Simulation Optimization Research Articles

Simulation optimization of supply chain uncertainty factors based on support vector machines optimized with improved Jellyfish Search Algorithm

To address uncertainties in the supply chain, this paper analyzes potential uncertainties by constructing a three-tier supply chain simulation model and proposes an optimized support vector machine algorithm (IJS-SVM) using the improved Jellyfish Search Algorithm to optimize these uncertainties. In comparison to the classical Jellyfish Search Algorithm, the proposed algorithm incorporates Logistic chaotic mapping, Cauchy variation, and inverse learning strategies, with the goal of improving solution efficiency and avoiding local optima. The experimental results indicate that IJS-SVM outperforms the support vector machine optimized by the classical Jellyfish Search Algorithm (JS-SVM), the support vector machine optimized by the genetic algorithm (GA-SVM), the support vector machine optimized by the particle swarm optimization algorithm (PSO-SVM), and the traditional support vector machine (SVM) in terms of classification accuracy. Building on this study, the SHAP explanatory model is employed to interpret IJS-SVM’s prediction results, quantify the specific impact of uncertainty factors on each stage of the supply chain, and input these quantitative results into the simulation system to assess warehouse allocation and identify strategies to enhance warehouse utilization, ultimately minimizing supply chain costs. The experiment demonstrates that the model not only offers valuable practical guidance for CEPC engineering project planning but also has broad applicability and can be extended to uncertainty management and optimization within the tertiary supply chain sector.

Optimization of hybrid manufacturing thermal energy resources and simulation of enterprise economic benefits based on blockchain technology

Optimization of hybrid manufacturing thermal energy resources and simulation of enterprise economic benefits based on blockchain technology

A simulation–optimization approach for capacitated lot-sizing in a multi-level pharmaceutical tablets manufacturing process

A simulation–optimization approach for capacitated lot-sizing in a multi-level pharmaceutical tablets manufacturing process

Revelation and Enhancement for Pedestrian Evacuation at Metro Station: Metamodeling-Based Simulation Optimization Approach

Revelation and Enhancement for Pedestrian Evacuation at Metro Station: Metamodeling-Based Simulation Optimization Approach

An integrated simulation–optimization method for flexible assembly job shop scheduling with lot streaming and finite transport resources

An integrated simulation–optimization method for flexible assembly job shop scheduling with lot streaming and finite transport resources

Measuring the effectiveness and efficiency of simulation optimization metaheuristic algorithms

Metaheuristic algorithms have proven capable as general-purpose algorithms for solving simulation optimization problems. Researchers and practitioners often compare different metaheuristic algorithms by examining one or more measures that are derived through empirical analysis. This paper presents a single measure that can be used to empirically compare different metaheuristic algorithms for optimization problems. This measure incorporates both the effectiveness and efficiency of the metaheuristic algorithm, which is especially important in simulation optimization applications because the number of simulation runs available to the analyst (i.e., the run budget) can vary significantly with each simulation study. Therefore, the trade-off between the effectiveness and efficiency of a metaheuristic algorithm must be examined. This single measure is especially useful for multi-objective optimization problems; however, determining this measure is non-trivial for two or more objective functions. Additional details for calculating this measure for multi-objective optimization problems are provided as well as a procedure for comparing two or more metaheuristic algorithms. Finally, computational results are presented and analyzed to compare the performance of metaheuristic algorithms using knapsack problems, pure binary integer programs, traveling salesman problems, and the average results obtained across a diverse set of optimization problems that include simulation and multi-objective optimization problems.

Design of low-carbon planning model for vehicle path based on adaptive multi-strategy ant colony optimization algorithm

