Discrete Event Simulation Research Articles

Integrating discrete event simulation and data envelopment analysis for system performance and efficiency evaluation

ABSTRACT Healthcare facilities, particularly outpatient clinics, are crucial for local communities’ medical needs. To enhance their performance, these facilities need to optimize their operations. Key system performance metrics, such as patient throughput, waiting times and cycle times, can quantitatively be measured using discrete event simulation (DES). DES replicates facility structures and behaviours, offering a valuable platform for assessing the configuration effects of resources on system performance. However, current DES tools cannot identify the best resource configurations for future performance improvement. To address this issue, this paper develops a DES model for an outpatient clinic and improves its performance through data envelopment analysis (DEA). The case study involves a clinic in Northern Malaysia, where relevant data were observed and collected. The analysed data were then input into the DES model. The model measured the clinic’s current performance and assessed the impact of its resource configurations. 37 different resource configurations were tested, and their relative efficiencies were evaluated using DEA. The analysis revealed room for improvement in resource allocation.

Integrating storage allocation with manual order picking and replenishment operations in a distribution centre

ABSTRACT This paper introduces a mathematical programming formulation and a simulation-based heuristic for the allocation of storage positions to products picked by human operators on man-aboard vehicles traveling through the warehouse of a wholesale company. In this problem, the tactical level of the assignment decisions affects the operational level of the picking process. We propose a simulation-optimisation framework that integrates the two. Our formulation of the storage location assignment problem also handles the constraint according to which a picking position should be paired with a (vertical) replenishment position for a given item. To solve realistic instances, we design an iterated local search (ILS) metaheuristic with an embedded discrete-event simulator (DES) that evaluates the most promising moves at each iteration. The DES allows reproducing the handling operations performed by multiple order pickers under uncertainty, mutual interferences and congestion-related phenomena. Overall, the flexible simulation-optimisation (SO) framework evaluates the operational times and daily productivity of the order picking organisation. Numerical results are presented for real data, under an S-shape picking policy with a skip-and-go rule to deal with lacking items. Under a proper tuning of the ILS parameters, the SO framework allows to achieve a nearly 17% improvement in warehouse productivity.

Production and changeover control of textile and PET recycling

Circular economies have become a strong candidate for addressing environmental challenges by managing end-of-life products to reduce landfill waste. We herewith focus on the recycling of PET from plastic waste and textiles. This paper focuses on recycling PET from plastic waste and textiles and proposes a model for controlling plant operations, emphasizing Quality-Based Changeovers over cleaning to ensure production continuity. This paper also identifies technological and managerial challenges in PET recycling plants as raised in the related literature, such as the need for technology improvement, more effective collection routes and sorting processes, and devoted managerial strategies. Since a relevant industrial need is to manage the changeovers, we develop a model to control the operative management of the plant and to find the feedstock quality to be processed at a given time for profit maximization. A discrete event simulation model is built to represent the behavior of the system under an approximate state-based control policy, i.e. a two-threshold policy. Numerical results and sensitivity analysis highlight the impact of cleaning costs on system behavior, offering insights into optimal operational conditions.

Requirements for upgrading trusted nodes to a repeater chain over 900 km of optical fiber

Abstract We perform a numerical study of the distribution of entanglement on a real-world fiber grid connecting the German cities of Bonn and Berlin. The connection is realized using a chain of processing-node quantum repeaters spanning roughly 900 kilometers. Their placement is constrained by the fiber grid we consider, resulting in asymmetric links. We investigate how minimal hardware requirements depend on the target application, as well as on the number of repeaters in the chain. We find that requirements for blind quantum computing are markedly different than those for quantum key distribution, with the required coherence time being around two and a half times larger for the former. Further, we observe a trade-off regarding how target secret-key rates are achieved when using different numbers of repeaters: comparatively low-quality entangled states generated at a high rate are preferred for higher numbers of repeaters, whereas comparatively high-quality states generated at a lower rate are favored for lower numbers of repeaters. To obtain our results we employ an extensive simulation framework implemented using NetSquid, a discrete-event simulator for quantum networks. These are combined with an optimization methodology based on genetic algorithms to determine minimal hardware requirements.

