Discrete Event Simulation Research Articles

Bringing together Lean Six Sigma and discrete event simulation: a systematic literature review

Purpose This study aims to discover, compare and analyse the primary works that integrate Lean Six Sigma (LSS) and discrete event simulation (DES), exploring theoretical aspects to provide support for future integrated applications of the methods. Design/methodology/approach The authors carried out a systematic literature review involving selecting and evaluating papers in six scientific databases. This approach aimed to explore various aspects related to the integration of LSS and DES. Among the main aspects evaluated, the authors highlight the primary steps that involve applying both techniques, the structural organisation of each method, the associated objectives, adopted software, application areas, frameworks and other pertinent factors. Findings LSS offers DES a problem-solving-oriented approach, while DES allows LSS to test various scenarios planned for the DMAIC improvement phase. Despite being a promising theme, few studies examine these methods in an integrated manner. Moreover, among the works analysed, none presents a framework with guidelines for implementing these methods in an integrated way. Research limitations/implications This paper may not have included some of the studies due to the inaccessibility and selection criteria followed for the study. Originality/value This paper offers an overview and research insights considering the state of the art on integrating LSS and DES methods for researchers and practitioners in continuous improvement and computer simulation areas.

Digital-twin co-simulation framework to support informed decision in healthcare planning and management

This manuscript aims to show the proof-of-concept for the integration of digital twin (DT) and discrete event co-simulation through its application to forward simulation of physical activities of care providers and robots in an intensive care unit (ICU). Co-simulation focuses on modular integration of heterogeneous simulation components to capture the complexity inherent in systems. Advancing this conception, the proposed co-simulation attempted to narrow gaps with real-world systems by using DT while simultaneously leveraging the forward simulation capacity of discrete event simulation (DES). In particular, we created a co-simulation for human–robot interactions in a simulated ICU facility, which modeled the hospital layout including the facility, robot, avatars for healthcare professionals, equipment, patient bed, and vital signs, which comprise physical twin (PT). In addition, specific patient-care scenarios were defined to drive PT for which the states of interest were represented in the DT, and then used for a dynamic input to the discrete event co-simulation. At the onset of the co-simulation, the parameters needed to run DES were manually configured to align the parameters with those of the DT. For internal validation, this DES was replicated under different work-system policies, and the co-simulation outcomes were interpreted in clinical context. Our co-simulation approach has potential for application to a broader class of problems in healthcare planning and management by supporting informed decision-making. Methodologically, timely information exchanges of PT with DT, and ensuing model updates in the Discrete Event System Specification (DEVS) is a promising approach for co-simulation, to maintain robustness against perturbations to real-world systems.

Building credibility for human systems integration in model‐based systems engineering

AbstractRobust and trusted digital human representations are necessary to successfully account for human considerations in model‐based systems engineering (MBSE). Multiple domains and modeling frameworks leverage verification, validation, and accreditation (VV&A) processes to characterize when and under what conditions a model is valid to establish credibility. A literature review was completed on mathematical, physics‐based, software development, discrete event simulation, agent‐based, system dynamics, and MBSE models with the goal of proposing a process for performing VV&A on digital engineering (DE) and MBSE models for sociotechnical systems. However, this research also revealed the need for a broader framework to characterize the risk associated with using these models for making high‐consequence decisions. While accomplishing the literature review, another approach to building credibility was identified that is used heavily in the financial industry, namely model risk management (MRM). This process is extended by leveraging MRM approaches from within the financial community to propose a framework for sociotechnical model users to characterize the risk of using MBSE models to make programmatic decisions. The primary contribution of this work is to document a meta‐analysis of model VV&A while proposing an alternative approach to characterizing and communicating credibility that was discovered during this analysis. This approach could be a viable option for ensuring the credibility of human systems integration in MBSE models.

