Simulation Optimization Research Articles

Efficient Quantum Circuit Simulation by Tensor Network Methods on Modern GPUs

Efficient simulation of quantum circuits has become indispensable with the rapid development of quantum hardware. The primary simulation methods are based on state vectors and tensor networks. As the number of qubits and quantum gates grows larger in current quantum devices, traditional state-vector-based quantum circuit simulation methods prove inadequate due to the overwhelming size of the Hilbert space and extensive entanglement. Consequently, brutal force tensor network simulation algorithms become the only viable solution in such scenarios. The two main challenges faced in tensor network simulation algorithms are optimal contraction path finding and efficient execution on modern computing devices, with the latter determining the actual efficiency. In this study, we investigate the optimization of such tensor network simulations on modern GPUs and propose general optimization strategies from two aspects: computational efficiency and accuracy. First, we propose to transform critical Einstein summation operations into GEMM operations, leveraging the specific features of tensor network simulations to amplify the efficiency of GPUs. Second, by analyzing the data characteristics of quantum circuits, we employ extended precision to ensure the accuracy of simulation results and mixed precision to fully exploit the potential of GPUs, resulting in faster and more precise simulations. Our numerical experiments demonstrate that our approach can achieve a 3.96× reduction in verification time for random quantum circuit samples in the 18-cycle case of Sycamore, with sustained performance exceeding 21 TFLOPS on one A100. This method can be easily extended to the 20-cycle case, maintaining the same performance, accelerating by 12.5× compared to the state-of-the-art CPU-based results and 4.48–6.78× compared to the state-of-the-art GPU-based results reported in the literature. Furthermore, the strategies presented in this article hold general applicability for accelerating problems that can be tackled by contracting tensor networks such as graphical models and combinatorial optimizations.

Task offloading for multi-server edge computing in industrial Internet with joint load balance and fuzzy security

The industrial Internet revolutionizes traditional manufacturing through the incorporation of technologies such as real-time production optimization, big data analysis, etc. Computing resource-constrained industrial terminals struggle to effectively execute latency-sensitive and computation-intensive tasks triggered by these technologies. Edge computing (EC) emerges as a promising paradigm for offloading tasks from terminals to the adjacent edge servers, offering the potentiality to augment the computational capacities for industrial terminals. However, the development of accurate offloading strategies poses a prominent challenge for EC in the industrial Internet. Incorrect offloading strategies will misguide the task offloading procedure, resulting in adverse consequences. In this paper, we study the latency-aware multi-server partial EC task offloading problem in the industrial Internet with the consideration of joining load balancing and security protection to provide accurate strategies. Firstly, we establish a task offloading model that supports partial offloading, facilitating latency reduction, task offloading across multiple edge servers with load balance, and accommodation of fuzzy task risks. We quantify the established model as a constrained optimization formulation and prove its NP-hardness. Secondly, to solve the composite offloading strategy comprising both the offloading location and offloading ratio derived from our model, we propose a bi-layer offloading algorithm with joint load balance and fuzzy security, which is based on the adaptive genetic algorithm and simulated annealing particle swarm optimization. Based on extensive experimental results, we find that the established model is effective in reducing the objective value, with a respective decrease of 27% and 46% compared to full execution in edge servers and local execution in industrial terminals. Furthermore, the proposed offloading algorithm exhibits superior performance in terms of solution accuracy compared to existing algorithms.

Ant lion based optimization for performance improvement of methanol production

Abstract Methanol (CH3OH) is a versatile compound used in various industries. Catalytic reactors used in CH3OH production are expensive due to high energy and raw material costs. Multi-objective optimization (MOO) is used to optimize CH3OH production, but it is still lacking. Researchers use alternative strategies or modify existing ones to achieve better results. This study applied model-based optimization using an ASPEN Plus simulator and Multi-objective Ant Lion Optimization (MOALO) to address the issue. The results revealed the highest conversion and product rate, with the lowest energy cost, side product, and bare module cost, CBM. The decision variable plots indicate that the reactor’s pressure significantly affects the optimal solution. This study provides valuable insight into optimizing CH3OH production.

