Simulation Optimization Research Articles

Purpose This paper aims to propose a structured method to support decision-making in complex operational contexts by improving the efficiency of multi-objective simulation optimization (MOSO). The focus is on helping managers and analysts handle large-scale decision problems with high-dimensional search spaces, often present in production and logistics systems. Design/methodology/approach The proposed method integrates Latin hypercube design (LHD) and data envelopment analysis with variable returns to scale (DEA-VRS), including super-efficiency analysis, to identify promising regions in the search space. The approach was applied to two real-world case studies in logistics and manufacturing environments. Findings The proposed method achieved a substantial reduction in the search space, ranging from 70% to 89%, and reduced the number of optimization experiments by up to 31%. In both case studies, the reduced search space led to improved outcomes across most optimization profiles. In the logistics case, costs decreased by up to 10%, and the quantity shipped increased by up to 219%. In the manufacturing case, lead time was reduced by up to 26% while maintaining the same production output, demonstrating enhanced computational efficiency without compromising solution quality. These results confirm that the method enhances computational efficiency without compromising solution quality in complex MOSO scenarios. Practical implications The method enabled the identification of high-quality solutions with significant operational benefits. These improvements were achieved using fewer simulation runs, up to 31% less, demonstrating the method’s ability to accelerate decision-making and reduce computational effort. Its integration with existing simulation platforms and consistent performance across diverse optimization profiles make it a valuable tool for supporting data-driven decisions in complex operational environments. Originality/value This study introduces a novel combination of LHD and DEA-VRS to enhance the performance of simulation optimization methods. It contributes to both the fields of operations research and operations management by offering a robust, interpretable and computationally efficient framework for solving complex MOSO problems in industrial applications.

ABSTRACT Phase separation refers to the segregation of a uniform mixture into distinct regions with unique physical and chemical properties. This phenomenon plays a crucial role in systems containing immiscible fluids such as oil and water, with significant implications for petroleum refining, chemical manufacturing, food science, and biological processes. In this study, Monte Carlo simulations were employed to investigate phase separation phenomena in mixed oil-water particle systems. Simulations were conducted on systems with 500–2,500 particles (in increments of 500), confined within square domains with side lengths from 25 to 40 units at 5-unit intervals. Key parameters such as box size, particle density, and interaction strength were systematically varied to evaluate their influence on phase behavior and dynamics. Results indicate that a box size of L = 30 and particle number N = 2,000 produce the most distinct oil-water phase separation. Larger systems and stronger interactions facilitate the transition from homogeneous to phase-separated states. Extended simulation durations further improve phase clarity, underscoring the critical importance of equilibration at MCstep = 106 from the tested Monte Carlo time steps of 103-106. These results guide simulation optimization and deepen molecular insights into phase separation and wettability, supporting improved enhanced oil recovery (EOR) strategies.

To mitigate renewable energy curtailment and maintain long-term power balance, both planning and operational strategies must be addressed. However, most existing studies on power system capacity optimization focus on a single objective, such as economic efficiency or carbon reduction. To overcome this limitation, this paper proposes a two-stage robust capacity optimization and decision-making framework for power systems that incorporates multi-objective optimization. In the first stage, a bi-level robust capacity optimization model is developed, where the upper-level problem targets capacity expansion planning and the lower-level problem addresses chronological production simulation and operational optimization. The upper-level objectives include minimizing investment and operating costs, maximizing supply reliability, and maximizing renewable energy integration. Secondly, the NSGA-II algorithm is employed to solve the constructed bi-level multi-objective optimization model. Finally, a decision-making model based on the Best–Worst Method (BWM), entropy weighting, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is constructed to further evaluate and select among multiple Pareto-optimal solutions obtained in the first stage, thereby determining the final capacity configuration scheme. The case study demonstrates that the proposed two-stage framework maintains good stability under scenarios such as extreme weather, ensuring a power supply reliability of 98.78% and a new energy utilization rate of 98.5% under various conditions.

