Simulation Optimization Method Research Articles

Robust design optimization of a multi-body system with aleatory and epistemic uncertainty

There are several issues in robust design optimization of the multi-body system with aleatory and epistemic uncertainty, such as the difficulty in the multi-source uncertainty characterization and low efficiency in dynamic modeling, analysis and the corresponding solving strategy for its robust design optimization. To address these problems, this paper firstly constructs a unified analysis framework for aleatory and epistemic uncertainty for the multi-body system based on probability theory and evidence theory. Taking a multi-body system of a shipborne artillery model as an example, the transfer matrix method is next employed to rapidly calculate the dynamic response of the muzzle. The results are compared with those by the finite element method to verify the accuracy and efficiency of the transfer matrix method. A decision model of the robust design optimization with both aleatory and epistemic uncertainty is proposed. Based on the polynomial chaos expansion-Kriging model, subset simulation optimization method and a weighted single-objective solving strategy, the multi-objective robust design optimization problem involving aleatory and epistemic uncertainty is then transformed into a single-objective optimization one. Finally, results of both the multi-objective and weighted single-objective robust design optimization methods for the shipborne artillery model are compared to demonstrate the performance of the proposed method.

Design and Experimental Study of a Biomimetic Pod-Pepper-Picking Drum Based on Multi-Finger Collaboration

In order to reduce ground drop loss during mechanical pepper picking and improve the net recovery rate, a drum snap finger picking device was designed. The picking device is mainly composed of a picking drum and auxiliary picking components; the picking finger arrangement was designed biomimetically and its structure and operating parameters were optimized by the DEM (discrete element method). According to the physical and mechanical characteristics of the pepper and the simplified three-dimensional model of the picking device, a virtual simulation model of the pepper-picking device was established using the EDEM software. Through simulation analysis and using the orthogonal test method, the main factors which affect the ground drop loss rate of pepper and their optimal parameter combination values were determined. The simulation results were verified by a pepper-picking field experiment. Orthogonal tests show that, when the picking drum speed (V′) is 210 rpm, the pepper-feeding speed (V″) is 1100 mm·s−1, the bending angle of each picking spring tooth (C) is 162°, and each group of circumferential fingers has rows, the picking device has a good picking effect. At this time, the ground drop loss rates in both the simulation and field test were 7.50% and 7.85%, respectively, and the drop error was only 4.46%, which was within the allowable range. The design form and parameter optimization simulation method in this paper provide an important reference for the design and optimization of pepper-harvesting machinery.

Transfer Matrix Model for Emission Profile Optimization of Radial Gratings

AbstractRadial Bragg gratings are commonly used to enhance light extraction from quantum emitters, but lack a well‐suited, fast simulation method for optimization beyond periodic designs. To overcome this limitation, an algorithm based on the transfer matrix model (TMM) to calculate the free‐space emission of such gratings is proposed and demonstrated. Using finite difference time domain (FDTD) simulations, free‐space emission, and transfer matrices of single grating components are characterized. The TMM then combines any number of components to receive the total emission. Randomized benchmarks verify that results from this method agree within 98% with FDTD while reducing simulation time by one to two orders of magnitude. The speed advantage of this approach is shown by maximizing emission of a fifteen‐trench circular grating into a Gaussian mode. It is expected that this novel algorithm will facilitate the optimization of radial gratings, enabling quantum light sources with unprecedented collection efficiencies.

Study on the Solution and Variation Law of Diffusion Coefficient Based on the Numerical Simulation Optimization Method.