Potential conflict diagnosis, simulation optimization and coordination of production-living-ecological space in gully areas of the Loess Plateau, China

The gully areas on the Loess Plateau have always been ecologically fragile agricultural areas. These areas often face the common problem of intensified conflicts in production-living-ecological space during economic development, and there is an urgent need for systematic spatial governance. This study innovatively developed a full process technical framework of “identification diagnosis - simulation optimization - coordinated governance” for potential conflicts in the production-living-ecological space, and used Huachi County in the gully area of the Loess Plateau as an example for empirical research. Under this framework, we analyzed the transfer patterns of production, living, and ecological space in the research area; Improved the spatial suitability evaluation index system of “multi-dimensional + multi-objective”; The conflict determination matrix was used to identify the scope, scale, type, and intensity of potential spatial conflicts; We conducted spatial optimization simulations under two scenarios: prioritizing ecological protection and prioritizing social and livelihood development, and designed an overall spatial optimization plan; Propose spatial collaborative governance strategies based on the theory of public space governance. Research shows that this framework has good applicability for the research area and is more direct for guiding subsequent spatial planning and policy practice. The potential conflict between production space and ecological space in Huachi County is the most common, the potential conflict intensity between living space and production space is the largest, and the potential conflict risk between living space and ecological space is rising. To coordinate potential conflicts in space, it is necessary to establish a vertical allocation type of spatial function coordination chain of “goal + collaboration + adjustment”, as well as a horizontal negotiation type of spatial configuration coordination chain of “allocation + cooperation + balance”, forming a “double vertical and double horizontal” spatial coordination framework.

Enhancing Oxygen-Dissolving Capacity of Rotary Drum Food Waste Composting: Tumbling Process Optimization and Experimental Validation with Discrete and Finite Element Methods

An optimized tumbling process can significantly improve the oxygen dissolving capacity of composting and fertilizer quality: by increasing the fluffiness of the lower layer of the pile, localized anaerobic fermentation can be avoided, thereby enhancing compost quality. This paper presents a method for improving the oxygen dissolving capacity of rotary drum food waste composting through a combination of simulation optimization and experimental validation. First, the discrete element method was used to optimize the key parameters of the tumbling process. The response surface method was then employed to analyze the composting test results and determine the optimal conditions. To ensure the reliability of the equipment under this method, failure risk analysis was conducted using the finite element method. The simulation optimization results showed that with a rotary drum reactor speed of 3.5 r/min, a horizontal angle of inclination of 2.5°, a mixing blade angle of inclination of 43°, and a blade pitch of 580 mm, the fluffiness of the lower layer of the pile increased by 8.515%, achieving the best tumbling and indirectly enhancing oxygen dissolving capacity. The maximum deformation of the load-bearing components was only 0.0548 mm, and the minimum safety factor was 4.408 (≥1 is considered safe). A 14-day composting experiment was conducted to validate the optimized parameters. The results showed that the maximum temperature of the compost pile reached 68.34 °C (lasting 7 days), with the pH, moisture content, C/N ratio, humus substances, humic acid, and fulvic acid contents of the fertilizer all meeting or exceeding the levels recommended by Chinese national standards. These findings indicate that the optimized tumbling device effectively improved the stability and dissolved oxygen efficiency of food waste composting, providing valuable practical insights and a research foundation for enhancing oxygen efficiency in the composting of other organic wastes.

Hydrological Simulation and Parameter Optimization Based on the Distributed Xin’anjiang Model and the Particle Swarm Optimization Algorithm: A Case Study of Xunhe Watershed in Shandong, China

Hydrological models serve as essential tools in hydrological research, allowing us to address practical hydrological issues. This study focuses on the Xunhe Watershed in Shandong Province, China, constructing a distributed Xin’anjiang hydrological model. Furthermore, traditional manual calibration and automatic calibration using the Particle Swarm Optimization (PSO) algorithm were employed to determine model parameters, followed by hydrological simulations, with the aim of investigating the applicability of the distributed Xin’anjiang model in this watershed. The research findings indicate that the distributed Xin’anjiang model accurately simulates the hydrological processes in the Xunhe Watershed. There is a high level of agreement between the observed data and the simulated results, including key indicators such as peak discharge, runoff volume, and peak time. After optimizing the model parameters using the PSO algorithm, the distributed Xin’anjiang model demonstrates improved simulation performance in the Xunhe Watershed. During the calibration period, the mean relative peak discharge error (RPE) is 4.1%, the mean relative runoff error (RRE) is 4.34%, and the average Nash–Sutcliffe efficiency (NSE) for simulating the flood events is 0.89. During the validation period, the mean RPE is 3.82%, the mean RRE is 6.1%, and the average NSE for the process is 0.83. This indicates that the distributed Xin’anjiang model has good applicability in this watershed, providing a reliable reference for flood control and disaster reduction in the Xunhe Watershed.

