Simulation-based Approach Research Articles

Data Envelopment Analysis-based Scenario Selection for Sequencing Pattern in a Simulated Robotic Cell

In this study, the performance of suggested scenarios for part input sequences in a 3-machine robotic cell producing different parts is determined through the application of data envelopment analysis (DEA) and the Banker–Charnes–Cooper model. A single gripper robot supports the manufacturing process by loading and unloading products and moving them inside the system. This study addresses random machine failures and repairs to minimize cycle time based on two robot move cycles in a three-machine robotic cell and overall production costs. Here, simulation assists in the modeling of uncertainty and a simulation-based optimization approach is applied to find the best scenarios for sequencing patterns in the cell through several numerical examples using DEA. The results displayed that, efficient scenarios satisfying minimum time and cost, are those, in which the percentages of operations assigned to the machines are close to each other. This enables decision-makers in manufacturing systems to make precise selections of the optimal part sequencing pattern with the lowest production cost and cycle time for robotic cells.

Integrated optimization of the building envelope and the HVAC system in office building retrofitting

Increasing the energy efficiency of existing buildings is the major strategy to reach carbon neutral targets, given that the building sector is a major emitter. In particular, one major part of building energy usage is the building heating, ventilation and air-cooling system, which are highly depending on the thermal performance of building envelope and increase the challenge of selecting optimal packages of energy efficiency measures. This paper thereby presents a method for optimizing the building envelope and heating, ventilation and air-cooling system. The simulation-based NSGA-III approach developed in this paper is based on coupling the dynamic simulation models created in EnergyPlus software with the non-dominated sorting genetic algorithm, as the engine that performs an optimization process. A large domain of energy efficiency packages combined with heating, ventilation and air-cooling systems and building envelope are investigated to minimize energy consumption, retrofit cost and thermal discomfort in indoor environments, which including ideal loads air system, four pipe fan coil system and variable refrigerant flow system, insulation materials and insulation thickness for external walls and roof, different types of external windows, and window-wall ratio. An office building in Chongqing was chosen as a case study to demonstrate the practical implementation of the proposed method. The results show that the four pipe fan coil system was superior in the diversity and performance of the Pareto front. Then, the optimal packages were generated by a synthetic method, which have 58.57 kWh/m2 of energy consumption, 30.76 CNY/m2 of retrofit cost and 40.67 % of the percentage of thermal discomfort hours. The results also indicate that the yearly energy savings rates are 17.30 %, 23.05 % and 21.10 % separately for ideal loads air system, four pipe fan coil system and variable refrigerant flow system after performing optimization. These results highlight the importance and necessity of performing an optimization procedure for existing buildings to select the optimal retrofit measures.

A Simulation-Based Approach to Teach Interaction Effects in Postgraduate Biostatistics Courses

Simulation-based teaching can be a valuable method for learning statistical concepts. Its practical implementation for health-related subjects is seldomly evaluated. We propose a simulation-based approach to teach interaction effects in a postgraduate biostatistics course. We describe the steps involved in organizing and implementing a simulation-based activity and evaluate its execution. Mainly master and doctoral students in medical sciences (public health/epidemiology) participated to a 3-hr long online workshop on interaction effects. We presented one main learning activity, broken down into six progressive steps, from visualizing an interaction effect to investigating samples leading to erroneous inference. A total of 85 people attended of which 53 filled out an evaluation survey. Most (89%) of the respondents reported that the proposed activities helped them better understand interaction effects. A qualitative content analysis of free-text answers indicated that learning activities focusing on interaction effects in this way were useful for developing an understanding of both the meaning of statistical models and their interpretations. We conclude that a simulation-based approach can be a useful and interactive tool to create a valuable learning experience. The materials provided in this article can serve as a starting point for teachers who wish to implement such a method.