Since the diffusion coefficient is a key parameter to characterize the diffusion rate of methane molecules, its measurement and solution have always been a research hotspot. The diffusion coefficient is normally solved through analytical solutions of theoretical models, which is complex and poorly applicable. In comparison, the numerical simulation optimization method can seek a solution easily and quickly, providing a clue for solving such problem. In this paper, first, gas desorption experiments were conducted on coal samples with different initial gas equilibrium pressures, coal particle sizes, and metamorphic degrees. Combined with existing theoretical models, the numerical simulation optimization method was adopted to solve the diffusion coefficient of the coal particle. Furthermore, the applicability and advantages of the numerical simulation optimization method were discussed. Finally, the variation law of the diffusion coefficients was analyzed. The results demonstrate that the numerical simulation optimization method can not only solve the diffusion coefficient easily and quickly but also reveal the law of diffusion concentration with time. The d values between the solution results and the experimental data under different conditions are all smaller than 0.2, which proves the effectiveness and accuracy of the simulation optimization method. The diffusion coefficient of gas from coal particles is unrelated to the initial gas equilibrium pressure, yet it has a Z-shaped relationship with the coal particle size and a V-shaped relationship with the metamorphic degree.

Uniformity optimization of ion beam sputtering coating equipment based on strong current ion source

The widespread application of ion beam sputtering coating, especially in optical devices, requires the improvement of beam current intensity and uniformity of large-area uniform coatings. The advent of high current Penning sources offers a potential solution. This study introduces an automated optimization simulation method based on a three-electrode extraction system to investigate its influence on ion beam quality and uniformity. Focusing on high current intensity and uniformity, our simulation explores the effects of plasma electrode, inhibition electrode, and extraction electrode angles and distances on ion beam performance. Evaluation metrics include average beam intensity density, average energy of a single particle, and reciprocal variance of each macro particle position, which are achieved through normalization functions, allowing comprehensive comparison of simulation results. To assess coating efficiency, we estimate sputtering yield and depth. The study identifies patterns among electrodes and emphasizes the influence of different ion ratios on beam extraction. The results indicate that optimizing the angle of the plasma electrode and the distance of the suppressed electrode yields a highly uniform ion beam for low charge ions. However, for highly charged ions, similar optimization will reduce the current strength, so compensation needs to be achieved through electrode shape optimization. This research provides a model for systematically optimizing the three-electrode extraction system, guiding researchers in achieving optimal solutions based on ion source characteristics and application requirements. Additionally, we introduce a method of estimating the sputtering depth of mixed ion beams. This study provides valuable insights for advancing ion beam sputtering coating technology and reference for making the decision on design and application of ion source.

Adaptive DBSCAN Clustering and GASA Optimization for Underdetermined Mixing Matrix Estimation in Fault Diagnosis of Reciprocating Compressors.

Underdetermined blind source separation (UBSS) has garnered significant attention in recent years due to its ability to separate source signals without prior knowledge, even when sensors are limited. To accurately estimate the mixed matrix, various clustering algorithms are typically employed to enhance the sparsity of the mixed matrix. Traditional clustering methods require prior knowledge of the number of direct signal sources, while modern artificial intelligence optimization algorithms are sensitive to outliers, which can affect accuracy. To address these challenges, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with Adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering as initialization, named the CYYM method. This approach incorporates two key components: an Adaptive DBSCAN to discard noise points and identify the number of source signals and GASA optimization for automatic cluster center determination. GASA combines the global spatial search capabilities of a genetic algorithm (GA) with the local search abilities of a simulated annealing algorithm (SA). Signal simulations and experimental analysis of compressor fault signals demonstrate that the CYYM method can accurately calculate the mixing matrix, facilitating successful source signal recovery. Subsequently, we analyze the recovered signals using the Refined Composite Multiscale Fuzzy Entropy (RCMFE), which, in turn, enables effective compressor connecting rod fault diagnosis. This research provides a promising approach for underdetermined source separation and offers practical applications in fault diagnosis and other fields.

Agent-based Decision Making and Control of Manufacturing System Considering the Joint Production, Maintenance, and Quality by Reinforcement Learning

Taking an integrated approach towards production, maintenance, and control in manufacturing systems is crucial due to the profound impact of their interconnections. Investigating these aspects in isolation may lead to infeasible solutions. This research focuses on the real-time and autonomous decision-making process concerning joint production planning, maintenance, and quality problems in a stochastic deteriorating production system with limited maintenance activities. Formulating the problem as a continuous semi-Markov decision process accounts for the complexities of the real production system and the occurrence of events over an uneven and continuous period. While dynamic programming is a common tool for addressing joint optimization problems, it has limitations, such as the curse of dimensionality. In this study, the optimal policy of the decision-maker agent is obtained by the goal-directed machine learning method called (R-SMART) and agent-based modeling. To the author's knowledge, the proposed approach is novel, and there is little research on such an implementation of the joint optimization problem. The quality of the optimal policy is evaluated through heuristic and simulation-optimization methods in various scenarios. The results demonstrate that the proposed RL-based method outperforms others in most scenarios, achieving a stable, integrated optimal policy.