An integrated approach using discrete event simulation and design of experiments for working capital management in the fuel retail trade

ABSTRACT This study introduces an innovative approach to Working Capital Management (WCM) in the fuel retail sector, using Discrete Event Simulation (DES) and Design of Experiments (DOE). Efficient WCM is essential for small businesses, particularly in fuel retailing with its tight margins and intense competition. Unlike static financial tools relying on historical data, DES interacts with indicators such as Working Capital Requirement (WCR) and Liquidity Ratios, providing stochastic and accurate information. The study examined three variables (Supplier Payment Deadline, Credit Card Receipt Deadline, and Customer Payment Profile) across 16 experimental scenarios. The Payment Term to Suppliers emerged as the key factor in reducing WCR. Despite lower liquidity potential, the company balanced short-term obligations, identifying optimal WCM scenarios. The study shifts WCM analysis from static data to forecasted, real-time data, supporting dynamic business decision-making. It concludes that DES is a valuable tool for effective WCM in fuel retail, encouraging further research.

Discrete-Event Simulation Integrates an Improved NEH Algorithm for Practical Flowshop Scheduling Problems in the Satellite Industry

The production of multiple types of satellites based on a common manufacturing platform represents a permutation flowshop scheduling problem (PFSP) with complex constraints. This is a highly complex scheduling problem, yet there is still a gap between theoretical research and practical application, particularly in the satellite industry. Therefore, we propose a more practical method that integrates discrete-event simulation modelling and an improved NEH algorithm to solve a more realistic PFSP. The discrete-event simulation-based method includes the following three main components: a flexible PFSP simulation modelling approach, an improved NEH algorithm, and an interaction mechanism between the simulation model and the optimisation algorithm. The proposed method allows automatic and flexible simulation modelling according to the characteristics of the actual satellite manufacturing workshop, which determines the practical nature of the approach proposed in this paper and then achieves excellent scheduling results based on the special interaction mechanism. The computational results demonstrate that this is a 9.18% improvement over the initial NEH algorithm and a 1.40% improvement over the best current improved NEH algorithm.

Optimizing the Sweet Potato Supply Chain in Zimbabwe Using Discrete Event Simulation: A Focus on Production, Distribution, and Market Dynamics

This study leverages a Discrete Event Simulation (DES) model to optimize the sweet potato supply chain in Zimbabwe, focusing on production, distribution, and market dynamics under varying climate conditions. The integration of climate data into the simulation model reveals significant insights into the resilience of different sweet potato varieties, particularly highlighting the suitability of yellow-skinned sweet potatoes for harsh climates due to their high resilience and drought resistance. However, market preferences still favor white-skinned varieties despite their vulnerability to climate extremes. The DES model identifies key bottlenecks, particularly in cultivation and transportation, that hinder supply chain efficiency. To address these challenges, the study emphasizes the importance of targeted interventions, such as improving access to irrigation, strengthening pest management, and adopting community-based resource-sharing approaches. These strategies are critical for enhancing both the resilience and efficiency of the supply chain. Additionally, the study highlights the urgent need for adaptive strategies to mitigate the effects of drought on agricultural productivity, especially in regions that heavily rely on crops like sweet potatoes. Overall, this research offers strategic insights for policymakers and stakeholders aiming to improve food security and agricultural productivity in Zimbabwe, as well as in other countries with similar climate challenges.

Multiobjective design of automated order fulfillment systems: A case study for an existing surgical complex

We present a case study that introduces a systematic framework for designing an automated guided vehicle (AGV) order fulfillment system. The multi-stage framework integrated discrete event simulation (DES), super-efficiency data envelopment analysis (S-DEA) model, and Design of Experiments (DOE) methodologies for the design of an AGV system in an existing large-scale surgical complex within a comprehensive hospital in Singapore. Performance metrics derived from this model were consolidated into a single relative efficiency score using the S-DEA model. This efficiency score was then utilized in a DOE methodology, combined with a post-hoc model selection process, to robustly identify appropriate models for evaluating alternative system designs. The proposed framework optimizes AGV deployment by first identifying opportunities for AGVs to add value within the surgical system through a value-stream mapping approach. It then derives the optimal system design by balancing cost efficiency, manpower reallocation, and order fulfillment rates, facilitated by the S-DEA model augmented with the DOE approach and post-hoc model selection. Application of this framework to the surgical complex revealed several insights that can significantly enhance AGV efficiency prior to actual deployment. However, a key limitation of this case-study-based approach is its limited generalizability across different systems. Despite this, the methodology provides a robust foundation for AGV system design in similar healthcare environments.