Optimizing Wireless Power Transfer Systems: A Simulation-Based Approach Using CST Studio Suite

In the rapidly evolving field of wireless power transfer (WPT), achieving systems that combine efficiency with reliability is crucial. This paper presents a simulation-based approach using CST Studio Suite to optimize WPT systems for wireless battery chargers in electric bikes. The study fixes the size of both the transmitter and receiver coils, the spacing between turns, and the number of coil turns, with a set distance of 20mm between the transmitter and receiver coils. CST Studio Suite is employed to determine the parameters of resonance capacitors and the optimal switching frequency to enhance WPT efficiency. This powerful simulation tool allows researchers and engineers to design resonance compensation circuits more effectively, optimizing power transfer efficiency. The simulation conducted with CST Studio Suite confirms its effectiveness in designing a WPT system, achieving an impressive peak efficiency of 99.61% at the resonant frequency. The paper demonstrates CST Studio Suite's capability to accurately predict WPT system performance by developing detailed electromagnetic models and fine-tuning simulation parameters to reflect real operating conditions. Iterative simulations resulted in optimized practices, validated by select results that highlight the method's effectiveness and showcase an optimal balance between system complexity and efficiency. These findings provide a strategic framework for future WPT system development, offering practical guidance for researchers and industry professionals aiming to advance WPT technology.

Developing resilience pathways for interdependent infrastructure networks: A simulation-based approach with consideration to risk preferences of decision-makers

In this study, we propose a methodological framework to identify and evaluate cost-effective pathways for enhancing resilience in large-scale interdependent infrastructure systems, considering decision-makers’ risk preferences. We focus on understanding how decision-makers with varying risk preferences perceive the benefits from infrastructure resilience investments and compare them with upfront costs in the context of high-impact low-probability (HILP) events. First, we compute the costs of interventions as the sum of their capital costs and maintenance costs. The benefits of the interventions include the reduction in physical damage costs and business disruption losses resulting from the improved resilience of the network. In the final stage, we develop statistical models to predict the perceived net benefits of different network resilience configurations in power, water, and transport networks. These models are employed in an optimization framework to identify optimal resilience investment pathways. By incorporating Cumulative Prospect Theory (CPT) in the optimization framework, we show that decision-makers who assign higher weights to low probability events tend to allocate more resources towards post-disaster recovery strategies leading to increased resilience against HILP events, like earthquakes. We illustrate the methodology using a case study of the interdependent infrastructure network in Shelby County, Tennessee.

ReSG: A Data Structure for Verification of Majority-based In-memory Computing on ReRAM Crossbars

Recent advancements in the fabrication of Resistive Random Access Memory (ReRAM) devices have led to the development of large-scale crossbar structures. In-memory computing architectures relying on ReRAM crossbars aim to mitigate the processor-memory bottleneck that exists with current complementary metal-oxide semiconductor technology. With this motivation, several synthesis and mapping approaches focusing on the realizations of Boolean functions in the ReRAM crossbars have been proposed earlier. Thus far, the verification of the designs realized on ReRAM crossbars is done either through manual inspection or using simulation-based approaches. Since manual inspections and simulation-based approaches are limited to smaller designs, they cannot be applied to the verification of complex designs on large-scale ReRAM crossbars. Motivated by this, we propose, for the first time, an automatic equivalence checking flow that determines the equivalence between the original function specification (e.g., Majority-inverter Graph ) and the crossbar micro-operations file formats. We consider two crossbar structures, zero-transistor, one-memristor (0T1R) and one-transistor, one-memristor (1T1R) to implement the micro-operations. While the micro-operations file format exists for 0T1R crossbar structures, no representations for micro-operations to be executed in 1T1R crossbars exist yet. In this work, we introduce the micro-operation file format for 1T1R crossbar structures to efficiently represent the micro-operations as ReRAM crossbar netlists. Afterwards, we introduce two intermediate data structures, ReRAM Sequence Graph for 0T1R crossbars (ReSG-0T1R) and for 1T1R crossbars (ReSG-1T1R) , that are derived from the 0T1R and 1T1R crossbar micro-operations file formats, respectively. These ReSGs are then translated into Boolean Satisfiability (SAT) formula, and then the verification is done by checking the generated SAT formulae against the golden functional specification (represented in Verilog) using Z3 Satisfiability solver. Experimental evaluations confirm the effectiveness of the proposed verification methodology on MCNC and ISCAS benchmarks.