FEM simulation optimization and slab method on rotating extrusion of thin-walled round tube under constant shear friction

This research considering constant shear friction is to explore the rotating extrusion of thin-walled round tube based on FEM simulation and slab method, and compares the results of both models to realize the variations and acceptance. The effective stress, the effective strain, the velocity field, the average thickness, the extrusion force, and the extrusion torque can be obtained from this study. It reveals the average thickness obtained from rotating extrusion is more uniform than that obtained from no rotating extrusion. The FEM optimization for extrusion force can be combined with Taguchi method, the L934 orthogonal table considers the four control factors which are outer diameter to thickness ratio, frictional factor, rotating angular velocity, half die angle, and three levels. The ranking of influence factors and the optimization combination can be obtained from FEM simulation optimization. Eventually the extrusion force between FEM simulation and slab method is compared under rotating angular velocity, 0.2 rad/s, the maximum error is 10.49 % and the minimum error is -0.29%; the average error is 4.16%, so the trend is in good agreement each other. Therefore, the both models can be verified.

An efficient simulation optimization method for the redundancy allocation problem with a chance constraint

We explore the Redundancy Allocation Problem (RAP) under the objective of minimizing the cost of a production system of general topology in which system reliability is treated as a chance constraint. A novel simulation optimization-based solution method grounded in the concepts of the trust region and response surface methodology is proposed to efficiently solve the generalized RAP (GRAP) under random system survival times. The generalizability of the RAP model and efficiency of the solution method allows for our approach to be utilized in a wide variety of real-world applications. We demonstrate in a series of numerical experiments based on production systems of varying complexity that the finite convergence of the proposed method is much more efficient than the commonly-used genetic algorithm. It is shown that on a simple bridge network, only the proposed algorithm can find the true optimal solution to the GRAP under an allotted computational budget. On a complex network which includes series, parallel, and logical relationships, the proposed algorithm is also shown to find solutions to the GRAP which have substantially lower total system cost than those found by GA under a wide variety of scenarios.

A Simulation Optimization Approach for Wetland Conservation and Management in an Agricultural Basin

Decreasing water quantity and growth in water demand have increased the competition between satisfying societal water needs and protecting ecosystem requirements. Wetlands are some of the most productive ecosystems on Earth. They provide various services to people’s livelihoods, in addition to being suitable habitats for many plant and wildlife species. However, wetlands are under threat of loss and degradation due to anthropogenic activities, particularly the diversion of water for irrigation. The flow regime is usually considered the most crucial ecological factor and a key component of wetland management. So, determining the allocation of environmental requirements is a main factor for managing, restoring, and protecting wetlands, and it is crucial to reach a compromise for optimal water allocation between different sectors. For this purpose, in this research, a new approach is developed to achieve the optimal environmental flow of the wetland in an agricultural-dominated basin using a combination of remote sensing and the simulation optimization method. Waterbirds and vegetation are used as bioindicators of wetland ecosystems. First, using remote sensing data and analyses, we obtained the interrelation between the wetland water regime, vegetation, and waterbird characteristics using different time series of Landsat spectral indices. Then, by employing the long-term simulation optimization (WEAP-MOPSO) model, the optimal e-flow of the wetland is evaluated in such a way that the suitable ecological condition of the wetland is achieved and the wetland is able to provide its functions and services.