Using simulation for long-term bed modelling in critical care

ABSTRACT A simulation model of the University Hospital of Wales (UHW) Critical Care (CC) Department is presented. This is the first CC model that considers the impact of future demand on capacity, supporting planning decisions to build a new hospital. A combination of long-term demand trajectories and Discrete Event Simulation (DES) are used. The results suggest the unit will need at least 66 Intensive Care beds and 19 Post-Anaesthesia Care beds to fulfil predicted demand in 2040 while being at capacity less than 5% of the time. Non-critical care ward beds impact patient flow in CC, thus must be considered when planning a new hospital. This study’s findings directly impact on decision making at UHW, having informed capacity planning of the planned unit. This paper contributes by presenting an infrastructure planning project using simulation as a decision-making tool, with transferable insights applicable to the planning of other CC units.

Discrete-event simulation in logistics and supply chain management: a scientometric perspective

ABSTRACT This paper has been designed in the form of a systematic literature review intended for evaluating the state-of-the-art of discrete-event simulation (DES) usage in the logistics and supply chain management fields under a scientometric perspective. With this primary aim in mind, 127 papers published between 2009 and 2022 are analysed, with the specific purposes of: 1) providing an overview of DES studies in logistics and supply chain management, map the main topics covered within this stream of literature and their evolution in time; 2) providing a robust ranking of the journals and authors in the field; 3) evaluating the rigor in the implementation of DES models in the logistics and supply chain management literature; and finally 4) correlating, through statistical elaborations, all the previous aspects to the success of these papers, measured as the citations received per year, taking inspiration from scientometric disciplines.

Analysis and simulation of the operational capacity of a rail motorway terminal

This work aims to analyze the factors that influence the operational time at a railway-motorway intermodal terminal, with a specific focus on the loading and unloading of trains with semi-trailers. This operation is commonly referred to as a rail motorway intermodal process. The terminal under analysis is the Zaragoza-PLAZA intermodal terminal, located in Zaragoza, Spain. Using AnyLogic® PLE, a discrete event and block-based logic simulation software, a model is developed to realistically simulate the terminal's operations, offering potential improvements. The model enables observation of the terminal's behavior by modifying certain parameters. The results obtained can be directly applied to the existing terminal for performance enhancements. Finally, a sensitivity analysis of the model is conducted, allowing us to assess how various factors influence the terminal's operations.

The PHEM-B toolbox of methods for incorporating the influences on Behaviour into Public Health Economic Models

BackgroundIt is challenging to predict long-term outcomes of interventions without understanding how they work. Health economic models of public health interventions often do not incorporate the many determinants of individual and population behaviours that influence long term effectiveness. The aim of this paper is to draw on psychology, sociology, behavioural economics, complexity science and health economics to: (a) develop a toolbox of methods for incorporating the influences on behaviour into public health economic models (PHEM-B); and (b) set out a research agenda for health economic modellers and behavioural/ social scientists to further advance methods to better inform public health policy decisions.MethodsA core multidisciplinary group developed a preliminary toolbox from a published review of the literature and tested this conceptually using a case study of a diabetes prevention simulation. The core group was augmented by a much wider group that covered a broader range of multidisciplinary expertise. We used a consensus method to gain agreement of the PHEM-B toolbox. This included a one-day workshop and subsequent reviews of the toolbox.ResultsThe PHEM-B toolbox sets out 12 methods which can be used in different combinations to incorporate influences on behaviours into public health economic models: collaborations between modellers and behavioural scientists, literature reviewing, application of the Behaviour Change Intervention Ontology, systems mapping, agent-based modelling, differential equation modelling, social network analysis, geographical information systems, discrete event simulation, theory-informed statistical and econometric analyses, expert elicitation, and qualitative research/process tracing. For each method, we provide a description with key references, an expert consensus on the circumstances when they could be used, and the resources required.ConclusionsThis is the first attempt to rigorously and coherently propose methods to incorporate the influences on behaviour into health economic models of public health interventions. It may not always be feasible or necessary to model the influences on behaviour explicitly, but it is essential to develop an understanding of the key influences. Changing behaviour and maintaining that behaviour change could have different influences; thus, there could be benefits in modelling these separately. Future research is needed to develop, collaboratively with behavioural scientists, a suite of more robust health economic models of health-related behaviours, reported transparently, including coding, which would allow model reuse and adaptation.