Computational Strategies to Enhance Cell-Free Protein Synthesis Efficiency

Cell-free protein synthesis (CFPS) has emerged as a powerful tool for protein production, with applications ranging from basic research to biotechnology and pharmaceutical development. However, enhancing the efficiency of CFPS systems remains a crucial challenge for realizing their full potential. Computational strategies offer promising avenues for optimizing CFPS efficiency by providing insights into complex biological processes and enabling rational design approaches. This review provides a comprehensive overview of the computational approaches aimed at enhancing CFPS efficiency. The introduction outlines the significance of CFPS and the role of computational methods in addressing efficiency limitations. It discusses mathematical modeling and simulation-based approaches for predicting protein synthesis kinetics and optimizing CFPS reactions. The review also delves into the design of DNA templates, including codon optimization strategies and mRNA secondary structure prediction tools, to improve protein synthesis efficiency. Furthermore, it explores computational techniques for engineering cell-free transcription and translation machinery, such as the rational design of expression systems and the predictive modeling of ribosome dynamics. The predictive modeling of metabolic pathways and the energy utilization in CFPS systems is also discussed, highlighting metabolic flux analysis and resource allocation strategies. Machine learning and artificial intelligence approaches are being increasingly employed for CFPS optimization, including neural network models, deep learning algorithms, and reinforcement learning for adaptive control. This review presents case studies showcasing successful CFPS optimization using computational methods and discusses applications in synthetic biology, biotechnology, and pharmaceuticals. The challenges and limitations of current computational approaches are addressed, along with future perspectives and emerging trends, such as the integration of multi-omics data and advances in high-throughput screening. The conclusion summarizes key findings, discusses implications for future research directions and applications, and emphasizes opportunities for interdisciplinary collaboration. This review offers valuable insights and prospects regarding computational strategies to enhance CFPS efficiency. It serves as a comprehensive resource, consolidating current knowledge in the field and guiding further advancements.

Guessing Human Intentions to Avoid Dangerous Situations in Caregiving Robots

The integration of robots into social environments necessitates their ability to interpret human intentions and anticipate potential outcomes accurately. This capability is particularly crucial for social robots designed for human care, as they may encounter situations that pose significant risks to individuals, such as undetected obstacles in their path. These hazards must be identified and mitigated promptly to ensure human safety. This paper delves into the artificial theory of mind (ATM) approach to inferring and interpreting human intentions within human–robot interaction. We propose a novel algorithm that detects potentially hazardous situations for humans and selects appropriate robotic actions to eliminate these dangers in real time. Our methodology employs a simulation-based approach to ATM, incorporating a “like-me” policy to assign intentions and actions to human subjects. This strategy enables the robot to detect risks and act with a high success rate, even under time-constrained circumstances. The algorithm was seamlessly integrated into an existing robotics cognitive architecture, enhancing its social interaction and risk mitigation capabilities. To evaluate the robustness, precision, and real-time responsiveness of our implementation, we conducted a series of three experiments: (i) A fully simulated scenario to assess the algorithm’s performance in a controlled environment; (ii) A human-in-the-loop hybrid configuration to test the system’s adaptability to real-time human input; and (iii) A real-world scenario to validate the algorithm’s effectiveness in practical applications. These experiments provided comprehensive insights into the algorithm’s performance across various conditions, demonstrating its potential for improving the safety and efficacy of social robots in human care settings. Our findings contribute to the growing research on social robotics and artificial intelligence, offering a promising approach to enhancing human–robot interaction in potentially hazardous environments. Future work may explore the scalability of this algorithm to more complex scenarios and its integration with other advanced robotic systems.