In contemporary transportation systems, the imperatives of route planning and optimization have become increasingly critical due to vehicles’ burgeoning number and complexity. This includes various vehicle types, such as electric and autonomous vehicles, each with specific needs. Additionally, varying speeds and operational requirements further complicate the process, demanding more sophisticated planning solutions. These systems frequently confront myriad challenges, including traffic congestion, intricate routes, and substantial energy consumption, which collectively undermine transportation efficiency, escalate energy usage, and contribute to environmental pollution. Hence, strategically planning and optimizing routes within complex traffic milieus are paramount to enhancing transportation efficacy and achieving low-carbon and environmentally sustainable objectives. This article proposes a vehicle path low-carbon planning model, Adaptive Cooperative Graph Neural Network (ACGNN), predicated on an adaptive multi-strategy ant colony optimization algorithm, addressing the vehicle path low-carbon planning conundrum. The proposed framework initially employs graph data from road networks and historical trajectories as model inputs, generating high-quality graph data through subgraph screening. Subsequently, a graph neural network (GNN) is utilized to optimize nodes and edges computationally. At the same time, the global search capability of the model is augmented via an ant colony optimization algorithm to ascertain the final optimized path. Experimental results demonstrate that ACGNN yields significant path planning outcomes on both public and custom-built datasets, surpassing the traditional Dijkstra’s shortest path algorithm, random graph network (RGN), and conventional GNN methodologies. Moreover, comparative analyses of various optimization methods on the custom-built dataset reveal that the ant colony optimization algorithm markedly outperforms the simulated annealing algorithm (SA) and particle swarm optimization algorithm (PSO). The method offers an innovative technical approach to vehicle path planning and is instrumental in advancing low-carbon and environmentally sustainable goals while enhancing transportation efficiency.

Parallel Bayesian optimization of free-electron lasers based on laser wakefield accelerators

Abstract In this paper, the parallel Bayesian optimization algorithm is investigated for the simulation optimization of compact free electron lasers (FELs) driven by laser wakefield accelerators (LWFAs). The radiation energy serves as the objective function, which can be optimized to 25 μJ after several tens of iterations, initiating from random input samplings in soft x-ray regimes. The analysis of the FEL gain process has revealed that the localized properties of the electron beam (e beam) can be concurrently optimized, ensuring a high efficiency of energy extraction from the e beam. Our proposed scheme not only presents a viable design for compact FELs driven by LWFAs in the soft x-ray regime utilizing the start-to-end model, but also highlights the great potential of parallel Bayesian optimization algorithms for tackling the challenges associated with costly and complex black-box optimization problems.

Biomechanics of a Physically Impaired Individual from Early Medieval Ranelagh, Ireland

This research describes the methods used to re-create the biomechanics of a physically impaired middle-aged male, SK67, dated 893–1023 C.E., from the early medieval cemetery at Ranelagh, Co. Roscommon, Ireland, who experienced an antemortem femoral head fracture. A gap in research exists between the osteological evidence and the nature of the true biomechanical gait. Computer simulation of musculoskeletal models is an emerging engineering approach to estimate true gait patterns from geometric measurements of skeletal anatomy. This study paves the way for bridging the gap between osteological evidence and true biomechanics by obtaining information from SK67 and using it to build a three-dimensional musculoskeletal model of SK67 using OpenSim. Gait was then simulated using SCONE software (a musculoskeletal simulation optimization system to predict task-specificmotion), thereby revealing SK67’s likely limb function post-impairment. The patterns of habitual biomechanical stress are reflected in the quantity and distribution of cortical bone; based on this approach, the individual’s limbs were compared for signs of weight-bearing by analyzing cortical bone thickness from radiographs. The upper limbs were also examined for entheseal changes that might indicate the use of walking aids. The results of these interdisciplinary methods enabled the biomechanics of SK67 to be revealed and assessed how SK67 would have used walking aids to regain mobility and independence in the wake of their major trauma. The lack of arms on the musculoskeletal models may provide a limitation to this study, as well as the errors/learning curve in altering inputfiles to simulate the gait. This method opens new avenues for future research into reconstructing the biomechanics of individuals with physical impairments within archaeological contexts, across both time and location.

Statistical-based simulation and reflow profile optimization for improving self-alignment performance − an experimental study

Statistical-based simulation and reflow profile optimization for improving self-alignment performance − an experimental study

Surrogate-based model parameter optimization in simulations of the West African monsoon