Numerical simulation and parametric optimization of hot deformation behavior of copper-based composite material

Dynamic simulation and regulation strategy optimization of a CFB boiler after a sudden power failure

Comprehensive performance improvement of methanol/diesel dual-fuel engines using multi-physics field coupling simulation and surrogate-assisted many-objective optimization

This paper presents a compact dual-band frequency-reconfigurable monopole antenna for sub-6 GHz wireless applications. Using a single PIN diode, the antenna switches between 2.7 GHz and 3.9 GHz bands, achieving bandwidths of 472 MHz and 1130 MHz, respectively, with peak gains up to 1.65 dB. The demand for smaller devices has driven the development of compact antennas capable of operating across multiple bands. The main benefits of this antenna include its compact size, enhanced bandwidth, and design simplicity, which is achieved by integrating slots into the patch and introducing a tiny slot etched over the ground plane. The antenna is created using an FR4 material with a thickness of 1.6 mm and dimensions of 25×15 mm². The antenna prototype was fabricated and tested to validate its performance. Simulation optimization reveals that the antenna operates with a gain of 0.9–1.65 dB and a bandwidth of (472–1130 MHz). The design also achieves a VSWR of less than 1.3 and a radiation efficiency between 74% and 78%. The performance enhancement of the reconfigurable antenna was fine-tuned utilizing microwave solvers in both computer simulation technology (CST) and advance design system (ADS).

A deep reinforcement learning based multi-agent simulation optimization approach for IGV bidirectional task allocation and charging joint scheduling in automated container terminals

The research on ceramic powders has always been one of the main research directions of laser cladding technology. This article mainly focuses on the numerical simulation method of temperature field and intelligent optimization of preparation parameters in the laser cladding preparation process of iron-based ceramic layers, in order to optimize the overall process of preparing iron-based ceramic coatings. Firstly, the generation and transfer of laser heat, as well as the heat absorption process of materials, were analyzed and constructed. After determining the Gaussian three-dimensional heat source model, the temperature field of the heat source model was simulated using ANSYS software to obtain appropriate laser power and other parameters. Then, based on various process parameters such as coating material, material supply method, pre coating thickness, laser power scanning rate, and spot size during the preparation process, a multivariate nonlinear model of processing parameters and various performance measurements was constructed with melting power and feed rate as inputs, dilution rate and overlap rate as responses. Nonlinear regression analysis was then conducted, and an improved differential evolution algorithm was used to solve the optimal solutions of various parameters in the layer preparation process. Finally, simulation experimental results were presented to verify the effectiveness and feasibility of the proposed method in this paper.

This study introduces a parametric multi-objective optimization framework for urban block morphology. It integrates micro-climate data corrected by the Urban Weather Generator (UWG), energy simulation through EnergyPlus and Honeybee, and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) within the Wallacei platform. Using Wuhan, China, a city with a representative hot-summer and cold-winter climate, as a case study, the framework simultaneously optimizes three key objectives: Average Sunshine Hours (Av.SH), Energy Use Intensity (EUI), and Average Universal Thermal Climate Index (Av.UTCI). The framework systematically links parametric modeling, environmental simulation, and evolutionary optimization to explore how block typologies and height configurations affect the trade-offs among solar access, energy demand, and outdoor thermal comfort. Among the feasible solutions, Av.SH exhibits the greatest variation, ranging from 4.30 to 7.93 h, followed by Av.UTCI (44.13 to 45.46 °C), while EUI shows the least fluctuation, from 91.69 to 93.36 kWh/m2. Key design variables, such as building type and height distribution, critically influence the outcomes. Optimal configurations are achieved by interweaving low-rise (2 to 3 floors), mid-rise (6 to 8 floors), and high-rise (15 to 20 floors) buildings to enhance openness and ventilation. The proposed framework offers a quantifiable strategy for guiding future climate-responsive and energy-efficient neighborhood design.

