulation

Open pit mining is based on dynamic operations within the open pit, with the haulage operation being the most decisive in terms of the cost and the overall productivity of the operation. A vital aspect for the success of the haulage operation are the conditions of the roads, which change over time due to wear and tear. The present work aims to model and to evaluate the effect of temporal deterioration affecting mining roads in an open pit mining operation through rolling resistance, using the discrete event simulation (SED) tool to evaluate two key aspects of the operation: productivity and fuel consumption. Additionally, to reverse road deterioration, the assignment of a grader with a variable frequency of use will be modeled through a parallel model. The case study is a 5 km road with a slope of 9.5%, a fleet of 10 trucks, and their capacities of 300 tons, loaded in a 24 h simulation model. The results show that not considering road deterioration can mean an overestimation of productivity of up to 600 Ton/hr, and an overconsumption of 78%. The application of a motor grader could decrease the negative effects of roads deterioration, which depends on the frequency of its assignment. The results show that for the rate of productivity and fuel consumption for every hour of frequency that the motor grader does not do road maintenance, the production loss is 600 tons, and the fuel consumption increases by approximately 1800 L in a day.

Embedding R inside the PhysPK Bio-simulation Software for Pharmacokinetics Population Analysis

Abstract Background: PhysPK stands as a flexible and robust bio-simulation and modeling software designed for analysis of population pharmacokinetics (PK) and pharmacodynamics (PD) systems. PhysPK equips users with standard diagnostic plots for pre- and post-analysis to delineate PK and PD within population-based frameworks. Furthermore, PhysPK facilitates the establishment of mathematical models that elucidate the intricate interplay between exposure, safety, and efficacy. Methods: Enhancing simulation modeling capabilities necessitates seamless integration between commercial discrete-event PK and PD simulation tools and external software. This synergy can be amplified by incorporating open-source solutions, like R, which boasts a rich array of comprehensive packages tailored for diverse tasks, including data analysis (ggplot2), scientific computation (stats), application development (shiny), back-end web development (dplyr), and machine learning (CARAT). The integration of R within PhysPK holds the potential to efficiently interpret and analyze PK/PD output and routines using R packages. Results: This article presents a tutorial that highlights the incorporation of R code within PhysPK and the rendering of R scripts within the PhysPK monitor. The tutorial utilizes a two-compartment model for comparison against the analysis developed by Hosseini et al. in 2018 within the context of the gPKPDSim application and WinNonlin® software. The illustrative example that is provided and discussed demonstrate estimated and simulated plots, revealing negligible differences in the significance for CL and CLd (6.89 ± 0.2 and 45.5 ± 17.4 [reference], and 7.06 ± 0.32 and 49.04 ± 9.2 [PhysPK], respectively), as well as volumes V1 and V2 (49.15 ± 3.8 and 34.61 ± 5.2 [reference], and 48.8 ± 3.66, and 33.2 ± 3.95 [PhysPK], respectively). Conclusions: Our study underscores the potential of integrating open-source software, replete with an array of innovative packages, to elevate predictive capabilities and streamline analyses in PK methods. This integration ushers in new avenues for an advanced intelligent simulation modeling within the realm of PK, thus holding significant promise for the advancement of drug research and development.

A discrete-event simulation tool for airport deicing activities: Dallas-Fort Worth International Airport

Aircraft deicing/anti-icing fluids (ADFs) are applied to remove and prevent icing on aircraft during taxi and takeoff. The Dallas-Fort Worth (DFW) International Airport uses deicing pads for deicing activities that collect and contain the spent deicing fluids for proper treatment or disposal. Local waterways receive ADF as “drip and shear” during the aircraft taxi on the runway and then takeoff. The glycol-based ADF serves as a nutrient for bacteria that grow exponentially, deplete dissolved oxygen (DO) from receiving waterways, and subsequently kill aquatic life. In this paper, we present a data-driven discrete-event simulation modeling process developed in collaboration with DFW Airport to assess aircraft assignment strategies to deicing pad locations by monitoring impact on DO. Our process consists of the following phases: (1) Data Collection, (2) Probability Distribution Modeling, and (3) State Transition Modeling. Both Phases (2) and (3) utilized data mining approaches, including treed regression and variable selection via false discovery rate. Detailed implementation of these phases is described for the DFW Airport case study, and the DFW Airport deicing activities simulation tool framework is presented. The actual data and simulation results were compared in terms of the DO levels in airport receiving waterways to verify the model validity after implementing the proposed model for DFW. Thus, the proposed model can be implemented by airports to control and minimize the adverse environmental effects resulting from deicing activities by optimizing the aircraft assignment to the pad locations.