The Role of Discrete Event Simulation for Assembly Planning

Abstract Montagelinien sind häufig sehr komplex und ihre Ausgestaltung hat direkten Einfluss auf Ausbringung und Margen. Die diskrete Eventsimulation (DES) gilt als ein leistungsstarkes Werkzeug zur Planung, Bewertung und Optimierung von Montageprozessen. Dieser Beitrag untersucht einen Ansatz für die Verwendung von DES für die Planung einer Montagelinien auf der Grundlage einer morphologischen Analyse. Im Fallbeispiel steht nicht die Qualität des Simulationsmodells im Vordergrund, sondern die Frage, wann dessen Einsatz sinnvoll ist.

Cost-effectiveness of diagnostic imaging modalities in symptomatic patients with lower limb peripheral arterial disease: discrete event simulation model.

Lower limb peripheral arterial disease in the symptomatic stage has a significant effect on patients´ functional disability. Before an intervention, an imaging diagnostic examination is necessary to determine the extent of the disability. This study evaluates cost-effectiveness of duplex ultrasonography (DUS), digital subtraction angiography (DSA), computed tomography angiography (CTA) and magnetic resonance angiography (MRA) in the diagnostics of symptomatic patients with lower limb peripheral arterial disease indicated for endovascular or surgical intervention. Discrete event simulation was used to capture lifetime costs and effects. Costs were calculated from the perspective of the health care payer, and the effects were calculated as quality-adjusted life year's (QALY's). The cost-effectiveness analysis was performed to pairwise compare CTA, MRA and DSA with DUS as the baseline diagnostic modality. A scenario analysis and probabilistic sensitivity analysis were carried out to evaluate the robustness of the results. In the basic case, the DUS diagnostic was the least expensive modality, at a cost of EUR 10,778, compared with EUR 10,804 for CTA, EUR 11,184 for MRA, and EUR 11,460 for DSA. The effects of DUS were estimated at 5.542 QALYs compared with 5.554 QALYs for both CTA and MRA, and 5.562 QALYs for DSA. The final incremental cost-effectiveness ratio (ICER) value of all evaluated modalities was below the cost-effectiveness threshold whereas CTA has the lowest ICER of EUR 2,167 per QALY. However, the results were associated with a large degree of uncertainty, because iterations were spread across all cost-effectiveness quadrants in the probabilistic sensitivity analysis. For imaging diagnosis of symptomatic patients with lower limb peripheral arterial disease, CTA examination appears to be the most cost-effective strategy with the best ICER value. Baseline diagnostics of the DUS modality has the lowest costs, but also the lowest effects. DSA achieves the highest QALYs, but it is associated with the highest costs.

Insights on the Optimization of Short- and Long-Term Maintenance Decisions for Floating Offshore Wind Using Nested Genetic Algorithms

The present research explores the optimization of maintenance strategies for floating offshore wind (FOW) farms using nested genetic algorithms. The primary goal is to provide insights on the decision-making processes required for both immediate and strategic maintenance planning, crucial for the viability and efficiency of FOW operations. A nested genetic algorithm was coupled with discrete-event simulations in order to simulate and optimize maintenance scenarios influenced by various operational and environmental parameters. The study revealed that short-term maintenance timing is significantly influenced by wind conditions, with higher electricity prices justifying on-site spare parts storage to mitigate operational disruptions, suggesting economic incentives for maintaining on-site inventory of spare parts. Long-term strategic findings emphasized the impact of planned intervals between inspections on financial outcomes, identifying optimal strategies that balance operational costs with energy production efficiency. Ultimately, this study highlights the importance of integrating sophisticated predictive models for failure detection with real-time operational data to enhance maintenance decision-making in the evolving landscape of offshore wind energy, where future farms are likely to operate farther from onshore facilities and under potentially highly varying market conditions in terms of electricity prices.