Vibrational Effects on the Acoustic Performance of Multi-Layered Micro-Perforated Metamaterials

Broadband noise reduction over the low–mid frequency range in the building and transportation sectors requires compact lightweight sound absorbers of a typical subwavelength size. The use of multi-layered, closely spaced (micro-)perforated membranes or panels, if suitably optimized, contributes to these objectives. However, their elasticity or modal behaviors often impede the final acoustical performance of the partition. The objective of this study is to obtain insights into the vibrational effects induced by elastic limp membranes or panel volumetric modes on the optimized sound absorption properties of acoustic fishnets and functionally graded partitions (FGP). The cost-efficient global optimization of the partitions’ frequency-averaged dissipation is achieved using the simulated annealing optimization method, while vibrational effects are included through an impedance translation method. A critical coupling analysis reveals how the membranes or panel vibrations redistribute the locations of the Hole-Cavity resonances, as well as their cross-coupling with the panels’ first volumetric mode. It is found that elastic limp micro-perforated membranes broaden the pass-band of acoustic fishnets, while smoothing out the dissipation ripples over the FGP optimization bandwidth. Moreover, the resonance frequency of the first panels mode sets an upper limit to the broadband optimization of FGPs, up to which a high dissipation, high absorption, and low transmission can be achieved.

Optimization of the chamber of OWC to improve hydrodynamic performance

Nearly 71% of Earth's surface area is occupied by the ocean, which is abundant in energy, with wave energy being one of its essential components. As a result, the investigation of wave energy generation devices is a crucial matter. This paper analyzes the performance of the oscillating water column (OWC) wave energy conversion device under the influence of unidirectional regular waves and optimizes the OWC device accordingly. The impact of different structural dimensions on the first-stage conversion efficiency of the OWC device is determined. Moreover, to evaluate the performance of the OWC device in real sea conditions, the wave climate at Sanmen Gorge is employed as the experimental parameter. The study examines the performance and characteristic response of the OWC device regarding the air chamber width, thickness of the front wall, and draught of the front wall. The findings reveal that the width of the OWC air chamber and the draught of the front wall significantly affect the OWC performance. Decreasing the width of the air chamber results in a stable horizontal plane, eliminating the occurrence of standing wave phenomena. As the draft of the front wall increases, the reflection coefficient of the air chamber gradually increases, and it becomes difficult for the wave to penetrate into the air chamber section. The Latin hypercube design experiment method was used to select experimental points and establish quadratic polynomial response functions. The OWC device was optimized based on multi-island genetic algorithm. The simulation and fault tolerant optimization methods presented in this paper can be widely used to improve system performance.

An efficient approximation to the pull policy for hybrid manufacturing and remanufacturing systems with setup costs

We consider a continuous-review inventory control problem for a hybrid manufacturing and remanufacturing system with product recovery and setup costs. A pull policy has been proposed in the literature, however, finding its optimal parameters requires an exhaustive search and a single cost function evaluation is itself complex. We propose a tractable approach to finding these parameters using an interim policy called double (r, Q) with parameters ( r m , Q m , r r , Q r ) . When the inventory position of the serviceable item reaches rr , a remanufacturing lot size Qr is setup if recoverable inventory is sufficient. Otherwise, we allow the inventory position to decrease further. As it drops to rm , a manufacturing lot size Qm is placed. Unlike the pull policy, this interim policy suspends the remanufacturing option when the inventory position is less than rr . This facilitates an efficient approximation of the recoverable inventory which decouples the double (r, Q) problem into two standard (r, Q) problems. It can then be efficiently solved using a modification of existing (r, Q) algorithms for two instances. Numerical studies show that our approach performs well relative to the optimal pull policy with parameters estimated from an extensive Simulation-Optimization method and other heuristics found in the literature. The approach is also extended to correlated demand and return arrivals.