Simulation Optimization for Inpatient Bed Allocation with Sharing

Simulation Optimization for Inpatient Bed Allocation with Sharing

A Multi-Sized Unit Cell Method for the Design of LPBF Lattice Support Structures Concerning Complex Geometries

Abstract Composed of individual unit cells strategically arranged to achieve a desired function, lattices are a promising solution for laser powder bed fusion support structure design in additive manufacturing. Despite their many advantages (e.g., multifunctionality and reduced material cost), prior work in lattice support structure design primarily focuses on horizontal support domains that are not translatable to support domains for complex geometries, thereby limiting their application. This work introduces a multi-sized unit cell design optimization (MSO) method to create lattice support structures (LSS) for parts with complex geometries. The proposed method utilizes voxelization to generate LSS using box-like unit cells of different sizes. It also allows for efficient, high-dimensional design optimization for the types and locations of user-specified unit cells through a modified simulated annealing-based optimization algorithm. The effectiveness and efficiency of the MSO method are demonstrated through the case study of an adapter pipe for a high-temperature heat exchanger. For this demonstration, LSS using multi-sized unit cells is designed to increase heat transfer rate while satisfying structural integrity and material cost constraints. The case study results indicate that the design of the LSS derived from the MSO method fulfills all constraints, including the design constraint of 50% material cost reduction, compared to the solid support structure. In contrast, the lattice support structure designs derived from equal-sized unit cell methods either cannot satisfy all design constraints or have a lower heat transfer rate than the design of the MSO method.

THOR: inclusion of charge carriers’ creation and transport in semiconductor detectors in the PENELOPE Monte Carlo simulation code

This study introduces the simulation framework THOR, which was designed to include the creation and transport of electrons and holes in pixelated semiconductors detectors used for x-ray imaging in the PENELOPE v. 2014 Monte Carlo code and its penEasy v.2015 extension. Following the simulation of radiation transport and energy deposition in the detector, the THOR code breaks down into electron-hole pair (EHP) creation, transport, trapping, and modes of detection, including simulations of energy integrating and photon counting detectors. Validation of the THOR code was conducted using the ARTEMIS code. This study shows the impact of the simulation parameters (detector geometry, photon energy, charge carrier dispersion, etc.) on the detector performance metrics. Notably, the energy cut-off for electrons and photons were found to significantly influence accuracy and computational speed, with the identification of the optimal value as 250 eV for improved computational performance without sacrificing accuracy. The ability to adjust the energy necessary to create an EHP specifically for a-Se sensors introduce a novel approach to balancing speed and accuracy effectively. Moreover, the THOR code allows the user to choose the bias direction and which charge is collected, showing the code’s adaptability across various operational scenarios and materials. For instance, the results showed that using a negative bias and collecting holes results in less spatial resolution because the charge would have to travel a longer distance to be collected. Moreover, the trapping is also a factor, since the and the probability of trapping increases with the distance traveled by the charge carrier. The inclusion or exclusion of charge trapping in simulations can significantly influence the detector efficiency, with differences up to 60%. Analysis of physical parameters such as photon energy, detector thickness, composition, and applied electric field provides insights into their effects on detector performance metrics. Understanding these effects is essential for optimizing imaging systems for specific applications, thereby advancing diagnostic capabilities and patient care in medical imaging. In summary, this study offers a comprehensive understanding of detector behavior and simulation optimization, aiming to enhance imaging techniques.

Rolling over Simulation and Structure Optimization of Electric Automobile Charging Gun

Abstract The anti-rolling performance of the electric vehicle charging gun is critical in its daily work. Firstly, to improve the accuracy of the simulation, the tire FEA model is modeled in detail, including the tire airbag simulation tire pressure, the Beam unit simulation tire bead wire, etc. We also calibrate the FEA model of the charging gun. Secondly, through the rolling-over simulation and comparison with the test results, both the gun heads are broken and the damage positions of both are the same, which indicates the accuracy of the rolling-over simulation model. Finally, based on the results of the rolling-over simulation and test, the structure of the charging gun is optimized, the rolling-over simulation and test are carried out again, and the charging gun head and the gun shell are not damaged. The results show that the simulation model can guide the design of the anti-rolling performance of charging gun.