Optimization of Chemical Engineering Processes in the Mining and Metal Industry: A Review

Process optimization is acutely vital: mining and a metal industries research. Through applying modelling and optimization tools, businesses see tremendous improvement in process efficiencies, declining costs of production, and ensuring high quality. The valuable of simulation tools have also been reflected in the design of the virtual plants which make possible to conduct test runs of different process scenarios thereby providing the opportunity to optimize the performance of the plants. So, even if these issues are close to being resolved, some challenges must still be tackled to approach the full field of process optimization enjoyment. One of the main drawbacks is the integration of sustainable development and environmental evaluation into optimization phase. This can be achieved by developing models that are amended to an environmental impact of each process parameters and afterwards an optimization of processes will be based on environment factors. The second challenge is for the research community to come up with more advanced and sophisticated mathematical models that are capable of not only capturing but also depicting the diverse and complex interactions that take place between different processes variables. Such an approach indeed requires implementation of more precise data analysis tools and techniques, including neural networks and genetic algorithms. While there are many difficulties in it way, the process optimization of a bright future is foreseeable. The fast growth of the technological sphere, such as machine-2-machine and big data analytics, enables process optimization which was unavailable before. By implementation of a set of sensors and real-time data analysis plant operators are being provided with necessary information that enables this way to exercise real-time decisions for the sake of the increase of the plant performance. Minimizing energy consumption, increasing the product yield, and less damaging to the environment is a potential focus for the research. Through the application of mathematical models, optimization algorithms, and simulation-based approaches, one can compare the different methods of each process and the produced product yields.

Optimizing of Microbump Design for Stable Solder Joints in Thermocompression Bonding: A Simulation-Based Approach

Optimizing of Microbump Design for Stable Solder Joints in Thermocompression Bonding: A Simulation-Based Approach

Modeling Solubility Induced Elemental Fractionation of Noble Gases in Oils

This study explores the estimation of solubility-induced elemental fractionation of noble gases in hydrocarbon-based oils through existing empirical and theoretical models, complemented by a novel molecular simulation-based approach. Quantifying such fractionation is essential for a deeper understanding of fluid processes and migration in subsurface geological resources, an area currently lacking in experimental data. To address this, the research introduces a predictive model that employs the Peng-Robinson equation of state and a fugacity-coefficient-based method to assess noble gas elemental fractionation in hydrocarbons, including normal alkanes, cycloalkanes, and aromatics. However, this model struggles with precise quantitative predictions, prompting the introduction of adjusted cross-interaction parameters to enhance its performance. Furthermore, molecular simulations, in conjunction with the refined equation of state, are shown to offer a novel method for calculating noble gas fractionation coefficients across different hydrocarbon solvents. A key finding is the identification of a universal master curve, demonstrating that noble gas solubility fractionation at low pressure in simple hydrocarbon solvents can be quantitatively determined by temperature and density alone, without the need for detailed compositional information. Consequently, a new correlation is proposed for deriving elemental fractionation coefficients based solely on oil temperature and density, offering significant improvements over existing empirical methods for a wide range of temperatures. Although limitations are noted when applying this approach to oils rich in complex heavy components like resins and asphaltenes, it allows to address inconsistencies observed in traditionally used experimental correlations at high temperatures.

Autosomal suppression of sex-ratio meiotic drive influences the dynamics of X and Y chromosome coevolution.

Sex-ratio meiotic drivers are selfish genes or gene complexes that bias the transmission of sex chromosomes resulting in skewed sex ratios. Existing theoretical models have suggested the maintenance of a four-chromosome equilibrium (with driving and standard X and suppressing and susceptible Y) in a cyclic dynamic, studies of natural populations have failed to capture this pattern. Although there are several plausible explanations for this lack of cycling, interference from autosomal suppressors has not been studied using a theoretical population genetic framework even though autosomal suppressors and Y-linked suppressors coexist in natural populations of some species. In this study, we use a simulation-based approach to investigate the influence of autosomal suppressors on the cycling of sex chromosomes. Our findings demonstrate that the presence of an autosomal suppressor can hinder the invasion of a Y-linked suppressor under some parameter space, thereby impeding the cyclic dynamics, or even the invasion of Y-linked suppression. Even when a Y-linked suppressor invades, the presence of an autosomal suppressor can prevent cycling. Our study demonstrates the potential role of autosomal suppressors in preventing sex chromosome cycling and provides insights into the conditions and consequences of maintaining both Y-linked and autosomal suppressors.