The Remaining Oil in Place (ROIP) in carbonate rock reservoirs is often substantial. This is due to the tendency of carbonate rocks to be oil-wet in terms of wettability. The oil's inherent property of wetting the rock causes the residual oil to adhere to the rock's pores, making it challenging to extract to the surface. One method to enhanced oil recovery (EOR) is through biosurfactant injection, i.e., sophorolipid, a fungal biosurfactant that possesses the properties of surfactants in general. This study aims to evaluate the effectiveness of sophorolipid biosurfactant injection in enhancing oil recovery in carbonates, as well as to identify the dominant mechanism at work during the injection process and optimize it through coreflooding simulation. This research was conducted through laboratory testing and validation using a simulator, comprising two phases: coreflooding tests and coreflooding simulations. Coreflooding simulation was conducted to reduce the need for coreflooding experiments, which are time-consuming and costly. The simulator used in this research is CMG-GEM with sensitivity parameter and optimization using CMOST. The Sobol Analysis was conducted to assess the sensitivity parameters and identify the primary mechanism of sophorolipid. Then, optimization is achieved by adjusting the parameters, such as sophorolipid concentration, pore volume (PV) injection, and injection rate. Coreflooding sensitivity results show that the dominant parameter is the nonwetting trapping number (DTRAPN), which is closely related to the mechanism of wettability alteration and mix viscosity. The effectiveness of the Sophorolipid mechanism in modifying wettability, enhancing displacement efficiency, and facilitating emulsion formation, hence improving sweeping efficiency. The recovery factor (RF) increased from the coreflooding simulation optimization results, reaching 19%-33%.

With the rapid rise of electric vehicles, lightweight design of battery boxes has become a critical focus to enhance driving range and reduce carbon emissions. The lower battery box offers greater flexibility for structural and material innovation due to fewer functional constraints. This paper focused on the application of vinyl ester prepreg composites as a substitute for traditional metal structures to achieve weight reduction while maintaining safety. Previous research shows that most studies concentrate on simulation and topology optimization, while few directly analyze material performance. This study compared epoxy, phenolic, and vinyl ester prepregs, demonstrating that vinyl ester prepreg exhibits superior mechanical properties, hygrothermal aging resistance, and impact toughness. Furthermore, assembly using polyurethane adhesives ensures both bonding strength and airtightness. The study concluded that a hybrid structure combining prepreg composites and metal alloys can achieve a weight reduction target of 22.6% while meeting the performance requirements of electric vehicle lower battery boxes.

ABSTRACT With the increasing interconnection and intelligence of power systems, the cyber-physical power communication network (PCN) has become the core infrastructure for real-time monitoring, protection, and control. However, its scale-free topology, heterogeneous traffic distribution, and access-level vulnerabilities expose the system to evolving cyber-physical threats such as false data injection (FDI), stealthy load manipulation, and targeted link attacks. To address these challenges, this paper proposes a systematic vulnerability assessment and mitigation framework tailored to the PCN. We first model the PCN as a weighted, scale-free graph, where the business importance between node pairs depends on both geographical distance and operational criticality. We then define a cumulative business importance loss metric to quantify network vulnerability under random, greedy, and optimal attack strategies. To enhance resilience, we propose a simulated annealing-based routing optimisation method that equalises link load distribution while preserving key operational constraints. Extensive experiments on both synthetic scale-free networks and real-world backbone topologies demonstrate the effectiveness of the proposed metric in distinguishing between attack strategies and highlighting the superior performance of optimised routing in reducing vulnerability.

