AI-Driven Circular Manufacturing Framework for Predictive Material Flow Optimisation

A discrete event simulation model approach for probabilistic estimates of helicopter search and rescue times to maritime incidents in the Canadian Arctic

Small Scale Metal Industries (SMI) require huge amounts of energy to function. Although small-scale industries play a vital role in contributing to the economic development of the nation by way of exporting manufactured goods, the use of outdated systems and equipment results in a loss of energy efficiency and a lack of visibility and transparency of the process flow. As a solution to the problems that arise from poor digital integration in such processes, this paper presents a framework referred to as DLCMF (Digital Life Cycle Management Framework) for energy-intensive small and medium enterprises. In order to monitor the consumption and energy flow in the processes, process mining, real-time analytics, and discrete event simulations have been incorporated into the framework. In the context of this research, a simulation of a Machining industry in the state of Maharashtra was undertaken using the software Flexsim, which has been designed with Industry 4.0 and IoT capabilities. It has proven to be effective in reducing the consumption of energy (by 22%) and the amount of materials used (17%). In addition to this, the model facilitates a seamless integration process with respect to smart sensors, PLCs, and ERP systems, resulting in digital transformation within traditional manufacturing settings. The results resonate well with the Sustainable Development Goals, especially SDG 9, SDG 7, and SDG 13, due to improved energy efficiency, cleaner energy usage, and lower emissions of greenhouse gases. The paper provides good implications for policymakers, SME owners, and researchers who would want to align small-scale industrial practices with global sustainability objectives.

ABSTRACT The age distribution of disease-causal human papillomavirus (HPV) infections that lead to high-grade cervical intraepithelial neoplasia (CIN2+) is not well understood in Europe. The objective of this study was to estimate the age at acquisition of disease-causal HPV infection in women diagnosed with CIN2+ in Denmark, Estonia, Norway, Slovenia, and Sweden. A previously published discrete event simulation model was modified to estimate the age of disease-causal HPV infection for females diagnosed with CIN2+ by comparing predicted and observed age distributions of CIN2+ diagnoses using a chi-square goodness-of-fit test. Scenario and sensitivity analyses were also performed. The predicted median age (years) of disease-causal HPV infection was 23.69 in Denmark, 26.73 (narrow case definition for CIN2+ diagnosis) and 28.34 (broad definition) in Estonia, 29.32 in Norway, 32.03 in Slovenia, and 26.33 in Sweden. The percentage of women aged over 26 and 45 years with a predicted causal HPV infection was 42.7%/8.1% (26/45 years) in Denmark, 52.8%/9.0% (narrow definition) and 57.9%/12.7% (broad definition) in Estonia, 64.7%/11.9% in Norway, 72.3%/21.4% in Slovenia, and 51.2%/9.4% in Sweden. Model results were robust to variations in inputs and parametrization tested in scenario and sensitivity analyses. A considerable proportion (42.7–72.3%) of causal HPV infections are projected to occur in women over age 26, while 8.1%–21.4% are projected to occur in women over the age of 45. These findings highlight a need to consider HPV vaccination for previously unvaccinated women to reduce the burden of CIN2+ and cervical cancer.

Using mixed methods approaches for problem-solving has a long history in Operations Research (OR) and Modelling and Simulation (M&S). Data Science (DS), with its strong alignment with the disciplines of Mathematics, Statistics and Computer Science, has experienced a surge in interest in recent decades and is now increasingly applied in business and management. Similarly, M&S, a sub-field of OR, has a long history of theoretical and practical work in the dynamic modelling of operational systems. Thus, hybrid models employing DS techniques such as supervised machine learning and reinforcement learning with M&S approaches like agent-based modelling and discrete-event simulation enable us to realise synergies associated with multiple methods; their combined application potentially goes beyond what could be possible by employing single techniques. Through a comprehensive survey of 117 researchers and practitioners, our work aims to identify the key challenges and opportunities in developing hybrid models. Our findings suggest that hybrid M&S-DS models can improve model accuracy, reduce computational costs, improve efficiency and potentially lead to improved decision-making. Our study advances the M&S knowledge base by broadening the methodological foundations that current and future researchers engage with, showcasing how combining traditional M&S approaches with emerging DS techniques deepens critical understanding of mixed methods approaches. By capturing both theoretical frameworks and practitioner insights, it supports the development of a more contemporary and comprehensive view of M&S practice.