Analyzing economic effect on mRNA vaccine inventory management with redistribution policy

This study focused on the challenges posed by the fluctuating demand for COVID-19 vaccines, considering factors such as side effects, religious objections, and absenteeism, which result in the accumulation of excess vaccines. Recognizing the resulting social, economic, and environmental issues, this study investigated the application of a lateral transshipment policy for the management of the inventory of short-term vaccines, considering related unpredictabilities. A discrete event simulation built on foundational principles derived from a mixed-integer linear programming model was employed to explore the dynamics of mRNA-based vaccine distribution among two hospitals based on lateral transshipment and reordering policies. Through the simulation of various scenarios over periods of 1-30 days, transshipment based on the availability policy is employed to determine the quantity of vaccines to be transshipped, constrained to vial amounts, and the (s, S) inventory system for reordering. The results of this study underscore the efficacy of lateral transshipment, particularly in situations where demand discrepancies exist between hospitals, thereby revealing its superiority over non-transshipment strategies within 7 days.

Smoothly pass the parcel: implementing the theory of swift, even flow

This research examines the application of the Theory of Swift, Even Flow (TSEF) by a distribution company to improve the performance of its processes for parcels. TSEF was deployed by the company after experiencing lean improvement fatigue and diminishing returns from the time and effort invested. This case study combined quantitative and qualitative approaches to develop a good understanding of the operation. This approach enabled the business to utilise Discrete Event Simulation (DES), which facilitated the implementation of TSEF. From this study, the development of a novel DES application revealed the primacy of process variation and throughput time, key factors in TSEF, in driving improvements. The derived DES approach is reproducible and demonstrates its utility with production improvement frameworks. TSEF, through the visualisations and analysis provided by DES, broadened the scope of improvements to an enterprise level, therefore assisting the business managers in driving forward when lean improvement techniques stagnated. The impact of the research is not limited to the theoretical contribution, as the combination of DES and TSEF led to significant managerial insights on how to overcome obstacles and substantiate change.

Managing Constraints: A Method To Improve The Accessibility And Quality Of A Brazilian Public Emergency Hospital

Accessibility to public hospitals in Brazil is a serious problem and there is much to be done to improve the quality of care. The private care system is expensive and only a small percentage of the population can afford health care. The consequence is that most emergency hospitals are constantly overcrowded. Patients must wait to start treatment and delays can lead to an increased risk of sequelae or even represent the difference between life and death. Reducing waiting times for treatment is considered an important measure to control costs and improve service efficiency. The objective of this article is to present a method that aims to improve the access and quality of the emergency hospitalization system of a municipal public hospital in the State of Rio de Janeiro, Brazil. The method is based on the authors' experience in health services operational research and combines the theory of constraints and discrete event simulation. It is hoped that this article will contribute to administrative knowledge and the results can be implemented in other national public hospitals

A hybrid simulation approach to predicting noise in restaurants

High levels of noise are a well-known source of occupant discomfort in dining environments, and noise ranks highly among the most common customer complaints in restaurant reviews. Prior work has focused on measuring existing restaurant soundscapes and attempting to predict acoustic performance; however, current techniques are still limited in accuracy for restaurants with time-varying occupancy and complex layouts. This presentation demonstrates a novel approach to predicting ambient noise in restaurants via a hybrid simulation framework. Acoustical computations for a given restaurant space are performed in a room acoustics model, while a discrete event simulation (DES) model is used to simulate the arrival patterns of patrons and their conversational behaviors. Predictions from the room acoustics model inform the DES model, allowing for noise level predictions at each seat position. Results from the prediction model are validated against measurements in a case study restaurant. Further development of this framework may support the practical assessment of design interventions in any restaurant while also accounting for time-varying patterns of occupancy.

Applying lean production system philosophy to reduce patient waiting time in healthcare services: Simulation-based optimization and validations through experiment