Network Attack Simulation and Defense System Optimization based on Big Data

Network Attack Simulation and Defense System Optimization based on Big Data

Simulation and Parameter Optimization of Two-Dimensional SQUID Gradiometer

Simulation and Parameter Optimization of Two-Dimensional SQUID Gradiometer

Simulation model calibration with dynamic stratification and adaptive sampling

ABSTRACT Calibrating simulation models that take large quantities of multi-dimensional data as input is a hard simulation optimization problem. Existing adaptive sampling strategies offer a methodological solution. However, they may not sufficiently reduce the computational cost for estimation and solution algorithm’s progress within a limited budget due to extreme noise levels and heteroscedasticity of system responses. We propose integrating stratification with adaptive sampling for the purpose of efficiency in optimization. Stratification can exploit local dependence in the simulation inputs and outputs. Yet, the state-of-the-art does not provide a full capability to adaptively stratify the data as different solution alternatives are evaluated. We devise two procedures for data-driven calibration problems that involve a large dataset with multiple covariates to calibrate models within a fixed overall simulation budget. The first approach dynamically stratifies the input data using binary trees, while the second approach uses closed-form solutions based on linearity assumptions between the objective function and concomitant variables. We find that dynamical adjustment of stratification structure accelerates optimization and reduces run-to-run variability in generated solutions. Our case study for calibrating a wind power simulation model, widely used in the wind industry, using the proposed stratified adaptive sampling, shows better-calibrated parameters under a limited budget.

Research on Integrated Optimization of Design Parameters and Process Simulation for Throughput Efficiency

This thesis presents a comprehensive approach to enhance the production throughput of aluminium bumper beams, a critical component in automotive manufacturing. Using CATIA, an innovative bumper beam design was developed, focusing on simplicity of design and manufacturability. The design parameters considered included material selection, cross-sectional area optimization, and welding techniques to ensure both performance and ease of manufacturing. Transitioning to Tecnomatix Plant Simulation, a virtual environment was constructed to simulate the bumper beam production process. The simulation incorporated the identified design parameters, process routes, layout considerations, and machine arrangements to optimize throughput without inflating costs. By leveraging advanced simulation techniques, the study aimed to identify the most efficient production strategies while maintaining design integrity and minimizing resource consumption. Through systematic scenarios experimentation and analysis, the research yielded insights into the complex relationship between design decisions and manufacturing processes. The results highlight the potential for significant throughput enhancements achievable through collaborative optimization of design and production parameters. The optimization was able to achieve an increase in throughput of about 12.64% of its initial throughput. Moreover, the study underscores the importance of holistic approaches integrating design engineering with manufacturing simulation to drive continuous improvement in industrial operations. This thesis contributes to advancing automotive manufacturing practices by offering a methodology for enhancing production efficiency while preserving product quality and cost-effectiveness. The findings provide valuable guidance for engineers and practitioners seeking to optimize manufacturing processes through the use of simulation and design parameters

Water Resources Quality Indicators Monitoring by Nonlinear Programming and Simulated Annealing Optimization with Ensemble Learning Approaches

Water Resources Quality Indicators Monitoring by Nonlinear Programming and Simulated Annealing Optimization with Ensemble Learning Approaches

Optimization and Analysis of Flow Field Simulations Across Different Mach Regimes Using Discrete Numerical Methods

This article explores the development of flow field models in steady-state environments utilizing Euler equations and potential flow equations, with verification processes conducted using Python. The models demonstrate stability and high accuracy in low-velocity scenarios, capturing essential dynamics effectively. However, as the conditions transition to supersonic speeds, the models begin to exhibit increased errors. This discrepancy highlights the challenges faced in simulating high-speed aerodynamics accurately. The research underscores the importance of improving model fidelity in diverse Mach regimes, particularly in supersonic conditions where traditional methods struggle. Future research directions identified include the development of unsteady flow field models, which are crucial for dynamic analyses, optimization of grid structures for three-dimensional complex fields to improve computational efficiency, and the creation of extensive model libraries. These advancements aim to enhance the accuracy, reliability, and practicality of flow field simulations, extending their applicability in both academic studies and industry applications, particularly in aerospace engineering where precise flow modeling is critical.