ABSTRACT Shared autonomous vehicles (SAVs) are increasingly integrated into urban transport, yet effective evaluation tools for fleet sizing and charging infrastructure are lacking. This study develops an agent-based discrete event simulation model to evaluate SAV service levels and costs. Tested on the Manhattan road network, the model simulates interactions between vehicles and infrastructure agents. Key results show: (1) a heuristic charging deployment algorithm improves passenger waiting times compared to even distribution; (2) for small fleets, more charging stations significantly boost performance, though this effect diminishes once fleet size exceeds 6,600; and (3) while increasing charging stations reduces unsatisfied demand for smaller fleets, larger fleets eventually saturate demand regardless of station density. These findings offer insights for optimizing SAV fleet management and infrastructure deployment.

The COVID-19 pandemic placed tremendous strain on medical resources. To assess intervention strategies and resource planning for metropolitan pandemic response, a hybrid System Dynamics–Discrete-Event Simulation (SD-DES) framework is developed, using Wuhan as a case study. With SD capturing macroscopic infection dynamics and policy intervention and DES simulating medical resource scheduling, bidirectional feedback between macro and micro levels enables a more holistic and comprehensive evaluation of policy effects. By simulating 132 representative scenarios, key factors including containment measures, medical resource capacity, mask adoption, vaccine rollout speed, and the timing of external medical support were examined. Our analysis provides a comprehensive perspective on pandemic prevention and control decision-making, an aspect often underrepresented in prior studies. The model offers an extensible computational framework for complex socioeconomic systems, such as pandemic emergencies, where dynamic and process complexities coexist. It can assist policymakers in enhancing the healthcare system’s preparedness and mitigating the spread of the pandemic. Future research could improve model robustness by incorporating advanced parameter estimation techniques, such as particle filtering. To address computational challenges, we also recommend implementing asynchronous module execution and optimized programming to improve simulation efficiency and scalability.

Waiting time is a critical indicator of healthcare operational performance and patient-centered service quality. Although outpatient pharmacies represent the final service node in the care continuum, systematic quantitative evaluation of congestion dynamics remains limited in tertiary care settings in India. This study integrates analytical queuing theory, discrete-event simulation (DES), and cost-effectiveness analysis (CEA) to evaluate waiting time performance in the outpatient pharmacy of a tertiary care teaching hospital in Kerala. Empirical time–motion observations (N = 1,584 encounters) were conducted to estimate arrival and service parameters. The system was modelled as an M/M/4 queue under first- come-first-served discipline and validated using 100 simulation replications. Statistical comparison across three scenarios—baseline (four counters), temporary peak-hour expansion (five counters), and staff redeployment—revealed significant reductions in mean waiting time, F(2, 297) = 184.63, p < .001, η² = .55. Incremental cost-effectiveness analysis demonstrated superior efficiency of peak-hour expansion (Rs.187 per patient-hour saved) compared to permanent staffing expansion (Rs.349 per hour). Findings support demand- responsive staffing strategies and demonstrate the value of integrating operational analytics with economic evaluation in hospital management.