Value stream mapping (VSM) is an initial and critical tool for deploying lean concepts in the manufacturing and service sectors, including hospitals. Therefore, the aim of this research is to analyze the current working environment of a radiology department, identify the wastes in the operations by mapping the current patient flow, and test the best possible improvement plan with the help of discrete event simulation modeling using the Arena platform to develop an optimum future map to mitigate the wastes. Therefore, the current patient flow in the x-ray section of a radiology department was mapped using VSM for two types of patients named routine (RP) and procedure (PP). VSM results highlighted a high waiting time (TWT) of 188 and 3732 min for RP and PP. A total of seven scenarios were developed and analyzed to determine the impact of changes on five key performance indicators. Simulation results of the current scenario showed that there were 567 patients coming out of the system with a lead time of 200 and 926 min for RP and PP. Total waiting time obtained for RP and PP was 177 and 860 min, with a maximum wait time of 31 min/patient and an average of 43 patients in the system (WIP). The simulation results of the seventh scenario provided the best results, with 6% improvement in the number of patients getting out of the system and 75% and 55% decreased lead time for RP and PP. Total waiting time for RP and PP was reduced by 80% and 60%, with WIP falling by 62%. Arena Optimizer showed that by adding a technician and radiologist to the system, the optimum patient waiting time in the queue would be 6 min, and the lead time for RP and PP would be (160) and (884 min), respectively. Based on it, a future VSM was created; it provided a reduction of 72% and 60% in (TWT) for RP and PP, which could improve the number of patients to be entertained by the radiology services without adding any new x-ray machines. The results of the present case provide healthcare planners with a road map to discover the waste in operations since the monetary investment and organizational dedication required drive out non-value activities. Therefore, simulation helps to select the best possible improvement strategy for the system without pausing and making physical changes in busy and complex units of hospitals.

ENTIMOS: Decision Support Tool Highlights Potential Impact of Non-intravenous Therapies for Multiple Sclerosis on Patient Care via Clinical Scenario Simulation.

Administration of intravenous (IV), high-efficacy treatments (HETs) for the treatment of multiple sclerosis (MS) poses a high resourcing and planning burden on infusion centres, resulting in treatment delays that may increase the risk of breakthrough disease activity. Simulation tools can be used to systematically analyse capacity scenarios and identify and better understand constraints, therefore enabling decision-makers to optimise patient care. We have previously applied ENTIMOS, a discrete event simulation model, to assess scenarios of patient flow and care delivery using real-life data inputs from the neurology infusion suite at Charing Cross Hospital London. The model predicted that, given current capacity and projected demand, patients would experience high-risk treatment delays within 30 months. This study aimed to address key healthcare challenges for MS patient care management as seen from a neurologist's perspective. We used ENTIMOS to predict the impact of several distinct and clinically plausible scenarios on patient waiting times at the same MS infusion suite and to quantify mitigation strategies needed to assure continuity of care. We used real-world experience of an expert neurologist to identify five clinical scenarios: (1) switching patients to a subcutaneous (SC) MS treatment of the same therapeutic agent, in the same hospital setting; (2) extending opening times to the weekend; (3) reducing the number of infusion chairs (to simulate social distancing measures applied during the coronavirus disease 2019 [COVID-19] pandemic); (4) increasing demand for infusions; and (5) increasing the scheduling approval time. Patient waiting time for next due infusion and time to high-risk treatment delays (≥ 30 days) were the main analysed model outputs. Previously published base case results were used as reference. All hypothetical scenarios were run over a 3-year horizon (with the exception of scenario 1, which was run over a 3- and 5-year horizon). Strategies to mitigate treatment delays were analysed and discussed. Switching 50% of patients to SC treatment of the same therapeutic agent administered in hospital postponed the predicted high-risk treatment delays to shortly beyond the 3-year simulation timeframe (month 38). Weekend opening reduced waiting times from 20 days to 4 days and prevented treatment delays, however, at elevated labour costs. Reducing the infusion chairs increased waiting time to 53 days on average and 86 days at the end of the simulation, leading to high-risk treatment delays within 6 months. An increased demand for infusions increased waiting time to 26 days on average and 47 days at the end of the simulation, leading to high-risk treatment delays within 22 months. Prolonged scheduling approval time did not reduce the waiting time, nor postpone the high-risk treatment delays. Decision makers need transparency on capacity constraints to better plan resourcing at infusion suites and MS treatments. Our results showed that various mitigation measures, such as increasing capacity by additional infusion chairs per year and transferring patients to other infusion suites, may help prevent treatment delays. Switching patients from IV to an SC version of the same therapeutic agent reduced the waiting time moderately and postponed high-risk treatment delays. It was insufficient to prevent high-risk treatment delays in the long term.