Mission Planning Method for Dense Area Target Observation Based on Clustering Agile Satellites

To address the mission planning challenge for agile satellites in dense point target observation, a clustering strategy based on an ant colony algorithm and a heuristic simulated genetic annealing optimization algorithm are proposed. First, the imaging observation process of agile satellites is analyzed, and an improved ant colony algorithm is employed to optimize the clustering of observation tasks, enabling the satellites to complete more observation tasks efficiently with a more stable attitude. Second, to solve for the optimal group target observation sequence and achieve higher total observation benefits, a task planning model based on multi-target observation benefits and attitude maneuver energy consumption is established, considering the visible time windows of targets and the time constraints between adjacent targets. To overcome the drawbacks of traditional simulated annealing and genetic algorithms, which are prone to local optimal solution and a slow convergence speed, a novel Simulated Genetic Annealing Algorithm is designed while optimizing the sum of target observation weights and yaw angles while also accounting for factors such as target visibility windows and satellite attitude transition times between targets. Ultimately, the feasibility and efficiency of the proposed algorithm are substantiated by comparing its performance against traditional heuristic optimization algorithms using a dataset comprising large-scale dense ground targets.

Chemical Bath Deposition of NiFe Alloy Anode for Efficient Alkaline Water Electrolyzer Integration.

Green hydrogen production from water splitting is a feasible way for intermittent renewable energy storage and utilization, where the exploration and scale-up preparation of high-performance anodic oxygen evolution electrocatalysts are critical prerequisites for its industrial-level applications. Herein, a chemical bath deposition of FeNi3 intermetallic alloys onto Ni mesh support is performed, which delivers a current density of 0.62 Acm-2 at 1.72 V versus reversible hydrogen electrode for alkaline water oxidation in 1 m KOH and an excellent electrolysis stability at 0.2 A cm-2 for over 300 h. Moreover, via 3D computational fluid dynamics simulation and flow field optimization, a homogeneous deposition of ≈5400 cm2 NiFe anode is demonstrated within 4 min using the developed flow bath reactor. Once integrating the as-prepared NiFe anodes into alkaline electrolyzer stack, the voltage variation between each unit cell is below 40 mV at a total operation current of 71 A, or ca. current density of 0.2 A cm-2, confirming the uniformity of this batch synthesis protocol and its great potential for industrial alkaline water electrolysis.

A closed-loop dual-channel supply chain network for leather products: An integrated simulation optimization clustering approach

The leather industry is pivotal to the economic development of certain countries; however, it also poses significant environmental challenges. Thus, this study aims to design a closed-loop leather supply chain network that incorporates waste recycling and product regeneration processes. In light of the growing prevalence of both in-store and online shopping, the concept of a dual-channel supply chain is examined. Furthermore, as advertising through traditional and social media continues to expand, this research uniquely investigates the impact of two advertising modalities—SMS and social networks—alongside the design and layout of retail environments, considering their effects on product sales within the supply chain context. Additionally, outlet stores are integrated into the model to facilitate the sale of products that may become outdated over time, thereby enhancing the model's real-world applicability. To achieve this, a multi-product, multi-period mathematical model is developed to minimize costs and maximize customer responsiveness in both forward and reverse supply chain flows. The K-means clustering method is employed to categorize customers based on recency, frequency, and monetary (RFM) features. Through simulation, demand parameters are derived for each customer cluster across various scenarios. Moreover, the Aghezzaf approach is utilized to address scenario-based uncertainties within the model, and the proposed multi-objective model is solved using the Augmented Epsilon constraint method. A real-world case study is conducted to apply the proposed model, and critical parameters are examined through sensitivity analysis to validate its effectiveness. The findings suggest that incorporating return flows into the supply chain can significantly enhance both producer profitability and customer responsiveness regarding leather products.

Research on Deep Bomb Delivery Strategy Based on Monte Carlo Simulation and Adaptive Optimization Algorithm

Aiming at the problem of insufficient precision of deep bomb delivery in traditional anti-submarine warfare, this paper proposes a novel algorithm combining Monte Carlo simulation and adaptive optimization [1]. The algorithm optimises the deep bomb delivery strategy by defining the probability distribution of the submarine position. Specifically, Monte Carlo simulation is used to generate samples of possible submarine positions, analyse the effect of the submarine's three-dimensional positional error underwater on the probability of hitting a single depth charge, and explore the role of the dual-fuse detonation mechanism [2]. On this basis, the submarine's orientation error in 3D space is further considered, and an optimisation model is used to determine the optimal fixed-depth fuse detonation depth to maximise the hit probability of a single deep bomb [3]. In addition, this paper extends to the case of multiple depth charges, and optimises the overall hit probability by designing the array of charges and plane spacing to ensure that at least one depth charge can hit the submarine [4]. The experimental results show that the algorithm significantly improves the hit probability of the deep bomb, effectively reduces the impact of positioning error on the combat effect and demonstrates its potential application in practical anti-submarine warfare.