Process Simulation Optimization Research Articles

Innovate and Validate: Design-Led Simulation Optimization to Test Centralized Registration Feasibility in a Multispecialty Clinic.

This study utilizes a design-led simulation-optimization process (DLSO) to refine a hybrid registration model for a free-standing outpatient clinic. The goal is to assess the viability of employing DLSO for innovation support and highlight key factors influencing resource requirements. Manual registration in healthcare causes delays, impacting patient services and resource allocation. This study addresses these challenges by optimizing a hybrid centralized registration and adopting technology for efficiency. An iterative methodology with simulation optimization was designed to test a proof of concept. Configurations of four and five registration options within a hybrid centralized system were explored under preregistration adoption rates of 30% and 50%. Three self-service kiosks served as a baseline during concept design and test fits. Centralized registration accommodated a daily throughput of 2,000 people with a 30% baseline preregistration rate. Assessing preregistration impact on seating capacity showed significant reductions in demand and floor census. For four check-in stations, a 30%-50% preregistration increase led to a 32% seating demand reduction and a 26% decrease in maximum floor census. With five stations, a 50% preregistration reduced seating demand by 23% and maximum floor census by 20%. Innovating introduces complexity and uncertainties requiring buy-in from diverse stakeholders. DLSO experimentation proves beneficial for validating novel concepts during design.

Multi-objective optimization design of anti-roll torsion bar using improved beluga whale optimization algorithm

PurposeThis study aims to reduce the redundant weight of the anti-roll torsion bar brought by the traditional empirical design and improving its strength and stiffness.Design/methodology/approachBased on the finite element approach coupled with the improved beluga whale optimization (IBWO) algorithm, a collaborative optimization method is suggested to optimize the design of the anti-roll torsion bar structure and weight. The dimensions and material properties of the torsion bar were defined as random variables, and the torsion bar's mass and strength were investigated using finite elements. Then, chaotic mapping and differential evolution (DE) operators are introduced to improve the beluga whale optimization (BWO) algorithm and run case studies.FindingsThe findings demonstrate that the IBWO has superior solution set distribution uniformity, convergence speed, solution correctness and stability than the BWO. The IBWO algorithm is used to optimize the anti-roll torsion bar design. The error between the optimization and finite element simulation results was less than 1%. The weight of the optimized anti-roll torsion bar was lessened by 4%, the maximum stress was reduced by 35% and the stiffness was increased by 1.9%.Originality/valueThe study provides a methodological reference for the simulation optimization process of the lateral anti-roll torsion bar.

Optimized Flexible Lane Assignment Using Simulation and Metaheuristic Algorithms

Technological advances with connected and autonomous vehicles (CAVs) have opened opportunities to increase the efficiency of transportation networks. A novel control framework called combined flexible lane assignment and reservation-based intersection control (CFLARIC) system has been recently proposed for better management of directionally unrestricted CAVs traffic flows in an urban environment. CFLARIC offers a full spectrum of lane assignment possibilities in combination with the appropriate reservation-based intersection control. In CFLARIC, vehicles position themselves in a proper lane before they reach the downstream intersection, which enables resolution of vehicular conflicts both between intersections and within the intersection boxes. Although CFLARIC has shown promising results, only a limited number of lane assignment scenarios have been designed and tested using some preset rules and based on the previous research objectives. Therefore, the optimality of their performance has never been studied. The objective of this study is to address the flexible traffic lane assignment in such a system as a network optimization problem, in which an optimal lane assignment schema is achieved using metaheuristic optimization algorithms. To this end, a combination of NetLogo and the BehaviorSearch tool is utilized for the simulation modeling and optimization process using random search and brute-force as the search algorithms. The output of the optimization process is the lane assignment that leads to a minimum total travel time for a given network geometry and traffic volumes. Results indicate that a flexible control concept such as optimized CFLARIC has great potential to improve the efficiency of traffic control strategies with CAVs.

Study on Co-Pyrolysis of Coal and Biomass and Process Simulation Optimization

In this paper, the optimal process conditions for coal–biomass co-pyrolysis were obtained through pyrolysis experiments. The results show that under the condition of the pyrolysis temperature of 500 °C, the pyrolysis oil yield and positive synergistic effect reach the maximum, and the ratio of coal to biomass raw materials is 1:3. The effects of three loading methods (coal loading on biomass, biomass loading on coal, and coal–biomass mixing) on the distribution of simulated products of coal–biomass co-pyrolysis were constructed using Aspen Plus V11 software. The experimental results of pyrolysis carbon, pyrolysis oil, pyrolysis gas, and water under three different ratios are close to the simulation results, and the maximum error is 8%. This indicates that the model is dependent. This paper analyzes the economic situation in terms of investment in factory construction, raw material collection, product production, and product sales. The results show that when the processing scale is 9 tons h−1, the pyrolysis plant can be profitable in the first year. This study provides basic data and the basis for the commercialization investment of coal–biomass co-pyrolysis technology.