Circular supply chains as complex adaptive ecosystems: A simulation-based approach

Circular Supply Chains (CSCs) are self-sustained ecosystems designed to pursue Circular Economy principles. Compared to linear supply chains, they involve new classes of stakeholders performing circular activities and multiple resource streams (e.g. virgin materials, by-products, end-of-life products, waste) moving forward and back the original and different supply chains. Therefore, CSCs are characterized by increasing complexity that call for proper coordination mechanisms. In this regard, we propose a theoretical framework, based on the theory of Complex Adaptive Systems, capturing the static and dynamic dimensions of CSC complexity which characterize the archetype of a CSC and then we develop a novel agent-based model to simulate the effect of competition and collaboration mechanisms on CSC performance across different CSC archetypes. The results of numerical simulations suggest which mechanism to adopt for the coordination of certain CSC archetypes: while competition is always detrimental, local collaboration is effective for CSCs with closed loop structures, and global collaboration is instead required in presence of open and hybrid loop structures.

Market Penetration Rate Optimization for Mobility Benefits of Connected Vehicles: A Bayesian Optimization Approach

The advancements of connected vehicle (CV) technologies promise significant safety, mobility, and environmental benefits for future transportation systems. These benefits will largely rely on the market penetration rate (MPR) of CVs and connected infrastructure. However, higher MPR is not guaranteed to result in greater benefits in a transportation system in some cases even if we do not consider the deployment cost of CVs. Therefore, understanding the optimal CV MPR to achieve the best system benefits is informative and can provide some guidance for transportation agencies to use appropriate incentives or other policies to potentially affect the speed of CV adoption. Instead of using the traditional incremental method, this paper proposed a simulation-based approach combined with Bayesian optimization to determine the optimal CV MPR that achieves the highest performance benefits for a freeway segment. The proposed methodology is tested in the I-210 E (in California, U.S.) simulation freeway segment built and calibrated in Simulation of Urban Mobility software as a case study. The weighted sum of the average total travel time on the mainline and the average queue length of on-ramps is formulated as the objective function to optimize the CV MPR. Different weight combinations are tested as different scenarios. The optimization results of these scenarios show that, when the weight of total travel time is high, the optimal CV MPR tends to be high. On the contrary, when the weight of queue length increases, higher CV MPRs may not guarantee higher benefits for the traffic system. The globally optimal CV MPR can be as low as 3%. The case study also confirms the effectiveness of optimizing the CV MPR based on microsimulation and Bayesian optimization.

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.

Functional Design Analysis of Two Current Extended-Depth-of-Focus Intraocular Lenses.

To evaluate the differences between two extended depth-of-focus intraocular lenses, the Alcon IQ Vivity and the Bausch & Lomb LuxSmart and to compare them with a simple monofocal lens, the Alcon IQ, using a simulation-based approach. A mathematical lens model was created for each lens type based on a measured surface geometry. The lens model was then used in a raytracer to calculate a refractive power map of the lens and a ray propagation image for the focal zone. The simulations confirm the enhanced depth of focus of these two lenses. There are apparent differences between the models. For the Vivity, more light is directed into the far focus in low light conditions, whereas the LuxSmart behaves more pupil independent and prioritizes intermediate vision. The simulation-based approach was effective in evaluating and comparing the design aspects of these lenses. It can be positioned as a valuable third tool for lens characterization, complementing in vivo studies and in vitro measurements. With this approach not only focusing on the resulting optical performance, but the underlying functional mechanisms, it paves the way forward for a better adaptation to the individual needs and preferences of patients.