A Simulation-based Analysis Using Machine Learning Models to Optimize Patient Flow and Treatment Costs

This study investigates the application of discrete event simulation in analyzing patient management and cost dynamics within a hospital system. A simulation that integrates machine learning models, specifically Decision Trees, Random Forests, Support Vector Machines, and Gradient Boosting methods, to predict treatment costs and appointment availability was developed. Conducted over 30 days, the simulation generates synthetic data for training the models. The results are assessed in terms of the total number of patients treated, cumulative costs incurred, and the cost-effectiveness of each predictive model. The findings reveal significant variations in the performance of different machine learning techniques, demonstrating that adopting advanced analytics can substantially improve hospital resource management. This research aims to develop more efficient patient care strategies, contributing to optimizing hospital operations and enhancing patient experiences.

Hospital to community in Wales: What is the value of optometrists playing a greater role in managing neovascular AMD and glaucoma in primary care?

To evaluate the value of enhanced optometric services for managing neovascular age-related macular degeneration (nAMD) and glaucoma in primary care optometry services, instead of hospital eye services (HES). Seven enhanced optometric service pathways in primary care in Wales were assessed with a mixed-methods approach: three for nAMD and four for glaucoma. The methods were a patient-related experience measure (PREM), a Realist Review and Evaluation involving both patients and staff, a discrete event simulation model estimating the economic impact of the pathways and a workforce survey of optometrists to gauge capability and capacity. Patient-related experience measure responses (802) indicated that primary care experience was comparable to that of HES. Utilising enhanced optometric services in primary care resulted in reduced wait times compared with HES, with suspected nAMD shortened to 4-5 days and glaucoma monitoring to 5 days. Waiting lists were dramatically reduced with primary care-based services to just three people waiting for nAMD and five for glaucoma, compared with 216 and 5691 people, respectively, in HES. Consultant ophthalmologist time was reduced from 57% to 15%-16% for nAMD services and from 48% to 22%-23% for glaucoma services. Integrating enhanced optometric services into primary care incurred a similar cost. The workforce survey confirms that optometrists possess the skills and qualifications and are willing to deliver these enhanced optometric services. The Realist Review and Evaluation revealed that clear patient communication, effective coordination and strong interprofessional communication between optometrists and ophthalmologists along with a shared electronic record are crucial to the success of this change. Providing enhanced optometric services in primary care for nAMD and glaucoma brings substantial benefits for the UK National Health Service and patients, including reduced waiting times, waiting lists and released HES capacity. The success of this transition hinges on clear patient communication, administrative co-ordination and effective interprofessional communication.

Runtime integration of machine learning and simulation for business processes: Time and decision mining predictions

Recent research in Computer Science has investigated the use of Deep Learning (DL) techniques to complement outcomes or decisions within a Discrete Event Simulation (DES) model. The main idea of this combination is to maintain a white box simulation model complement it with information provided by DL models to overcome the unrealistic or oversimplified assumptions of traditional DESs. State-of-the-art techniques in BPM combine Deep Learning and Discrete Event Simulation in a post-integration fashion: first an entire simulation is performed, and then a DL model is used to add waiting times and processing times to the events produced by the simulation model.In this paper, we aim at taking a step further by introducing Rims (Runtime Integration of Machine Learning and Simulation). Instead of complementing the outcome of a complete simulation with the results of predictions a posteriori, Rims provides a tight integration of the predictions of the DL model at runtime during the simulation. This runtime-integration enables us to fully exploit the specific predictions while respecting simulation execution, thus enhancing the performance of the overall system both w.r.t. the single techniques (Business Process Simulation and DL) separately and the post-integration approach. In particular, the runtime integration ensures the accuracy of intercase features for time prediction, such as the number of ongoing traces at a given time, by calculating them during directly the simulation, where all traces are executed in parallel. Additionally, it allows for the incorporation of online queue information in the DL model and enables the integration of other predictive models into the simulator to enhance decision point management within the process model. These enhancements improve the performance of Rims in accurately simulating the real process in terms of control flow, as well as in terms of time and congestion dimensions. Especially in process scenarios with significant congestion – when a limited availability of resources leads to significant event queues for their allocation – the ability of Rims to use queue features to predict waiting times allows it to surpass the state-of-the-art. We evaluated our approach with real-world and synthetic event logs, using various metrics to assess the simulation model’s quality in terms of control-flow, time, and congestion dimensions.