Background: Modular and autonomous rail-based transport concepts promise increased flexibility and efficiency, but their feasibility strongly depends on reliable and scalable terminal handling operations. In such systems, transport units must be safely and rapidly coupled to carrier units without manual intervention. Methods: This study presents a structured pod-handling concept for a modular rail transport system, covering transport unit preparation, crane-based lifting and positioning, mechanical coupling via twist-lock interfaces, and automated electrical and media connections. To evaluate operational performance, a discrete-event simulation model was developed in AnyLogic that represents the complete loading process from order reception to pod dispatch. Results: Simulation results show that a single crane is sufficient under low-demand conditions, maintaining an average processing time of approximately 12 min per order. As demand increases, system performance becomes highly sensitive to crane availability; insufficient resources lead to excessive waiting times. For high-frequency demand, scalable crane allocation is required to preserve stable throughput. Conclusions: The results confirm that automated pod-handling mechanisms, combined with demand-adaptive terminal resources, are essential for the viability of modular rail pod systems. The proposed process model and simulation framework guide terminal design and support the integration of decentralised rail pods into future multimodal mobility and logistics networks.

Post-quantum cryptography (PQC) artifacts are one to three orders of magnitude larger than their classical counterparts and must be segmented via ISO-TP across a shared CAN-FD bus while coexisting with periodic safety-critical traffic. No prior work has quantitatively mapped the transport-level feasibility of these artifacts under realistic multi-electronic control unit (ECU) contention. This paper presents a validated discrete-event simulator and evaluates 29 parameter sets from nine algorithm families—spanning the KpqC final portfolio, NIST FIPS 203–205 standards, and the draft FIPS 206—across 534 scenarios classified as feasible, borderline, or infeasible. Results show that key encapsulation mechanism (KEM) feasibility is scenario-dependent: domain scale and startup coordination dominate over algorithm choice, with 4-ECU staggered deployments feasible for all Level-1 candidates, while 16-ECU simultaneous startup is universally infeasible. For digital signatures, FN-DSA achieves the best transport feasibility due to its compact signature, while HQC is uniformly infeasible and SLH-DSA is nearly uniformly infeasible, quantifying the CAN-FD bandwidth premium of algorithmic diversity. System-side traffic shaping—staggered startup and reserved bus windows—outperforms algorithm substitution as a mitigation strategy. To the best of our knowledge, these findings constitute the first design-space map of PQC artifact transport on CAN-FD and provide actionable deployment guidelines for post-quantum transition.

Abstract As products become more sophisticated and quality demands grow, manufacturing systems are becoming increasingly complex. In particular, for small and medium enterprises (SMEs) operating complex, high-mix, low-volume production systems, manufacturing layout significantly impacts productivity and operational costs. Given this impact, accurate prediction of production performance becomes essential for optimizing layout configurations and resource allocation. In this article, the objective is to develop a decision-support framework for selecting the best production layouts in labor-intensive SME manufacturing systems. The framework uses discrete event simulation (DES) with cellular manufacturing system (CMS) models to generate datasets. And, Gaussian process regression (GPR) is applied to provide probabilistic predictions for key performance variables while accounting for inherent uncertainties. When GPR-based production volume predictions for different layouts result in overlapping prediction ranges, the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) is then utilized to systematically rank the alternatives using multiple key variables as evaluation criteria. A case study demonstrates the framework's effectiveness by comparing three alternative layouts with GPR-based production volume predictions and fuzzy TOPSIS-based decision-making. The multicriteria evaluation systematically ranks the alternatives using key performance variables including adjusted cycle time, total buffer capacity, average manpower efficiency, and number of cells.