A Model Review on Joint Optimization of Part Quality Inspection Planning, Buffer Allocation, and Preventive Maintenance in SMMS

In serial multi-stage manufacturing systems (SMMS), optimization of part quality inspection planning (PQIP), buffer allocation problem (BAP), and preventive maintenance (PM), individually and jointly, is attracting researchers’ attention. The model formulation for complicated manufacturing systems and the previously mentioned joint decisions is very beneficial given the interdependencies between the various manufacturing functions. As a result, this paper evaluates the literature on joint optimization of the multi-stage serial production system. The literature is classified based on the decision variables basis to represent each manufacturing function [inspection sample size and allocation (PQIP), buffer sizing and allocation (BAP), and preventive maintenance scheduling (PM)], and a general example model is presented in each classification, with a summary of recent studies, solution methods, research gaps, and future research recommendations. In the integrated models, almost all the studies considered only two functions, with that it is worth noting that research into the optimization of over two functions is still in its beginning. Furthermore, most studies neglected many of the real industrial settings that should also be integrated into the model. And finally, there was no specific solution technique recommended in the literature, yet a general simulation optimization method was used to generate and evaluate the combinatorial complex joint models.

A Simulation Optimization Method for Coordination of Production, Transportation and Sales

This study considers a problem of coordinating production, transportation and sales in a multi-echelon supply chain network. A simulation model is built to generate the random customer demands at different locations, which are affected by a marketing strategy. Customer demands need to be satisfied by the supply chain through production, transportation and distribution. The optimization problem for coordination of production, transportation and distribution is first formulated as a linear programming with demands as input parameters in the constraint. Our objective is to maximize the expectation of the optimal profit of the supply chain given random demands by selecting an optimal marketing strategy. A simulation optimization technique is proposed to control the generation of random demands and solve the linear programming for efficiently learning the optimal marketing strategy. Numerical results show that our method can significantly improve the expected profit of the supply chain and reduce the computational burden of solving linear programming for achieving a given level of probability of correct selection of the optimal marketing strategy. Furthermore, we extend the optimization problem to a mixed integer programming and also demonstrate the computational efficiency of our proposed method.

Kinematic analysis and process optimization of root-cutting systems in field harvesting of garlic based on computer simulation technology.

Root cutting is an important process in garlic field harvesting but is the weakest link in the full mechanization of garlic production. To improve the current situation of technological backwardness and poor operational quality of mechanized garlic root-cutting in the main garlic-producing regions of China, this study combined the physical characteristics and agronomic requirements of garlic plants, and proposed an innovative floating root-cutting technology for garlic combine harvesters that enables the top alignment of bulb, adaptive profiling floating of cutter, and embedded cutting of roots. Through the kinematic analysis of the floating cutting process, the coordinate equations of the initial contact point of the bulb, the mathematical model of the floating displacement of the cutting component. Using computer simulation techniques, the dynamic simulation study of the floating cutting process was carried out in the rigid-flexible coupling numerical simulation model of root-cutting mechanism and garlic plant. The influence law of garlic conveying speed, extension spring preload force and stiffness on the floating displacement of the cutting component and the angular velocity of swing arm reset and its formation causes were analyzed by a single-factor simulation test. The key operating parameters of the root-cutting mechanism were optimized through the computerized virtual orthogonal test and fuzzy comprehensive evaluation. The significance of the factors affecting the floating cutting performance decreased in the following order: extension spring preload force, garlic conveying speed and extension spring stiffness. The optimal parameter combination of the root cutting mechanism obtained from the optimization were as follow: extension spring preload force was 16 N, garlic conveying speed was 0.8 m/s, and extension spring stiffness was 215 N/m. Tests conducted with the optimal parameter combination yielded a root excision rate of 92.72%, which meets the requirements of Chinese garlic field harvesting quality. This study provides computer simulation optimization methods for the optimal design of the root-cutting mechanism, and also provides technical and equipment support for the full mechanization of garlic production in China.

Aspects of Negative Entropy Flow in Cyclones: From Classical to Quantum

The study focuses on exploring the concept of negative entropy flow in cyclones and investigates the potential of applying quantum-inspired approaches to better understand and estimate the negentropy of these complex systems. By incorporating principles from quantum mechanics, such as the second law of thermodynamics and Gibbs energy, we aim to improve the accuracy of entropy predictions for cyclones. Classical methods for estimating entropy flow, such as Shannon entropy and von Neumann entropy, are examined, along with their limitations in capturing the complex dynamics of cyclonic systems. Additionally, the project utilizes the simulated annealing optimization method to explore the quantum aspects of entropy flow in cyclones. The objective is to provide insights into the negentropy of cyclones and its potential applications in weather forecasting. This project analyses data from two recent cyclones "Mocha" and "Biporjoy" and estimates their negentropy using the developed quantum-inspired algorithms. The findings contribute to a deeper understanding of the entropy dynamics in cyclones and showcase the potential of quantum-inspired techniques in weather forecasting and related fields.

