Simulation Optimization Method Research Articles

Customer-to-customer returns logistics: Can it mitigate the negative impact of online returns?

Customer returns are a major problem for online retailers due to their economic and environmental impact. This paper investigates a new concept for handling online returns: customer-to-customer (C2C) returns logistics. The idea behind the C2C concept is to deliver returned items directly to the next customer, bypassing the retailer’s warehouse. To incentivize customers to purchase C2C return items, retailers can promote return items on their webshop with a discount. We build the mathematical models behind the C2C concept to determine how much discount to offer to ensure enough customers are induced to purchase C2C return items and to maximize the retailer’s expected total profit. Our first model, the base model (BM), is a customer-based formulation of the problem and provides an easy-to-implement constant-discount-level policy. Our second model formulates the real-world problem as a Markov decision process (MDP). Since our MDP suffers from the curse of dimensionality, we resort to simulation optimization (SO) and reinforcement learning (RL) methods to obtain reasonably good solutions. We apply our methods to data collected from a Dutch fashion retailer. We also provide extensive numerical experiments to claim generality. Our results indicate that the constant-discount-level policy obtained with the BM performs well in terms of expected profit compared to SO and RL. With the C2C concept, significant benefits can be achieved in terms of both expected profit and return rate. Even in cases where the cost-effectiveness of the C2C returns program is not pronounced, the proportion of customer-to-warehouse returns to total demand becomes lower. Therefore, the system can be defined as more environmentally friendly. The C2C concept can help retailers financially address the problem of online returns and meet the growing need for reducing their environmental impact.

Design of Static Output Feedback Suspension Controllers for Ride Comfort Improvement and Motion Sickness Reduction

This paper presents a method to design a static output feedback active suspension controller for ride comfort improvement and motion sickness reduction in a real vehicle system. Full-state feedback controller has shown good performance for active suspension control. However, it requires a lot of states to be measured, which is very difficult in real vehicles. To avoid this problem, a static output feedback (SOF) controller is adopted in this paper. This controller requires only three sensor outputs, vertical velocity, roll and pitch rates, which are relatively easy to measure in real vehicles. Three types of SOF controller are proposed and optimized with linear quadratic optimal control and the simulation optimization method. Two of these controllers have only three gains to be tuned, which are much smaller than those of full-state feedback. To validate the performance of the proposed SOF controllers, a simulation is carried out on a vehicle simulation package. From the results, the proposed SOF controllers are quite good at improving ride comfort and reducing motion sickness.

Conjunctive management of groundwater and surface water resources using a hybrid simulation–optimization method

ABSTRACT This study aimed to determine the optimal conjunctive utilization of groundwater and surface water resources in the Halil River basin, one of the most significant study regions in Kerman Province (Iran). Multi-verse optimizer (MVO) and the ANFIS (adaptive neuro-fuzzy inference systems) simulation model, known as the MVO–ANFIS simulation–optimization model, were used for this purpose. Moreover, the optimal exploitation policy for the studied basin was presented. The ANFIS model yielded a coefficient of determination greater than 0.99 in Baft, Rabor, and Jiroft. This model had a high capability to simulate groundwater levels in these three regions. Therefore, the ANFIS model was adopted as the simulation model to predict the aquifer water table in these regions. Regarding the conjunctive utilization of groundwater and surface water resources, the exploitation policy resulting from the MVO–ANFIS simulation–optimization model had a desirable performance by supplying 91.70, 87.75, and 97.58% of the total demands of Baft, Rabor, and Jiroft, respectively. Moreover, results of water system performance indicators, including reliability (82.96, 72.65, 95.07), resiliency (70, 53.47, 80), vulnerability (29.54, 25.64, 17.02), and sustainability (74.24%, 66.10%, 85.78%) in the mentioned regions, respectively, showed the appropriate performance of the proposed model for the simulation–optimization problem.