BackgroundThe increasing incidence and financial burden of skin cancer place immense pressure on the UK’s National Health Service (NHS). Systemic challenges, including dermatologist shortages and long waiting lists, complicate timely assessment of skin lesions for patients under the urgent suspected cancer pathway. While teledermatology offers an innovative solution compared to traditional face-to-face appointments, standard teledermatology models still face limitations in addressing health care access barriers. Community-based decentralized models may reduce such barriers, but the cost and operational impact of such specific models remain largely underresearched.ObjectiveThis study evaluated the differences in financial cost to the NHS and patient waiting times at the Northern Care Alliance NHS Foundation Trust by comparing a community-based teledermatology model using Pathpoint eDerma against the Trust’s standard-of-care for patients in the urgent suspected skin cancer pathway.MethodsThis study used an ambidirectional design involving 2 distinct analyses. The cost comparison analysis (CCA) compared costs incurred under the teledermatology model (intervention arm, n=563) against the Trust’s standard care, represented by a synthetic comparator arm (n=4011). The discrete event simulation (DES) modeled the operational impact on patient waiting times over a 1-year period. Data for the intervention arm were collected prospectively from December 2022 to May 2023 for CCA and up to November 2023 for DES, while comparator data were collected retrospectively from September 2021 to December 2022. Publicly available resource costs were incorporated to ensure the robustness of the analyses.ResultsThe community-based teledermatology model was associated with significant improvements in both cost to the NHS and patient waiting times. The CCA revealed a mean cost saving of £45 (£1=US $1.24) per referral (95% CI £22-£60; P<.001). This cost saving was associated with a 26% reduction in the proportion of patients requiring a full diagnostic biopsy, falling from 48% (1925/4011) in standard care to 22% (124/563) in the teledermatology model as well as time savings in face-to-face clinics and administration. Furthermore, the DES demonstrated that, on average, the teledermatology pathways decreased the time to reach a clinical diagnosis by 9.90 (95% CI 9.64-10.16) days; to communicate a diagnosis to patients by 54.18 (95% CI 50.76-57.61) days; and to reach a histopathological diagnosis by 62.8 (95% CI 59.76-65.83) days compared to standard care.ConclusionsThe implementation of the community-based teledermatology model appears to be a highly effective, cost-efficient strategy associated with shortened patient journeys. The intervention showed a faster initial triage phase, but the study identified the histopathology process as the next major systemic constraint that could deter further pathway efficiency. Achieving timely diagnosis for all patients, including those requiring diagnostic biopsies, will necessitate continued strategic investment in innovative technologies to accelerate this downstream process.

Discrete event simulation models simulate times to events rather than using the cumulative survival probabilities provided by parametric survival models. This requires inversion of the survival functions to produce analytical solutions to derive these event times from given survival estimates. While numerical methods can approximate event times for more complex survival models, this process may be computationally expensive, especially when repeated over large numbers of simulations. We aimed to derive an analytical solution to inverse functions for Royston/Parmar restricted cubic spline parametric survival models and test the execution speed when implemented in Microsoft Excel against numerical approximation methods (Goal Seek) and a hybrid approach using Brent's root-solving algorithm. Three case types were classified according to the positioning of the given cumulative survival estimate " " between cumulative survival probabilities corresponding to the boundary knots from the Royston/Parmar restricted cubic spline model to determine the positioning of the solution "t" between knot values. For Case 1 (t before first knot) and Case 3 (t after last knot), a linear equation for ln(t) is produced, and single solutions are derived for t as a function of . For Case 2 (between boundary knots), a cubic equation of the form a 3 + b 2 + c + d = 0 is derived, with a published cubic equation-solving algorithm used to obtain the correct solution for t. Royston/Parmar restricted cubic spline models were then fitted to published colon cancer data, and used to test the average execution speed of a user-defined function coded in Visual Basic for Applications (VBA)based on the analytical inversion solution compared to two Goal Seek approaches (default and increased precision) and a hybrid approach using Brent's method in Microsoft Excel over 100 replications of event time simulations, for a range of given survival estimates between 1% and 99% for all fitted models. The mean (standard deviation) execution speed for the spline inversion user-defined function across 100 replications was 0.612 (0.029) seconds compared with 10.567 (0.175) seconds for the default Goal Seek approach, 12.230 (0.265) seconds for the increased precision Goal Seek approach and 1.140 (0.114) seconds for the hybrid Brent method, corresponding to 94.2%, 95.0%, and 46.3% reductions in average execution time, respectively. Analytical solutions to inverse functions of Royston/Parmar restricted cubic spline models can be derived to allow precise estimation of event times from given survival estimates and substantially increase simulation speed for event time generation in Microsoft Excel for discrete event simulation versus approximations using numerical methods, as well as facilitate derivation of a quantile function. Further research should be considered to test event time derivation speed in other software (such as R), extend the solution to time-varying covariates and identify other potential use cases for the analytical inversion solution.