Simultaneous identification of groundwater pollution source and important hydrogeological parameters considering the noise uncertainty of observational data.

Groundwater pollution identification is an inverse problem. When solving the inverse problem using regular methods such as simulation-optimization or stochastic statistical approaches, requires repeatedly calling the simulation model for forward calculations, which is a time-consuming process. Currently, the problem is often solved by building a surrogate model for the simulation model. However, the surrogate model is only an intermediate step in regular methods, such as the simulation-optimization method that also require the creation and solution of an optimization model with the minimum objective function, which adds complexity and time to the inversion task and presents an obstacle to achieving fast inversion. In the present study, the extreme gradient boosting (XGBoost) method and the back propagation neural network (BPNN) method were used to directly establish the mapping relationships between the output and input of the simulation model, which could directly obtain the inversion results of the variables to be identified (pollution sources release histories and hydraulic conductivities) based on actual observational data for fast inversion. In addition, to consider the uncertainty of observation data noise, the inversion accuracy of the two machine learning methods was compared, and the method with higher precision was selected for the uncertainty analysis. The results indicated that both the BPNN and XGBoost methods could perform inversion tasks well, with a mean absolute percentage error (MAPE) of 4.15% and 1.39%, respectively. Using the BPNN, with better accuracy for uncertainty analysis, when the maximum probabilistic density value was selected as the inversion result, the MAPE was 2.13%. We obtained the inversion results under different confidence levels and decision makers of groundwater pollution prevention and control can choose different inversion results according to their needs.

Discrete simulation optimization for tuning machine learning method hyperparameters

ABSTRACT An important aspect of machine learning (ML) involves controlling the learning process for the ML method in question to maximize its performance. Hyperparameter tuning (HPT) involves selecting suitable ML method parameters that control its learning process. Given that HPT can be conceptualized as a black box optimization problem subject to stochasticity, simulation optimization (SO) methods appear well suited to this purpose. Therefore, we conceptualize HPT as a discrete SO problem and demonstrate the use of the Kim and Nelson (KN) ranking and selection method, and the stochastic ruler (SR) and the adaptive hyperbox (AH) random search methods for HPT. We also construct the theoretical basis for applying the KN method. We demonstrate the application of the KN and the SR methods to a wide variety of machine learning models, including deep neural network models. We then successfully benchmark the KN, SR and the AH methods against multiple state-of-the-art HPT methods.

Simultaneous identification of groundwater contaminant source and hydraulic parameters based on multilayer perceptron and flying foxes optimization.

Groundwater contaminant source identification (GCSI) has practical significance for groundwater remediation and liability. However, when applying the simulation-optimization method to precisely solve GCSI, the optimization model inevitably encounters the problems of high-dimensional unknown variables to identify, which might increase the nonlinearity. In particular, to solve such optimization models, the well-known heuristic optimization algorithms might fall into a local optimum, resulting in low accuracy of inverse results. For this reason, this paper proposes a novel optimization algorithm, namely, the flying foxes optimization (FFO) to solve the optimization model. We perform simultaneous identification of the release history of groundwater pollution sources and hydraulic conductivity and compare the results with those of the traditional genetic algorithm. In addition, to alleviate the massive computational load caused by the frequent invocation of the simulation model when solving the optimization model, we utilized the multilayer perception (MLP) to establish a surrogate model of the simulation model and compared it with the method of backpropagation algorithm (BP). The results show that the average relative error of the results of FFO is 2.12%, significantly outperforming the genetic algorithm (GA); the surrogate model of MLP can replace the simulation model for calculation with fitting accuracy of more than 0.999, which is better than the commonly used surrogate model of BP.