Assessing Inland Waterway Transport (IWT) container logistics on the Rhine Alpine corridor: A discrete event simulation approach

Inland Waterway Transport (IWT) is pivotal for hinterland freight logistics, connecting numerous inland terminals with major deep-sea ports. Despite its significant advantages in cargo capacity and environmental sustainability, it remains underutilized due to operational inefficiencies, diminishing its competitive edge compared to road and rail transport. Addressing these challenges requires innovative solutions, yet existing studies often neglect to evaluate these innovations and their interactions within the broader IWT logistics framework and other transport modes.This study bridges these gaps by introducing a comprehensive Discrete Event Simulation (DES) model to capture the dynamic interactions within the IWT logistics system, its interplay with other transport modes, and the potential impact of innovative measures on IWT performance. The model simulates the current container logistics system along the Rhine-Alpine (RALP) corridor, incorporating key elements such as cost-time calculations, transport mode selection, network flow allocation, and the assessment of IWT innovations.The methodological framework and architecture of the DES model are presented, emphasizing verification and validation processes to ensure accuracy and reliability. The model outputs a network-wide analysis of the current IWT logistics system, examining mode split, cost, time, emissions, distance, and the interrelationships among these factors. This analysis serves as a benchmark for evaluating the effects of various innovative strategies on IWT performance.By employing the DES methodology, this research advances the understanding of container IWT logistics, providing critical insights for stakeholders and policymakers. It evaluates the current performance of container IWT in the RALP corridor and identifies opportunities to improve the efficiency and competitiveness of container IWT.

Cost-effectiveness of monitoring ocular hypertension based on a risk prediction tool

Background/AimsTo assess the cost-effectiveness of making treatment decisions for patients with ocular hypertension (OHT) based on a risk prediction (RP) tool in the United Kingdom.MethodsA discrete event simulation model was...

Modeling hospital resources based on global epidemiology after earthquake-related disasters

Injured people require hospital emergency services and timely medical treatment after extreme earthquakes. Earthquake-related patients often have trauma injuries and stress-linked (ischemic) ailments that require multiple healthcare procedures, such as minor orthopedic treatment, surgical treatment of fractures, and thrombolysis or thrombectomy. Hospital operation models have been proposed to examine these healthcare procedures; however, they exhibit two fundamental gaps that hinder their ability to assess critical service areas after earthquakes. First, these models rest heavily on emergency procedures based on injury severity rather than type. Second, healthcare demands are often modeled from injury profiles after moderate earthquakes in the United States without including epidemiology data after large earthquakes globally. This approach has led to oversimplified hospital emergency services and resource utilization representation. This research presents a new hospital operations model based on patient injury type and worldwide earthquake epidemiology to fill these gaps. We build the model using discrete-event simulations to capture dynamic metrics on hospital operational outcomes after the earthquake, such as patient time-to-treatment and unassisted patient ratio. We then studied how these metrics vary with different levels of functional capacity in the specific hospital resources. Our results showed that waiting times for emergency department (ED)-level patients vary non-linearly with changes in the number of functional service areas. Also, significant reduction in the waiting time for hospital-level procedures was found for relatively small decrease in the bed occupancy rate, for example, if reverse triage procedures are activated (i.e. a discharge of non-critical patients admitted before the earthquake). Our findings provide a valuable tool for decision-making in hospital preparedness as they explicitly measure the impacts of functional capacity on key healthcare metrics for specific earthquake-related patients.

Hardware requirements for trapped-ion-based verifiable blind quantum computing with a measurement-only client

Abstract In blind quantum computing (BQC), a user with a simple client device can perform a quantum computation on a remote quantum server such that the server cannot gain knowledge about the computation. Here, we numerically investigate hardware requirements for verifiable BQC using an ion trap as server and a distant measurement-only client. While the client has no direct access to quantum-computing resources, it can remotely execute quantum programs on the server by measuring photons emitted by the trapped ion. We introduce a numerical model for trapped-ion quantum devices in NetSquid, a discrete-event simulator for quantum networks. Using this, we determine the minimal hardware requirements on a per-parameter basis to perform the verifiable BQC protocol. We benchmark these for a five-qubit linear graph state, with which any single-qubit rotation can be performed, where client and server are separated by 50 km. Current state-of-the-art ion traps satisfy the minimal requirements on a per-parameter basis, but all current imperfections combined make it impossible to perform the blind computation securely over 50 km using existing technology. Using a genetic algorithm, we determine the set of hardware parameters that minimises the total improvements required, finding directions along which to improve hardware to reach our threshold error probability that would enable experimental demonstration. In this way, we lay a path for the near-term experimental progress required to realise the implementation of verifiable BQC over a 50 km distance.