TauSSA

In this paper, we present TauSSA, a discrete-event simulation tool for stochastic queueing networks integrated in the LINE solver. TauSSA combines Gillespie's stochastic simulation algorithm with tau leaping, a methodology for optimistic simulation acceleration. Although tau leaping is frequently used in chemical reaction network simulation, it has so far found limited application in queueing theory. TauSSA offers one of the very first attempts to make this method broadly applicable to analyze extended queueing network models, which include class switching, fork-join, and non-exponential service and arrival distributions. We conceptualize various strategies for handling ordering and illegal states in tau leaping that arise specifically within queueing network models, and compare their performance through numerical experiments. Our main finding is that strategies that sort events based on the network topological order incur a better trade-off between speedup and approximation error.

Reactive online scheduling of mobile resources for adaptive layout evolution in line-less assembly system

Assembly systems are required to be more flexible due to unpredictable resource and demand fluctuations. Line-less assembly systems (LAS) implement flexible job routes through multi-purpose resources and flexible transportation systems. Moreover, a completely reactive layout through (re-)arrangement of stations with mobile resources enables reconfigurations without interrupting production. A scheduling that can handle the complexity of dynamic events is necessary to plan job routes and control transportation in such an assembly system. Since the system layout and the job scheduling influence each other, only an integrated view of layout planning and scheduling implements a reactive system behaviour. Existing pre-planning methods with fixed cycle times are not designed for reactive layout optimization during line-less assembly operations with mobile resources. Therefore, the contribution of this paper is an integrated online optimization architecture for layout evolution using mobile resources and simultaneous job scheduling to increase the efficiency of LAS. The optimization is implemented using modular matching and scheduling algorithms considering temporary station setup states and parallel processing based on zone restrictions. The proposed algorithms are connected to a modular discrete-event simulation and scenario analysis tool of LAS. The simulation and control architecture is validated by applying an industry-based use case. The reactive layout evolution, including station exchanges and mobile resource repositioning are benchmarked with nonflexible control methods. Results show that the developed control system allows for shorter average lead-time and increased throughput and utilization of (mobile) resources.

A capacity sharing approach to manage jointly transportation and emergency fleets at EMS organisations

ABSTRACT Emergency Medical Services (EMS) first mission is to reach people requiring urgent medical attention and transport them to hospitals or care facilities. In many cases, EMS also provide a second mission, which concerns the non-emergency transportation of patients. These services have different characteristics and goals from a managerial standpoint and in practice, most EMS organisations split their fleet into two sub-fleets that are managed independently. However, both missions are in most of the cases carried out by the same types of ambulances and crews, suggesting that managing both fleets together might bring potential advantages. This study explores the potential advantages of a new management strategy that allows sharing resources between two separated ambulance fleets. In particular, the proposed strategy allows for dynamically modifying the size of each fleet considering that a subset of ambulances can change their mission during the day to better adapt to the system's state. This strategy offers an incomplete integration of the fleets, but has the worthy advantages of improving the overall system performance and being simple to implement by an EMS organisation. Numerical experiments on realistic instances demonstrate, using a discrete event simulation tool, the feasibility and benefits of the proposed strategy.