To address the issues of poor harvesting efficiency, unsatisfactory cutting performance, and high energy consumption in current golden needle mushroom harvesting machinery, this study designed a novel rotary cutter-type root cutting device featuring a slitting cutting angle cutter. The device utilizes a clamping mechanism on a feed turntable to secure the mushrooms, with root cutting achieved through synchronized rotation of the rotary cutter and the turntable. The study commenced with a theoretical analysis of the device’s trajectory displacement model and cutting process, determining the structural form and parameter ranges for key components. Utilizing EDEM discrete element software, simulation optimization tests were conducted using cutting force and unit area power consumption as evaluation metrics. Experiments investigated the effects of rotary cutter geometry and operational parameters, ultimately identifying the optimal cutter parameter combination: a sliding cutting angle of 26°, a cutting edge angle of 15°, and a thickness of 2 mm. The best operational parameters were determined to be a cutting speed of 1400 r/min, a turntable feed speed of 6 r/min, and a cutting height of 20 mm An test platform was constructed to validate the simulation results. The findings demonstrated that the new slitting angle cutter reduced cutting force by 39.4% and unit area power consumption by 24.5%. Additionally, the design significantly improved cutting flatness, ensuring that the device’s performance and efficiency met the design requirements. This study provides an effective solution to key technical challenges in the mechanized harvesting of golden needle mushrooms.

Hybrid Simulation Optimization Method for Biomass Supply Chain Planning: A Systematic Review

Glucose, as the core substance of energy metabolism in living organisms, exhibits significant differences in bioavailability, metabolic pathways, and physiological functions between its d- and l-enantiomers. Specifically, d-glucose serves as a key substrate in the cellular tricarboxylic acid cycle, while the l-enantiomer cannot be metabolized due to the absence of glucokinase, making it a non-caloric, diabetic friendly sweetener. The stark contrast in their bioactivity and application scenarios highlights the critical importance of developing rapid, label-free, and highly sensitive enantiomer discrimination technology for precision medical diagnostics and functional food development. This study innovatively proposes and validates a terahertz metamaterial sensor based on an open-ring resonator for precise identification of glucose enantiomers. Operating under a TE (transverse-electric) mode, the sensor demonstrates dual-resonance responses at 0.409 and 0.652 THz. Through full-wave simulation optimization using CST Microwave Studio®, the device was fabricated via UV photolithography and characterized with a terahertz frequency-domain spectroscopy system. Simulation results predict a high refractive index sensitivity of up to 222.3 GHz/RIU, while experimental measurements demonstrate the sensor’s capability for specific discrimination between d-glucose and l-glucose at concentrations as low as 2 mmol/l. At 8 mmol/l glucose concentration, the transmission peak intensity increase for l-glucose was 1.96 times greater than that of d-glucose. This sensor provides an innovative tool for real-time blood glucose monitoring and personalized diabetes treatment, demonstrating the unique technical advantages of terahertz metamaterials in the field of biological chiral recognition.

To address the challenges in precision seeding of Cyperus esculentus L. seeds caused by their irregular shape and uneven surface, this study investigates the effect of soaking pretreatment on seed germination and adopts rubber-based seed suction holes to improve adsorption performance. Subsequently, calibration and experiments on discrete element simulation parameters were carried out. Initially, by setting four soaking time gradients (0, 24, 48, and 72 h), the optimal soaking duration was determined. Furthermore, through free-fall collision tests, static friction tests, and rolling friction tests, combined with the Plackett–Burman design, steepest ascent experiments, and Box–Behnken response surface methodology, the contact parameters between seeds and between seeds and rubber suction holes were calibrated and optimized. The results showed that the static friction coefficient (D) between seeds, the rolling friction coefficient (E) between seeds, and the rolling friction coefficient (H) between seeds and rubber have significant effects on the stacking angle. The optimal parameter combination obtained was D = 0.592, E = 0.325, H = 0.171. Validation tests on the dynamic stacking angle demonstrated that the relative error between the simulated and physical test values was only 1.89%, confirming the accuracy of the parameters. This study provides reliable parameter references for the design and simulation optimization of precision seed metering devices for C. esculentus after soaking pretreatment.