Garment production line optimization using production information based on real‐time power monitoring data

AbstractThe implementation of Fourth Industrial Revolution technologies at a manufacturing site requires analysis of the site status based on real‐time information, optimization of the processes, and onsite execution. However, in labor‐intensive industries, such as the garment‐manufacturing industry, it is extremely difficult to develop smart factories based on real‐time onsite information because such industries are accustomed to managing labor using empirical expert judgment; moreover, they require rapid production circulation and are dependent on human‐related factors. In this study, we developed an optimization simulator using onsite real‐time production information that provides decision‐making support to maximize the productivity of a garment production plant. As an optimization method, a genetic algorithm was used to incorporate operator relocation into various garment production line variables. Through the developed simulator, field managers can predict and optimize productivity with simple operation in connection with production information. The application of the simulation optimization process to an actual production line in an Indonesian garment factory indicated that the simulator can improve productivity by 34.8%. The results of this study will provide guidance regarding the application of industrial information integration in labor‐intensive industries using methods that can systematically support decision‐making to achieve optimal productivity.

Ultra-wide band antipodal Vivaldi antenna design using target detection algorithm for detection application

This work presents a technique for detecting targets between two walls using ultra-wide band (UWB) modified antipodal Vivaldi antenna (MAVA). The detection system works on principle of time domain reflectrometry (TDR) using through wall imaging (TWI). This technique utilized a vector network analyzer (VNA) to produced short and small pluses to irradiate through an antenna array system onto the wall under study. The purposed detection system operated in UWB frequency spectrum (3.1 GHz to 10.6 GHz). Furthermore, an algorithm for hidden target detection has been developed. The results of the simulation of the designed antenna revealed a significant level of penetration, demonstrating a smart advancement in detecting and imaging system, to locate hidden metallic targets with good accuracy. A signal processing technique have been employed to improve the resolution of the target image. Using computer simulation technology (CST) software, the development and optimization process of an antenna is carried out, and the parametric performance of return loss, directivity and radiation pattern is evaluated.

Swarm intelligence machine-learning-assisted progressive global optimization of DNAPL-contaminated aquifer remediation strategy

Abstract Remediation projects of DNAPL-contaminated groundwater generally face difficulties of low contaminant removal rate and high remediation cost. Hence, a machine-learning-assisted mixed-integer multi-objective optimization technique was presented for efficiently programming remediation strategies. A swarm intelligence multi-kernel extreme learning machine (SI-MKELM) was proposed to build a reliable intelligent surrogate model of the multiphase flow numerical simulation model for reducing the computational cost of repetitive CPU-demanding remediation efficiency evaluations, and a hyper-heuristic homotopy algorithm was developed for progressively searching the global optimum of the remediation strategy. The results showed that: (1) The multi-kernel extreme learning machine improved by swarm intelligence algorithm significantly improved the approximation accuracy to the numerical model, and the mean residual and mean relative error were only 0.7596% and 1.0185%, respectively. (2) It only took 0.1 s to run the SI-MKELM. Replacing the numerical model with SI-MKELM considerably reduced the computational burden of the simulation–optimization process and maintained high computational accuracy for optimizing the DNAPL-contaminated aquifer remediation strategy. (3) The hyper-heuristic homotopy algorithm was capable of progressively searching the global optimum, and avoiding premature convergence in the optimization process. It effectively improved the searching ability of the traditional heuristic algorithms.

Determination of the cost-benefit efficient interval for sponge city construction by a multi-objective optimization model

The cost-benefit is a key factor when selecting an appropriate sponge city construction scheme. The research of applying intelligent technology to find cost-benefit efficient planning and construction of sponge city is urgently required. This paper established a multi-objective simulation optimization framework of sponge city construction which considered minimization of runoff control rate, pollutant control rate and life-cycle cost Non-dominated sorting genetic algorithm (NSGA-II) was successfully coupled to Storm water management model to complete the simulation-optimization process. A case study in Xining, China, was conducted to demonstrate the proposed framework. The results of this research suggested that 1) different sponge city construction schemes lead to different runoff control rates and pollutant control rates although under the same investment; 2) the runoff control rate and pollutant control rate total suspended solids decreased with the increase of the rainfall return period, while the cost of sponge city construction increased with the increase of rainfall return period. Furthermore, for T = 2-year, the sponge facility exhibited the most stable control effect on runoff and pollutants among the three different return periods (T = 2-year, 5-year, 10-year); 3) sponge city construction exhibited a “cost-benefit” efficient interval. For T = 2-year, the cost-benefit high efficiency interval of sponge city construction is calculated between 1.2 billion and 1.8 billion; for T = 5-year, the interval is between 1.2 billion and 1.8 billion, while for T = 10-year, the interval is between 1.3 billion and 2.1 billion. The above observations provide reference for reasonable and effective sponge city construction in Xining, China.