Abstract. The West African monsoon (WAM) system is a critical climatic phenomenon with significant socio-economic impacts on millions of people. Despite many advancements in numerical weather and climate models, accurately representing the WAM remains a challenge due to its intricate dynamics and inherent uncertainties. Building upon our previous work utilizing the ICON (icosahedral nonhydrostatic) numerical model to construct statistical surrogate models for quantities of interest (QoIs) characterizing the WAM, this paper focuses on the optimization of the three uncertain model parameters of entrainment rate, fall speed of ice, and soil moisture evaporation fraction through innovative multi-objective optimization (MOO) techniques. The problem is approached in two distinct ways: (1) optimization of 15 designated QoIs, such as the latitude and magnitude of the African rain belt or African easterly jet, using existing surrogate models and (2) optimization of twelve 2D meteorological output fields, such as precipitation, cloud cover, and pressure, using new surrogate models that employ principal component analysis. The objectives subject to minimization in the MOO process are defined as the difference between the surrogate model and reference data for each QoI or output field. As reference data, Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM IMERG) mission are used for precipitation, and the ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts are used for all other quantities. The multi-objective optimization problems are tackled through two strategies: (1) assignment of weights with uncertainties to the objectives based on expert opinion and (2) variation in these weights in order to assess their influence on the optimal values of the uncertain model parameters. Results show that the ICON model is already generally well-tuned for the WAM system. However, a lower entrainment parameter would lead to a more accurate simulation of accumulated precipitation, averaged 2 m dew point temperature, and mean sea level pressure over the considered domain (15° W to 15° E, 0 to 25° N). An improvement in 2D output fields instead of QoIs is barely possible with the considered parameters, which confirms meaningful default values of the model parameters for the region. Nevertheless, optimal model parameters strongly depend on the assigned weights for the objectives. To further enhance the accuracy of climate simulations and potentially improve weather predictions, it is crucial to prioritize the refinement of the overall physical models, including the reduction in inherent structural errors, rather than solely adjusting the uncertain parameters in existing model parametrizations. Nevertheless, our methodology demonstrates the potential of integrating statistical and expert-driven approaches to assess and improve the simulation accuracy of the WAM. The findings underscore the importance of considering uncertainties in MOO and the need for a holistic understanding of the WAM's dynamics to enhance prediction skills.

Magnetic field probe-based co-simulation method for irregular volume-type inductively coupled wireless MRI radiofrequency coils.

Inductively coupled wireless coils are increasingly used in MRI due to their cost-effectiveness and simplicity, eliminating the need for expensive components like preamplifiers, baluns, coil plugs, and coil ID circuits. Existing tools for predicting component values and electromagnetic (EM) fields are primarily designed for cylindrical volume coils, making them inadequate for irregular volume-type wireless coils. The aim of this study is to introduce and validate a novel magnetic (H-) field probe-based co-simulation method to accurately predict capacitance values and EM fields for irregular volume-type wireless coils, thereby addressing the limitations of current prediction tools. The proposed method involves several key steps: modeling the coil in EM simulation software, replacing lumped components with 50-Ω ports, placing well-decoupled double pick-up sniffer probes within the wireless coil, conducting full-wave EM simulations, and exporting the S-parameter matrix to an RF circuit simulation tool for optimization. The RF circuit simulation optimizes component values by maximizing the average magnitude of the root square of Sxys (mean_√Sxy) of double probes and minimizing the normalized standard deviation of √Sxy (normStd_√Sxy). The optimized capacitance values are validated through re-performing EM simulations, and hardware prototypes are fabricated and tested in MRI experiments. The method was validated using bottle-shaped and dome-shaped Litzcage coils designed for 1.5T MRI. Consistent resonant peaks and magnetic field distributions were observed across different coil designs. The optimized capacitance values obtained from circuit-level simulations were confirmed through EM simulations. Significant SNR enhancements were observed in MRI experiments, with the wireless hand and wrist/head coil showing an overall SNR enhancement of 12.8/3.4-fold in EM simulation and 13.4/3.8-fold in MRI experiments, compared to the body coil alone. The H-field probe-based co-simulation method provides an efficient and accurate solution for designing and optimizing irregular wireless RF coils in MRI. By integrating EM simulation, H-field probes, and RF circuit optimization, this method reduces the need for extensive full-wave EM simulations and accurately predicts capacitance values and EM fields. The validation using irregular Litzcage coils demonstrated the method's efficacy, contributing to improved imaging quality in MRI applications. This approach offers a valuable tool for coil developers and researchers, facilitating the development of high-quality irregular wireless coils for enhanced MRI performance.

Entropy-based groundwater quality monitoring network design using a simulation–optimization approach by coupling genetic algorithm, MODFLOW and MT3DMS

Entropy-based groundwater quality monitoring network design using a simulation–optimization approach by coupling genetic algorithm, MODFLOW and MT3DMS

Optimization and Analysis of Gear Set Structure for Continuous Loading and Unloading Hydraulic Power Tongs

Optimizes and analyzes the gear set structure of hydraulic power pliers used in continuous loading and unloading operations. The optimization design process of gear set structure was elaborated in detail, including the selection of independent variables, determination of constraint conditions, and establishment of optimization objectives and fitness functions. The simulated annealing particle swarm optimization algorithm (SAPSO) was used for optimization design, and the optimization results showed that the total volume of the gear set was reduced by 17.1%. Finally, the strength of the optimized gear set was verified through finite element analysis, and the results showed that the strength of the optimized gear set met the actual working requirements.