Cost-effectiveness of screening with transcriptional signatures for incipient TB among U.S. migrants.

Host-response-based transcriptional signatures (HrTS) have been developed to identify "incipient tuberculosis (TB)". No study has reported the cost-effectiveness of HrTS for post-arrival migrant screening programs in low-incidence countries. To assess the potential health impact and cost-effectiveness of HrTS for post-arrival TB infection screening among new migrants in the United States. We used a discrete-event simulation model to compare four strategies: (1) no screening for TB infection or incipient TB; (2) 'IGRA-only', screen all with interferon gamma release assay (IGRA), provide TB preventive treatment for IGRA-positives; (3) 'IGRA-HrTS', screen all with IGRA followed by HrTS for IGRA-positives, provide incipient TB treatment for individuals testing positive with both tests; and (4) 'HrTS-only', screen all with HrTS, provide incipient TB treatment for HrTS-positives. We assessed outcomes over the lifetime of migrants entering the U.S. in 2019, assuming HrTS met the WHO Target Product Profile (TPP) optimal criteria. We conducted sensitivity analyses to evaluate the robustness of results. The IGRA-only strategy dominated the HrTS-based strategies under both healthcare sector and societal perspectives, with an incremental cost-effectiveness ratio of $78,943 and $89,431 per quality-adjusted life-years (QALY) gained, respectively. This conclusion was robust to varying costs ($15-300) and characteristics of HrTS, and the willingness-to-pay threshold ($30,000-150,000/ QALY gained), but sensitive to the rate of decline in TB progression risk after U.S. entry. Our findings suggest that HrTS meeting the WHO TPP is unlikely to be a cost-effective component of post-arrival screening for migrants entering the U.S.

Assumptions and simplifications in discrete-event simulation modelling

ABSTRACT All discrete-event simulation models are approximations of the real system that they represent. It is through making assumptions and incorporating simplifications into such models that they become an approximation. However, despite their importance, the 4definition, role and management of assumptions and simplifications is not well understood. In this paper we explore assumptions and simplifications in discrete-event simulation modelling in detail. Our approach is firstly to explore the existing literature on this topic, and then to draw this together with our extensive experience of real projects to recommend an approach to managing assumptions and simplifications in a simulation study. In doing so, we locate the role of assumptions and simplifications in a simulation study against knowledge acquisition and model abstraction respectively. We define the terms “assumptions” and “simplifications”, also identifying the role of “simplifying assumptions”. An approach to documenting, assessing and treating assumptions and simplifications which has been implemented in real projects is then described; and it is illustrated through application to a Ford Motor Company case study. This paper provides much needed clarity on the topic of assumptions and simplifications in simulation modelling, and it sets out a means for managing assumptions and simplifications during a simulation study.

Application of simulation and machine learning in supply chain management: A synthesis of the literature using the Sim-ML literature classification framework

Stochastic modeling techniques, such as discrete-event and agent-based simulation, are widely used in supply chain management (SCM) for capturing real-world uncertainties. Over the last decade, data-driven approaches like machine learning (ML) have also gained prominence in SCM, employing methods such as supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL). As supply chains grow in complexity, hybrid models combining simulation (Sim) and ML are becoming increasingly common, and the field stands to gain from a structured review of this literature. Towards this, we developed the Sim-ML Literature Classification Framework, which includes a hierarchical taxonomy comprising five SC criteria, 22 Sim-ML classes and over 75 Sim-ML subclasses. We applied this framework to synthesize 99 papers, revealing significant diversity in how Sim-ML models are used to address supply chain challenges. Key findings include the recognition of the breadth of study objectives, identifying various forms of model hybridization achieved by combining discrete/continuous simulation techniques with SL, UL, and RL approaches, and the data flow mechanisms such as sequential and feedback methods employed by the simulation and ML elements of the hybrid model. Our findings also identify some gaps in the literature; for example, optimization is rarely incorporated into Sim-ML models. Also, most studies present Sim-ML models for addressing problems in general supply chains, likely due to the lack of access to industrial data. The review also highlights that Industry 4.0 technologies, such as digital twins and blockchain, are underrepresented in current research, as are topics like sustainability and transportation. These gaps suggest significant opportunities for future research. We provide guidelines for practitioners on applying Sim-ML models to manage supply chain drivers, mitigate the impact of disruptions, and integrate emerging technologies. Our review serves as a valuable resource for researchers, practitioners, and students interested in leveraging Sim-ML approaches in SCM.