A Machine-Learning-Assisted Simulation Approach for Incorporating Predictive Maintenance in Dynamic Flow-Shop Scheduling

In this paper, a machine-learning-assisted simulation approach for dynamic flow-shop production scheduling is proposed. This is achieved by introducing a novel framework to include predictive maintenance constraints in the scheduling process while a discrete event simulation tool is used to generate the dynamic schedule. A case study for a pharmaceutical company by the name of Factory X is investigated to validate the proposed framework while taking into consideration the change in forecast demand. The proposed approach uses Microsoft Azure to calculate the predictive maintenance slots and include it in the scheduling process to simplify the process of applying machine-learning techniques with no need for hard coding. Several machine-learning algorithms are tested and compared to see which one provides the highest accuracy. To gather the required dataset, multiple sensors were designed and deployed across machines to collect their vitals that allow the prediction of whether and when they require maintenance. The proposed framework with discrete event simulation generates optimized schedule with minimum makespan while taking into consideration predictive maintenance parameters. Boosted Decision Tree and Neural Network algorithms showed the best results in estimating the predictive maintenance slots. Furthermore, the Earliest Due Date (EDD) model produced the minimum makespan with 76.82 h while scheduling 25 products using 18 machines.

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.
 
 

Modelling Mass Crowd Using Discrete Event Simulation: A Case Study of Integrated Tawaf and Sayee Rituals During Hajj

Hajj is a mass gathering event that takes place annually in Makkah, Saudi Arabia. Typically, around three million people participate in the event and perform rituals that involve their movements within strict space and time restrictions. Despite efforts by the Hajj organisers, such massive crowd gathering and movement cause overcrowding related problems at the Hajj sites. Several previous simulation studies on Hajj focused on the rituals individually. Tawaf, followed by Sayee, are two important rituals that are performed by all the pilgrims at the same venue on the same day. These events have a strong potential for crowd buildup and related problems. As opposed the previous works in the literature, in this paper we study these two events jointly, rather than separately. We use ExtendSim, a Discrete Event Simulation tool, to integrate the Tawaf and Sayee rituals into one model. The validated model was applied to a wide range of scenarios where different percentages of pilgrims were allocated to the various Tawaf and Sayee areas. The effect of such allocations on the time to complete Tawaf and Sayee indicate strategies for managing these two key Hajj rituals.

Open-source discrete-event simulation software for applications in production and logistics: An alternative to commercial tools?

Abstract Discrete-event simulation is an established method to support decision making for planning tasks in production and logistics. However, there are still many enterprises, especially smaller companies that do not use discrete-event simulation because of the high costs associated with buying and maintaining commercial simulation tools. The question is whether or not free discrete-event simulation software is an alternative to commercial tools for solving typical planning tasks in production and logistics. The paper analyzes the modeling process with the three free and open-source discrete-event simulation tools Salabim, JaamSim and CloudSim and compares them with the two standard commercial simulation packages Arena and Plant Simulation. JaamSim provides everything which is necessary to model typical planning tasks in production and logistics and proves as a real alternative to commercial discrete-event simulation tools.

A simulation-based AHP approach to analyze the scalability of EHR systems using blockchain technology in healthcare institutions

Since their introduction, blockchain applications have been numerous and varied, allowing for the resolution of many issues previously considered insurmountable. In the field of health records management, the storing of electronic health records (EHRs) has been one of the topics of greatest interest to academics, though its implementation using blockchain technology has not been without flaws. Therefore, the objective of this research is to select the blockchain protocol that provides the best performance given the scalability issue in the implementation of a BT-based EHR system in a high-priority healthcare institution (HP–HI). To achieve this objective, a discrete-event simulation tool and the AHP technique are both combined, to model the selected blockchain protocols and to analyze the output data. Ethereum protocol emerges as the platform with the best overall performance for the HP-HI under consideration. This result is based on a rigorous analysis of three blockchain protocols–Ethereum, Dogecoin and Bitcoin–and five criteria to evaluate the scalability in BT-based EHR systems: sent transactions, received transactions, failed transactions, nodes, and cost. This research is the first practical study to assess the implementation of BT-based EHR systems in the context of the Colombian health system. This research also integrates a general evaluation framework that can be replicated to other similar HCs. In light of the scalability problem studied, Ethereum protocol is consistently the most appropriate blockchain solution for the selected HP-HI. This result is in line with previous research on BT-based EHR systems. The problem-solving integrated approach discussed in this research can also be replicated to other similar HCs to improve their performance against pandemics such as COVID-19.