Numerical Simulation and Structural Optimization of Combustion Processes in a 750 t/d Waste Incinerator

This paper presents a numerical simulation and structural optimization study of the combustion process within the grate and boiler furnace of a 750 t/d waste incineration. The study focuses on adjusting the secondary air velocity, secondary air inclination angle, and the arrangement of secondary air nozzles. These adjustments aim to optimize parameters such as the temperature field, pollutant emission, flow field, particle residence time, and filling degree. The findings demonstrate that high-temperature zones, which lead to slagging problems, are likely to form beneath the front arch. The combustibles inside the furnace are thoroughly burnt, reflecting efficient combustion. The concentration of NOx in the flue gas at the furnace outlet generally ranges between 170 and 200 ppm. Optimal operating conditions are identified as a secondary air inclination angle of 20° with an air velocity of 55 m/s, and an angle of 30° with an air velocity of 55 m/s and 65 m/s, in conjunction with a relative arrangement of the nozzles. Under these conditions, the incineration furnace achieves its best operational state.

Hybridizing evolutionary algorithms and multiple non-linear regression technique for stream temperature modeling

Abstract The present study hybridizes the new-generation evolutionary algorithms and the nonlinear regression technique for stream temperature modeling and compares this approach with conventional gray and black box approaches under natural flow conditions, providing a comprehensive assessment. The nonlinear equation for water temperature modeling was optimized using biogeography-based optimization (BBO) and invasive weed optimization (IWO), simulated annealing algorithm (SA) and particle swarm optimization (PSO). Two black box approaches, a feedforward neural network (FNN) and a long short-term memory (LSTM) network, were also employed for comparison. Additionally, an adaptive neuro-fuzzy inference system (ANFIS) served as a gray box model for river thermal regimes. The models were evaluated based on accuracy, complexity, generality and interpretability. Performance metrics, such as the Nash–Sutcliffe efficiency (NSE), showed that the LSTM model achieved the highest accuracy (NSE = 0.96) but required significant computational resources. In contrast, evolutionary algorithm-based models offered acceptable performance while reducing the computational complexities of LSTM, with all models achieving NSE values above 0.5. Considering interpretability, accuracy and complexity, evolutionary-based nonlinear models are recommended for general applications, such as assessing thermal river habitats. For tasks requiring very high accuracy, the LSTM model is preferred, while ANFIS provides a balanced trade-off between accuracy and interpretability, making it suitable for engineers and ecologists. While all models demonstrate similar generality, this model is developed for a specific location. For other locations, independent models with a similar architecture would need to be developed. Ultimately, the choice of model depends on specific objectives and available resources.

Achieving 32.9% efficiency in Pb-Based quantum dot solar cells via SCAPS-1D simulation optimization

Abstract This study presents a comprehensive investigation and in-depth analysis of the optimization of Pb-based quantum dot solar cells (QDSCs), concentrating on the influences of doping concentration, absorber layer thickness, defect density, temperature, and resistive elements. We systematically examined three absorber materials: lead sulfide (PbS), tetrabutylammonium iodide-treated PbS (PbS-TBAI), and lead selenide (PbSe) quantum dots (QDs). Optimal doping concentrations of 1 × 10 17 cm−3 for PbS and 1 × 10 22 cm−3 for both PbS-TBAI and PbSe were identified. Our findings reveal that precise control of these parameters can significantly enhance power conversion efficiency (PCE), achieving values of 24.6%, 28%, and 26.2% for PbS, PbS-TBAI, and PbSe, respectively. Additionally, we investigated the impact of absorber layer thickness on device performance. We discovered that a 1 µm thickness for PbS yields a maximum PCE of 32.9% due to balanced photon absorption and reduced Shockley–Read–Hall recombination. Conversely, PbSe’s performance declined with increased thickness because of its layer-dependent bandgap. We found that lower defect densities ( 1 × 10 14 cm−3) critically improve PCE and fill factor across all materials. The temperature-dependent studies demonstrated that PbS-TBAI exhibits remarkable resilience, maintaining efficiency under thermal stress due to effective surface passivation. Analyses of series and shunt resistances highlighted the importance of minimizing internal resistances to optimize device performance. The proposed device structure comprises a fluorine-doped tin oxide front contact layer, a silver sulfide (Ag2S) electron transport layer, the QD absorber layer (PbS, PbS-TBAI, or PbSe), and copper(I) oxide (Cu2O) hole transport layer. Utilizing cascade band alignment, we achieved a record PCE of 32.9%. This research highlights the significant potential of Pb-based QDSCs for achieving high efficiencies through promising material and structural optimization, positioning them as competitive candidates for next-generation solar technologies. The results provide a valuable understanding of designing high-performance QDSCs, paving the way for their integration into sustainable energy solutions.