Hydrogen fuel cell vessels represent a vital direction for green shipping, but the risk of large-scale hydrogen leakage and diffusion in their enclosed compartments is particularly prominent. To enhance safety, a simplified three-dimensional model of the deck-level cabins of the “Water-Go-Round” passenger ship was established using SolidWorks（2023） software. Based on a hydrogen leakage and diffusion model, the effects of leakage location, leakage aperture, and initial ambient temperature on the diffusion patterns and distribution of hydrogen within the cabins were investigated using FLUENT software. The results show that leak location significantly affects diffusion direction, with hydrogen leaking from the compartment ceiling diffusing horizontally much faster than from the floor. When leakage occurs at the compartment ceiling, hydrogen can reach a maximum horizontal diffusion distance of up to 5.04 m within 540 s; the larger the leak aperture, the faster the diffusion, with a 10 mm aperture exhibiting a 40% larger diffusion range than a 6 mm aperture at 720 s. The study provides a theoretical basis for the safety design and risk prevention of hydrogen fuel cell vessels.

The mechanical simplicity and the lack of moving parts of valveless pulsejet engines again fueled interest in using cost-effective forms of propulsion in aerospace propulsion (micro air vehicles (MAVs) and unmanned aerial vehicles (UAVs). This review describes the evolution of numerical simulation and design optimization with particular emphasis on historical performance, experimental work and current computational software such as ANSYS Fluent, and MATLAB. The high thrust to weight ratios of these engines are achieved through optimization of geometry (such as the size of the combustion chamber and exhaust piping) as well as ensuring the inlets facing backward in order to maximize static thrust. Computational Fluid Dynamics (CFD) and Large Eddy Simulation (LES) studies complex flow dynamics, acoustics, and chemical kinetics to increase the combustion efficiency and thrust of the plane, tackling the problem of unnecessary noise, or thermal inefficiencies. Among the innovations advancements that enhance stability and reduce noise include; flame holders, Helmholtz resonators and dual- intake systems; and thermally robust materials which include stainless steel and quartz glass. The thrust-specific fuel consumption of propane is (0.574 kg/N-h) better than that of gasoline meaning that there is the option of optimization of fuel. Both the performance enhancements by way of the golden section approach and acoustic delaying via plenum chambers happen. There are challenges in making them including as heat loss and vibration but the active control systems trend, hybrid propulsion and sustainable fuels have shown to be scalable and have longevity. Valveless pulsejets are powerful but lightweight propulsion systems that have come to form their own mini-industry with new development focused on pushing the limits through rigorous design simulations and material innovations.

Abstract Vibration energy harvesting technology promotes the development of self-powered sensors. In response to the existing issues in piezoelectric energy harvesting, this paper designs a multi-directional multi-modal multi-stable piezoelectric vibration energy harvester (MDMMMS-PVEH). The device consists of a support base and four multi-modal and multi-stable structures. By adopting an inclined beam array design, it achieves multi-directional vibration energy capture. Meanwhile, a composite beam-magnetic coupling multi-stable design is employed to achieve wideband response and high power density energy output. Firstly, theoretical analysis and simulation parameter optimization were conducted on the prototype. The COMSOL software was used to analyze the cantilever beam structure, calculating the natural frequencies and vibration modes of different beams. Then, the simulation optimization of the inclined beam angle affecting multi-directional energy collection was carried out, determining the optimal inclination angle to be 45°. The open-circuit voltage values of the beam structure were analyzed, and the arrangement of permanent magnets was also simulated. Finally, a prototype was developed and an experimental system was set up for testing. The experimental results show that the prototype can collect multi-dimensional vibration energy in the low-frequency range. Compared with the non-magnetic coupling, when excited in the Z-axis direction, the open-circuit voltage of the prototype increased by 36.1% and 61.2% at 12 Hz and 26 Hz excitation frequencies, respectively, and the maximum power density reached 21.50 mW cm−3, providing a solution for self-powered technology in the Internet of Things era.