Estimating elastic properties of sediments by pseudo-wells generation utilizing simulated annealing optimization method

The hydrate concentration model considerably affects elastic properties, including bulk and shear modulus. Defining seismic properties of sediments, such as compressional and shear wave velocity and density, provides valuable information to identify rock facies and fluid types. This information commonly results from pre-stack seismic inversion, while post-stack seismic information provides acoustic impedance as a layer-based property. Traditionally, seismic inversion requires well logs to produce an initial guess of inversion routines and provide a low-frequency part of the amplitude spectrum. Eventually, seismic inversion methods could not be performed in the areas without well-log data, such as deep sea areas. In such cases, pseudo-well logs derived from pre-stack seismic data are a solution. Pseudo-well generation is a title used to estimate the elastic parameters of sediments in areas, such as deep marine environments, where drilled wells are absent or sparse. Metaheuristic optimization algorithms are suitable tools for minimizing the cost function as they best match real and synthetic seismic data. In this study, the SEAM earth model has been used as a reference to investigate the quality of pseudo-well generation utilizing a simulated annealing (SA) algorithm as an optimization method of property model change, which minimizes the cost function of seismic inversion. As a result, considering an initial model type of the SEAM model, simultaneous seismic inversion introduced the best compressional and shear wave velocities and density logs, which provide the best real and synthetic seismic data match when synthetic data is calculated from the simplified Zoeppritz equation.

Seepage propagation simulation of a tunnel gasketed joint using the cohesive zone model

The gasketed joint plays a critical role in the waterproof or water-leakage resistance performance of tunnel segmental linings. This study presents a novel numerical simulation method based on the cohesive zone model (CZM) to accurately analyze the seepage phenomena along the interface of elastic sealing gaskets in shield tunnel joints. While traditional finite element methods are widely used, they cannot simulate interface opening, limiting their applicability. To overcome this challenge, the proposed method leverages the concept of hydraulic fracturing and redefines the seepage problem as a seepage problem of elastic impermeable media. By establishing a two-dimensional hydraulic fracturing model using the CZM, this study conducts a comprehensive sensitivity analysis of the cohesive element parameters and validates the accuracy and effectiveness of simulating seepage along the interface of elastic impermeable media. Based on the insights gained from the sensitivity analysis, a seepage calculation model is successfully developed, considering the unique characteristics of elastic sealing gaskets. The model is applied to three cross-sectional shapes of elastic sealing gaskets, and their seepage characteristics are analyzed based on the interface opening displacement and liquid pressure distributions during seepage. The results highlight the SC3 cross-sectional shape, which exhibits minimal opening displacement and the highest net water pressure at the moment of seepage occurrence, demonstrating superior waterproof performance compared to other forms. Through the introduction of the CZM, comprehensive parameter analysis, validation, and practical application, this study overcomes the limitations of traditional finite element methods in simulating interface opening. The proposed model, which more accurately captures seepage characteristics than other methods, provides an optimized simulation method for analyzing hydraulic coupling issues related to seepage at shield tunnel joints and is a significant contribution to the field, enhancing our understanding and enabling more accurate analyses of seepage phenomena in shield tunnel joints using elastic sealing gaskets.

Cost optimization of shuttle tanker offloading operations

This paper proposes an optimization model accounting for voyage costs, charter costs, and the cost of lost production for scheduling the operation of shuttle tankers that are used to transport the oil from the oil production units in deep water, to onshore terminals. This model introduces the effect of the costs from lost production, which can vastly surpass both voyage and charter costs, improving on previous research on the scheduling of such operations, which has focused solely on the optimization of voyage and charter costs, with the cost of lost production in case of failure of the logistics system being ignored. A simulation-optimization method is proposed, allowing for uncertainty in weather condition and oil production. Given the highly structured nature of the policies governing the underlying inventory routing problem, the problem can be recast as a routing problem, to great computational advantage. A novel efficient voyage-based optimization formulation is proposed, and optimality is achieved for instances with a size much larger than those of previous research. The application of the method is illustrated for Brazil, where problem instance sizes faced in practice are considerably large. Sensitivity analysis shows the considerable impact of costs from lost production on the obtained schedules.