Numerical simulation and parametric optimization of harmonic pulse water flooding technology

Pulse water injection technology is an emerging enhanced oil recovery method that improves oil-water interface and flow characteristics by periodically varying the injection rate. This study, using the TOUGH2 simulator, examines the impact of different pulse injection waveforms (sine, triangle, and square) on the recovery factor in medium to low-permeability carbonate reservoirs. It highlights that pulse water injection outperforms constant rate injection, with the triangle waveform yielding the best recovery rates. Through controlled simulations, findings indicate that increasing pulse injection time and amplitude enhances recovery and water flow. However, frequency variations in ultra-low frequency pulses have minimal effects on recovery. Additionally, the efficiency of pulse water injection is inversely related to reservoir porosity and permeability, while higher oil viscosity significantly boosts recovery efficiency. Conversely, increasing water temperature reduces dynamic viscosity, affecting the oil-water interface lag and decreasing overall displacement efficiency. The study also notes significant differences between heterogeneous and homogeneous reservoir models, emphasizing the need for future large-scale numerical modeling to consider reservoir heterogeneity. These findings provide valuable insights for optimizing pulse water injection in medium to low-permeability reservoirs, supporting practical oil field development strategies.

Geometric parameters optimization of tap via response surface methodology and nondominated sorting dung beetle optimization algorithm

Taps are widely used in the thread machining system. However, excessive loads during the tapping process can increase tap wear, which can affect tap life. Therefore, effective methods need to be proposed to solve the problems of excessive load and severe wear during tap tapping. This articleproposes a geometric parameters (e.g. rake angle ( Ky), cone angle ( Kr), helix angle ( Kh), etc.) optimization method based on response surface methodology (RSM) and nondominated sorting dung beetle optimization algorithm for a tap, exemplified through the tapping process of 304 stainless steel. This method is based on axial force ( Fa) and torque ( T). Firstly, the finite element model for the tapping process of 304 stainless steel is established, and the orthogonal simulation is carried out. Secondly, predictive models of Fa and T are established according to the RSM. The interaction effects of geometric parameters on Fa and T are revealed. After that, the nondominated sorting dung beetle optimization algorithm is implemented for the optimization of Ky, Kr, and Kh. The TOPSIS evaluation model is introduced to evaluate the Pareto optimal solution. Finally, some verification experiments are finished. The optimized tap shows a reduction in average axial force by 44.69 N and a decrease in average torque by 2.67 N·m. Under the same conditions, the flank wear width is reduced by 15.71%. The optimization method of tap geometry parameters in this paper is proposed based on finite element simulation and intelligent optimization algorithm. This method can provide technical guidance for the optimization of tool geometric parameters.

Hybrid in-situ and ex-situ hydrolysis of catalytic epoxidation neem oil via a peracid mechanism

The depletion of oil reserves and their price and availability volatility raise researchers’ concerns about renewable resources for epoxidized material. This study aims to produce in situ and ex-situ hydrolyzed dihydroxy stearic acid via the epoxidation of neem oil. Epoxidized neem oil was synthesized using in situ-generated performic acid. The Taguchi method was employed to optimize hydrolysis, aiming for maximum production of dihydroxystearic acid. The Taguchi method’s signal-to-noise (S/N) ratio analysis identified optimal conditions for producing dihydroxy stearic acid with a maximum hydroxyl value of 129.4 mg KOH/g: (1) water/neem oil molar ratio of 2:1, (2) water addition time of 90 min, and (3) reaction stop time of 120 min. ANOVA revealed the significant order of parameters as reaction stop time > water addition time > water/neem oil molar ratio. Lastly, a mathematical model was developed using MATLAB, applying the fourth-order Runge–Kutta method and simulated annealing optimization to determine the best-fitting kinetic model. This research aids in transforming neem oil into a value-added product, reduces petroleum dependence, and provides key insights into reaction kinetics for industrial applications.