Dynamic modeling and real-time performance analysis of multi-product batch manufacturing systems with perishable products

Production lines handling perishable goods in industries such as petrochemical and food processing demand meticulous precision in timing. These systems are governed by stringent timeframes, where products must strictly adhere to defined residence times. Exceeding these time constraints leads to product scrapping, posing a significant threat to overall productivity. Modeling such intricate systems presents a formidable challenge, and success hinges upon a deep understanding of the complex interplay between production and batch scrapping dynamics. In response to this challenge, this paper presents a novel model for a multiproduct batch production line with perishable products. Furthermore, the real-time performance metric of Permanent Production Loss (PPL) is extended to this system using a method of PPL back-adjustment in time. To validate the fidelity of the proposed mathematical model, numerical case studies are conducted, comparing the developed model to a discrete event simulation. The results demonstrate that the model accurately represents the system at hand. The expressions for PPL evaluation and attribution are also validated using case studies.

Timing of parathyroidectomy after kidney transplantation: A cost-effectiveness analysis

IntroductionParathyroidectomy is the definitive treatment for tertiary hyperparathyroidism post-kidney transplantation. However, cinacalcet-based medical management is increasingly used as an alternative. The financial consequences of each treatment remain unclear. We aimed to identify the most cost-effective strategy for managing hyperparathyroidism from the kidney transplantation recipient's perspective. MethodsWe constructed a patient-level discrete event simulation model to compare parathyroidectomy and cinacalcet-based medical management. The effects of hyperparathyroidism on allograft survival and all-cause mortality were considered in the discrete event simulation model with a time horizon of 15 years. Our base case was a 55-year-old kidney transplantation recipient with persistent hyperparathyroidism and hypercalcemia. The primary outcome was the cost-effectiveness measured by cost per quality-adjusted life years. ResultsThe monthly out-of-pocket cost of cinacalcet ranged from $12 to $288, depending on insurance coverage, with a base case cost of $150. Our base case analysis showed that parathyroidectomy was the dominant treatment with lesser cost ($1,315 vs $7,147) and greater effectiveness (3.17 quality-adjusted life years and 2.92 quality-adjusted life years) than cinacalcet. One-way sensitivity analysis on the cinacalcet treatment duration showed that parathyroidectomy became more cost-effective at 9 months. Two-way sensitivity analysis on the cost of cinacalcet and the duration of treatment with cinacalcet showed that as the monthly cost of cinacalcet increases, the expense of cinacalcet-based medical management quickly exceeds the cost of parathyroidectomy. ConclusionParathyroidectomy becomes more cost-effective for kidney transplantation recipients with tertiary hyperparathyroidism when they require cinacalcet-based medical management for more than 9 months. As part of shared decision-making, it is important to discuss the financial costs involved in treating tertiary hyperparathyroidism.

A dynamic resilience management framework for deep-tier supply networks

The unprecedented supply chain disruptions caused by COVID-19 has had severe operational and financial consequences to firms across industries and continents. While tactical reactionary strategies can help, firms are in need of proactive management approaches to design more resilient supply chain networks in the first place. Firms are looking for an effective framework to design and monitor supply networks, mitigate disruption consequences, and manage resilience under different scenarios. We propose a framework to manage the resilience of deep-tier automotive supply networks by integrating a simulation-based resilience assessment scheme for effectiveness with an efficient optimization-based framework to find optimal strategies for handling regular disruption events. The framework promotes network analysis techniques combined with discrete-event simulation informed by secondary data sources and global supply risk databases for improving resilience management. We validate the effectiveness of the proposed framework using a real-world global automotive original equipment manufacturer case study. Our results demonstrate that the proposed dynamic framework relying on deep-tier visibility can optimize resilience strategies through all key performance indicators. The results show an average of 35% and 40% reductions in back-ordered cost and shipment delays, respectively, with a marginal growth in holding cost when the proposed framework is implemented with deep-tier visibility.