Developing and validating a discrete-event simulation model of multiple myeloma disease outcomes and treatment pathways using a national clinical registry.

Multiple myeloma is a haematological malignancy typically characterised by neoplastic plasma cell infiltration of the bone marrow. Treatment for multiple myeloma consists of multi-line chemotherapy with or without autologous stem cell transplantation and has been rapidly evolving in recent years. However, clinical trials are unable to provide patients and clinicians with long-term prognostic information nor policymakers with the full body of evidence needed to perform economic evaluation of new therapies or make reimbursement decisions. To address these limitations of the available evidence, this study aimed to develop and validate the EpiMAP Myeloma model, a discrete-event simulation model of multiple myeloma disease outcomes and treatment pathways. Risk equations were estimated using the Australian and New Zealand Myeloma & Related Diseases Registry after multiple imputation of missing data. Risk equation coefficients were combined with multiple myeloma patients at diagnosis from the Registry to perform the simulation. The model was validated with 100 bootstraps of an out-of-sample prediction analysis using a 70/30 split of the 4,121 registry patients diagnosed between 2009 and 2023, resulting in 2,884 and 1,237 patients in the training and validation cohorts, respectively. For 90% of the 120 months in the 10-year post-diagnosis period, there was no significant difference in overall survival between the validation and simulated cohorts. These results highlight that the EpiMAP Myeloma model is robust at predicting multiple myeloma disease outcomes and treatment pathways in Australia & New Zealand. In the future, clinicians will be able to use the EpiMAP Myeloma model to provide personalised estimates of life expectancy to patients based on their specific characteristics, disease stage, and response to treatment. Policymakers will also be able to use the model to perform economic evaluation, to forecast the number of patients receiving treatment at different stages, and to determine the downstream impact of listing new, effective therapies.

A discrete-event simulation study for pre-hospital emergency systems

ABSTRACT Ambulance dispatch, deployment, and relocation are crucial decisions in pre-hospital emergency medical services (EMS) that significantly impact response times. In metropolitan areas, the rapid movement of people and complex traffic conditions create a challenging environment for effective decision-making. This paper presents a discrete-event simulation framework designed to evaluate and improve pre-hospital emergency systems by capturing core EMS workflows and incorporating comprehensive, dynamic data. To demonstrate the practical application of this framework, a simulation instance is implemented and validated in the context of downtown Shanghai, with an analysis of key performance indicators for EMS in this area. Additionally, we propose a dynamic ambulance relocation model to improve the alignment between ambulance deployment plans and emergency calls. Simulation experiments show that dynamic ambulance relocation effectively reduces response times. Due to its ability to integrate real-time data, this framework has the potential to support immediate decision-making and evaluate proposed strategies in the real world.

A simulation-based approach to analysing delays in the transport of critically ill neonates

ABSTRACT Neonatal interfacility transport ensures that critically ill neonatal patients can receive higher levels of care when needed. Delays in the transport process impact the quality of care and increase the risk of medical complications. The objective of this study is to investigate the operations-related factors that contribute to transport delays and explore the role of discrete-event simulation in improving the transport process. Semi-structured interviews were conducted with stakeholders to understand the neonatal interfacility transport process. Analysis of historical call logs and transport data was performed to identify inputs to the discrete-event simulation model. Statistical tests were used to identify the effect of various factors on wait time and transport time in the simulation model. High patient volume and limited bed capacity at the receiving hospitals are identified as bottlenecks that lead to extended wait time and transportation time. Additionally, having more geographically distributed ambulance resources does not significantly help with the time delays when the receiving hospital capacity stays unchanged. Discrete-event simulation models can be used to investigate the effects of operations-related factors in the interfacility transport of critically ill neonates to support future process improvement.