The tightening of Emission Control Area (ECA) and Vessel Speed Reduction (VSR) regulations poses significant challenges for biomass exporters, particularly from emerging economies. These regulations raise transportation costs and reduce competitiveness, threatening the resilience of the biomass supply chain (BSC). This study develops an integrated optimization framework that combines machine learning (ML) (Bayesian XGBoost and Long ShortTerm Memory (LSTM) models) with discrete-event simulation (DES) to enhance supply chain (SC) efficiency under regulatory constraints. Using operational data from twenty-four Vietnamese wood pellet plants and international shipping records (2023–2024), the framework optimizes procurement, inland logistics, maritime transport, and demand planning. Simulation results show that full SC optimization reduces costs by nearly 40%, while combining SC optimization with power plant efficiency innovations achieves up to a 43.1% reduction in total costs and doubles exporter profits compared to the baseline. Beyond economic gains, the approach enables compliance with international environmental regulations without imposing prohibitive costs on exporters. This study contributes a novel methodological framework that bridges ML and simulation for sustainable SC optimization, offering practical guidance for exporters in emerging economies to remain competitive under tightening global environmental policies.

ObjectiveTo quantify the risk of emergency medical resource exhaustion during the early stages of major infectious disease outbreaks and proposed resource allocation optimization strategies.MethodsThis study integrated discrete event simulation and patient flow theory to develop a dynamic medical resource allocation simulation system in Hubei Province from January 23 to February 21, 2020. This period referred to the first 30 days interval following the implementation of the lockdown in Wuhan. The system simulated the utilization of general hospital beds, ICU beds, and ventilators under three distinct resource supply scenarios: baseline (Expected), optimal (Best case), and worst (Worst case). Simulation outputs including cumulative depletion days, waiting time, and deaths attributable to delayed access to critical resources were summarized using descriptive statistical analysis.ResultsIn the Best case scenario, the cumulative depletion days of ICU beds and ventilators persisted for 4 days and 6 days respectively, while no shortages occurred for general beds; 384 deaths were attributable to waiting for resources. In the Expected scenario, the cumulative depletion days of ICU beds, ventilators, and general beds were 11 days, 17 days, and 1 day respectively, with 766 deaths attributable to waiting for resources. In the Worst case scenario, the cumulative depletion days of both ICU beds and ventilators were 28 days, and general beds also experienced severe congestion resulting in an average wait time of 4.18 days, a maximum wait time of 11.28 days, and with 15,029 deaths attributable to waiting for resources.ConclusionThe abrupt surge in cases at the onset of the epidemic exerted considerable pressure on medical resources. The intensity of resource supply is highly correlated with the risk of patient death, and ventilators and ICU beds are the key resources affecting the death risk. This simulation model can provide a scientific tool for emergency resource reserve and allocation in public health emergencies.

This paper presents an end-to-end generative artificial intelligence (Gen-AI) framework for automating the generation, adaptation, and evaluation of discrete-event simulation (DES) models in manufacturing. The approach integrates multiple large language models (LLMs) with a structured blueprint model and targeted human-in-the-loop controls to create executable simulation models from heterogeneous production data, implement targeted modifications, and interpret simulation outcomes. The workflow incorporates prompt engineering, zero- and one-shot implementations, and evaluator–optimizer loops. 21 experimental runs on two industrial case studies from a Swedish automotive manufacturer demonstrate that LLMs can support DES model generation and scenario exploration through a hybrid approach combining automation with human oversight. The results underline both the potential and current limitations of LLM-driven simulation, particularly regarding output consistency and generalizability. Future research should extend the method to more complex manufacturing systems and investigate the role of emerging autonomous Gen-AI tools in simulation-based decision support. • LLM-driven workflow automating discrete-event simulation (DES) model generation, adaptation, and evaluation. • Integrates multiple LLMs with a structured blueprint model and human-in-the-loop oversight. • Systematically tested across 21 runs in two industrial manufacturing case studies. • Demonstrates feasibility for scenario exploration while highlighting challenges in output consistency and reliability.