Improved multi-fidelity simulation-based optimisation: application in a digital twin shop floor

ABSTRACT In recent years, the literature has paid considerable attention to digital twin technology for the implementation of Industry 4.0 and intelligent manufacturing. Most of the literature argues that simulation models are a key platform for digital twins and considers discrete-event simulation to be a suitable method to model real dynamic manufacturing systems. However, the discrete-event simulation of complex manufacturing systems is a time-consuming process. Therefore, it is difficult to deal with the large-scale discrete optimisation problems in digital twin shop floors. To bridge this research gap, we propose an improved multi-fidelity simulation-based optimisation method based on multi-fidelity optimisation with ordinal transformation and optimal sampling (MO2TOS) in the current research. The proposed method embeds heuristic algorithms to accelerate the solution space search efficiency in MO2TOS. Moreover, we develop an improved multi-fidelity simulation-based optimisation system by integrating the proposed method with discrete-event simulation tools and apply this system to a digital twin-based aircraft parts production workshop. Based on this digital twin shop floor, we conduct different production planning experiments to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed improved multi-fidelity simulation-based optimisation method is well-applied in solving large-scale problems and outperforms other simulation-based optimisation methods.

Comparison of fixed and mobile in-pit crushing and conveying and truck-shovel systems used in mineral industries through discrete-event simulation

In-pit crushing and conveying (IPCC) systems (whether fixed, mobile, or semi-mobile) are an alternative to the traditional truck/shovel approach to materials handling, offering potential cost savings. However, assessing the viability of one of these systems for an existing or planned open-pit mine from an operational standpoint presents a great deal of challenge given the complexity of the problem as they evolve non-linearly in many regards. This research applied the discrete event simulation to compare and model the materials handling systems of a mine to assess the applicability and weaknesses of the techniques for such use. Varying configurations of a mine's geometry were fed into the simulation then run and optimized for cost using the discrete event simulation tool, Arena®. The development of the model and simulation highlighted the potential for discrete event simulation to serve as a rapid tool to compare IPCC and truck/shovel systems by assessing those parameters and associated costs inherent with the operating realities of each system.

A DISCRETE-EVENT SIMULATION MODEL FOR DRIVER PERFORMANCE ASSESSMENT: APPLICATION TO AUTONOMOUS VEHICLE COCKPIT DESIGN OPTIMIZATION

AbstractThe latest advances in the design of vehicles with the adaptive level of automation pose new challenges in the vehicle-driver interaction. Safety requirements underline the need to explore optimal cockpit architectures with regard to driver cognitive and perceptual workload, eyes-off-the-road time and situation awareness. We propose to integrate existing task analysis approaches into system architecture evaluation for the early-stage design optimization. We built the discrete-event simulation tool and applied it within the multi-sensory (sight, sound, touch) cockpit design industrial project.

A decision support methodology for integrated machining process and operation plans for sustainability and productivity assessment

This paper presents a systematic methodology to enable environmental sustainability and productivity performance assessment for integrated process and operation plans at the machine cell level of a manufacturing system. This approach determines optimal process and operation plans from a range of possible alternatives that satisfy the objectives and constraints. The methodology provides a systematic procedure to highlight parameters that have significant impact on both sustainability and productivity performance metrics. We developed models and applied them to analyze manufacturing life cycle scenarios for collecting and categorizing key concepts towards building a material information model for sustainability. Integration of process and operation plans allows globalized assessment of sustainability and productivity, while development of a multi-criteria decision-making method leads to optimization of process planning activities based on the impact of conflicting sustainability and productivity metrics. A case study is detailed to demonstrate the sustainability-focused methodology, wherein integrated simulation and optimization techniques are used to support analysis of candidate scenarios and selection of preferred alternatives from a finite set of alternate process and operation plans. A discrete event simulation tool is used to model evolution of sustainability metrics (e.g., energy consumption) and productivity metrics (e.g., production time, cost) of a shop floor. The outcomes of this work include determination of optimized feature sequence plans which optimize various key performance indicators depending on stakeholder interest based on time, sustainability and production cost.