Estimation of constitutive parameters for shape memory polymer thermo-mechanical cycle by an integrated simulation and inverse optimization method

In this work, a method for inverse optimization of shape memory polymer material parameters is proposed to determine the intrinsic model parameters more quickly and accurately for simulated experimental test analysis. First, the basic mechanical properties of the material are characterized experimentally and the parameters of the generalized Maxwell model based on SMP material are fitted. Then, a joint simulation inversion optimization method is built in the ISIGHT-MATLAB-ABAQUS integrated environment to optimize the time domain Prony series parameters using downhill simplex optimization algorithm. The results show that the average errors of the stress curves before and after the optimization of the cooling solid phase and the experimental results are 0.31 MPA and 0.23 MPA, respectively, and the average errors of the strain curves in the warming recovery phase are 1.70% and 0.40%, respectively. After the inversion optimization, the average errors of the stress curves are optimized by 25.8% and the average errors of the strain curves are optimized by 76.5%. This method makes the simulation results more stable with the experimental results, which can effectively reduce the experimental cost and promote the shape memory property of SMP to be widely used in engineering.

Confined aquifer dewatering optimization with a modified simulation–optimization method capable of determining the optimal well screen length and depth

Simulation-optimization methods are widely used in dewatering optimization. However, traditional simulation–optimization methods do not address the optimization of well screen length and depth. This study proposes a modified simulation–optimization method for confined aquifer dewatering optimization, which is capable of determining the optimal screen length and depth. The proposed method is based on the linear programming method, and the multivariate adaptive regression splines method is also introduced to develop the prediction model for the parameters required in the linear programming model. A hypothetical case of deep excavation dewatering was optimized using the proposed method to demonstrate its feasibility, with the optimal pumping rate, screen length and depth of each well computed. Furthermore, parametric studies were performed to investigate the effects of some key factors on the optimization results, such as the number of considered pumping wells, required drawdown, insertion ratio of the waterproof curtain, aquifer anisotropy coefficient, and prescribed well screen. The results show that optimal total pumping rate and screen length generally increase with increasing required drawdown and aquifer anisotropy coefficient, while they decrease with increasing well number and insertion ratio of the waterproof curtain. Adjusting screen length is more critical to the optimization results since lower screen depth is always preferred. Optimizing well screen is more essential for higher well number, insertion ratio of the waterproof curtain, and lower aquifer anisotropy coefficient.

Optimal energy storage system design for addressing uncertainty issues in integration of supply and demand-side management approaches

The primary goal of Sustainable Development Goal 7 (SDG 7) is to increase renewable energy use to reduce reliance on fossil fuels and mitigate climate change. Energy-intensive industries can benefit from in-house renewable power generation, reducing their reliance on fossil fuel-based grid power and making processes greener. However, integration among power generation/purchase, energy storage systems (ESS), and power consumption is crucial to overcome the intermittent nature of renewable power sources. ESS plays a vital role in increasing resilience and optimizing material production based on power availability and pricing. The efficacy of ESS needs critical evaluation considering cost, efficiency, and uncertainties related to renewable power generation and material product demand. The paper proposes two scenario-based optimization approaches to assess the impact of uncertainties on the integrated supply and demand side management (ISDM) system, focusing on lithium-ion batteries and cryogenic energy storage (CES). Compared to the conservative approach of the scenario-based robust optimization (SRO) method, the proposed stochastic simulation optimization (SSO) method provides a ‘risk-neutral’ solution, which is 6.45% less than the minimum expected cost solution. The analysis also suggests that lithium-ion batteries are more economically effective for the proposed integrated framework than CES, resulting in almost a 29% reduction in operating costs compared to no battery option. The proposed framework could contribute to sustainable and economically viable energy management practices in energy-intensive industries. Further research and implementation of such frameworks could accelerate the adoption of renewable energy and energy storage technologies in industrial processes.

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.