Redundancy allocation problem in repairable systems with variegated components: a simulation-based optimization approach

The redundancy allocation problem (RAP) is one of the most commonly used approaches for improving the reliability of systems. In previous studies, to simplify the problem model and mathematical solution methods, some real conditions were not emphasized in analysing the issue of RAP. But the present study, while considering these conditions, enables considering multiple goals, analysing repairable systems and examining the failure rate and repair rate appropriate to the type of components. Two goals of this purpose are maximizing the Mean Time To First Failure (MTTFF) and minimizing the total cost of the system, considering the weight and volume limitations and number of plugin components. To solve the problem model four Multi-objective meta-heuristic algorithms have been used: NSGA-II, MOPSO, PESA-II and MALO. In addition, the simulation method is used to estimate the first objective function of the problem.

Enhancing aviation English competency: A simulation-based approach for aspiring pilots

Aviation English (AE) represents a noteworthy area of investigation within the realm of English for Specific Purposes (ESP), aimed at fostering English language competency among aspiring pilots. In this study, a simulation-based aviation English course was designed and implemented for improved learning gains. The present study took place at a tertiary educational institution, focusing on the analysis of AE vocabulary development, the readback performance of student pilots, and the examination of participants' perceptions of simulation-based training. Employing a mixed-methods sequential explanatory research design, data were gathered from 21 ab-initio pilots enrolled in the Pilotage Training program. The quantitative results showed that participants significantly improved their readback performance and AE vocabulary knowledge over the course of a semester. With regard to the qualitative results, student pilots overall perceived the simulation-based training as an innovative and effective method. The present study, therefore, constitutes a notable advancement in the AE research domain by documenting, for the first time, the application of simulation-based training in the aviation context. Moreover, the study offers insights into the implications for ESP practitioners and stakeholders in the aviation sector, along with recommendations for future research, which are delineated in the study's conclusion.

Development of Low-Pressure Die-Cast Al-Zn-Mg-Cu Alloy Propellers Part II: Simulations for Process Optimization.

With the increasing demand for high-performance leisure boat propellers, this study explores the development of high-strength aluminum alloy propellers using the low-pressure die-casting (LPDC) process. In Part I of the study, we identified the optimal alloy compositions for Al-6Zn-2Mg-1.5Cu propellers and highlighted the challenges of hot tearing at the junction between the hub and blades. In this continuation, we developed a coupled thermal fluid stress analysis model using ProCAST software to optimize the LPDC process. By adjusting casting parameters such as the melt supply temperature, initial mold temperature, and curvature radius between the hub and blades, we minimized hot tearing and other casting defects. The results were validated through simulations and practical applications, showing significant improvements in the quality and structural integrity of the propellers. Non-destructive testing using X-ray CT confirmed the reduction in internal defects, demonstrating the effectiveness of the simulation-based approach for alloy design and process optimization.

Sample Size Calculation Under Nonproportional Hazards Using Average Hazard Ratios.

Many clinical trials assess time-to-event endpoints. To describe the difference between groups in terms of time to event, we often employ hazard ratios. However, the hazard ratio is only informative in the case of proportional hazards (PHs) over time. There exist many other effect measures that do not require PHs. One of them is the average hazard ratio (AHR). Its core idea is to utilize a time-dependent weighting function that accounts for time variation. Though propagated in methodological research papers, the AHR is rarely used in practice. To facilitate its application, we unfold approaches for sample size calculation of an AHR test. We assess the reliability of the sample size calculation by extensive simulation studies covering various survival and censoring distributions with proportional as well as nonproportional hazards (N-PHs). The findings suggest that a simulation-based sample size calculation approach can be useful for designing clinical trials with N-PHs. Using the AHR can result in increased statistical power to detect differences between groups with more efficient samplesizes.