ABSTRACT Long waiting times in the pre-examination stage of eye clinics can reduce patient satisfaction, while inefficient utilization of staff and equipment reduces operational efficiency. Effective management of this complex system is challenging due to sequential and patient-specific care pathways, non-stationary patient arrivals, and heterogeneous service times. This study employs Discrete Event Simulation (DES) to analyse multiple configurations of the pre-examination rooms, calibrated with real data from a high-volume ophthalmology clinic. The study compares flexible and dedicated resource strategies in terms of two metrics: average patient waiting time and nurse utilization. Three configurations are analysed within this context. Closing one room in the afternoon, which was simple to implement, saves one nurse-day, but has little effect on waiting. Equipping both rooms with flexible resources reduces nurse requirement by half but increases waiting times due to reduced concurrency. Finally, equipping both rooms with dedicated instruments, which reduces total waiting by roughly 20 h per month but requires more nurses. Our results show that simulation-based analysis provides a useful basis for evaluating trade-offs between flexibility, concurrency, and resource utilization, particularly in resource-limited healthcare systems.

Proper allocation of resources within the Emergency Departments (EDs) is very important in enhancing patient outcomes, wait times, and operational efficiency. The following paper introduces a Hybrid Simulation-Optimization Approach, which is a hybrid of the Discrete-Event Simulation (DES) and optimization algorithms in the allocation of resources, including medical staff, beds, and equipment dynamically. The implementation of the model was based on real-life ED data available in a hospital in a metropolitan city, and the model was compared to the traditional cloud-based and heuristic resource allocation models. The findings indicated a great improvement using the Hybrid Simulation-Optimization Model with 85 percent staff utilization, 4.5 patients treated / hour per staff member and 15 percent staff idle time, in comparison to Cloud-Based System that achieved 70 percent staff utilization and the staff-patient ratio was 1:6. More so, the Hybrid Model cut patient waiting time to 10 minutes as compared to the Cloud-Based System that recorded 15 minutes. These results pinpoint the power of the integration of Simulation and optimization to optimize the allocation of staff, manpower, and usage of resources in EDs. The staff-patient ratio was also streamlined to 1:4, which allowed enhancing the balance of work. The results of the staff utilization and patient care time improvements prove the usefulness of real-time information integration and optimization algorithms. Further studies on the subject should aim at extending the method to multiple hospitals, adding real-time data feeds, and machine learning algorithms to better predict demand and allocate resources, eventually enhancing the management of healthcare resources in a wide range of EDs.

Zone-based and flexible bay layout concepts are essential tools for facility designers, particularly in job shop manufacturing environments characterised by medium-to-high product variety and flexible routing. While traditional approaches focus primarily on optimising zone dimensions and allocating departments within zones, this paper advances the design process by investigating the subsequent steps required to finalise layout configurations in zone-based job shop systems. Specifically, these steps include identifying material-handling input/output points along cell boundaries, determining appropriate aisle dimensions, and selecting suitable material-handling transporters. To address these interrelated decisions, the study proposes a cascading methodology that integrates design of experiments (DOE), discrete-event simulation, supervised deep learning, and multi-objective optimisation. The optimisation process employs the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) in conjunction with an adaptive direct-search strategy implemented through NOMAD, a black-box optimisation solver. A numerical case study of a zone-based job shop system is presented. Computational results show that the optimised layouts achieve substantial reductions in material-handling time and work-in-process (WIP); for instance, one solution reduces average transport time by approximately 25% and WIP by approximately 35%, with only a modest increase in handling cost. These findings highlight the practical value of the proposed multi-objective approach.

Surgical Case Sequencing and Patient Flow: A Simulation Study With Downstream Resources.