A simulation–optimization framework for enhancing robustness in bulk berth scheduling

The service time of the vessels is one of the main indicators of ports’ competitiveness. This, together with the increasing volume of bulk transportation, make the efficient management of scarce resources such as berths a crucial option for enhancing the productivity of the overall terminal. In real scenarios, the information available to port operators may vary once the planning has been elaborated. Unforeseen events, errors, or modifications in the available information can lead to inefficient terminal management and the initial scheduling might become unfeasible. This implies that the use of deterministic approaches may not be enough to maximize productivity. Therefore, in this work, proactive simulation–optimization approaches that utilize the information collected during the simulation for guiding the optimization search to provide robust solutions are proposed. Moreover, a multi-objective approach based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for jointly tackling the problem objective as well as the deviations because of stochastic changes is developed. Finally, we also investigate the contribution of time management strategies such as buffers to absorb stochastic modifications and hence increase solutions’ robustness. The computational results indicate, on the one hand, the benefit of integrating both types of objectives (i.e., deterministic and stochastic) to guide the simulation–optimization process, and on the other hand, the benefit of using the multi-objective approaches like NSGA-II. Finally, the incorporation of buffers leads to better performance in terms of reducing penalty costs due to disruptions, shortening the planning risks related to only considering deterministic planning.

Thermal and noise insulation performance interaction of building envelope during building simulation optimization in tropical climates

Building simulation optimization is being increasingly adopted by researchers and designers as an efficient methodology for early-stage design and selection of building envelope parameters. Thermal performance has been the focus of many of the proposed methodologies owing to large energy consumption in buildings to maintain thermal comfort for the occupants. While the envelope is optimized to enhance its thermal performance, the effect on its noise insulation performance is not paid any attention as both performance objectives are assumed to be non-interacting or non-conflicting. It may be possible that the optimization for thermal performance may lead to degradation in noise insulation performance or vice versa. The present work aims to study the interaction between the thermal and noise insulation performance of the building envelope during a building simulation optimization process when both the performance objectives are optimized independently and simultaneously. The results obtained show the adverse effect of independent thermal and noise insulation optimization on noise insulation and thermal performance of the building envelope walls respectively. An exception was observed in the case of independent noise insulation optimization of both 8-hour and 24-hour conditioned buildings where average thermal performance of the final population was enhanced along with the noise insulation performance.

Research on Biomechanical Simulation and Simulation of Badminton Splitting and Hanging Action Based on Edge Computing

Sports biomechanics refers to the science of the laws of mechanical motion produced in the process of biological movement. Its essence is to systematically and digitally reconstruct the fundamental attributes and characteristics of motion. At present, the research of sports biomechanics mainly focuses on the theoretical research of basic aspects and lacks the new technology of sports biomechanics digital simulation innovation and data measurement. This article takes the badminton chopping action as the research object and carries out biomechanical simulation and simulation research with the help of edge computing and genetic algorithm. First of all, this paper constructs a badminton chopping and hanging action system framework based on edge computing, so as to facilitate simulation and improve data transmission efficiency. Secondly, genetic algorithm is used in biomechanics simulation and simulation optimization and data analysis process. System testing and simulation verify the excellent performance of the biomechanical simulation of badminton chopping and hanging action established in this paper. The research will provide a reference for the academic circles to explore the field of sports biomechanics.

Over-Size Pill-Box Window for Sub-Terahertz Vacuum Electronic Devices

The pill-box window is one of the important components of microwave vacuum electronic devices (VEDs), and research into it is of great significance. As the operating frequency increases, the problems associated with the reduction in the structure size include the reduction of the brazing plane and the reduction in the tolerance of the pill-box window. These problems will cause traditional pill-box windows to be unsuitable in high-frequency bands, especially in terahertz and sub-terahertz regions. The most influential factor is the length of the circular waveguide in the box window. The welding plane of the over-size pill-box window is the annular bottom surface on both sides of the dielectric sheet, which is larger than the circular waveguide, and the operating frequency does not directly affect the area of the brazing surface. Choosing a suitable diameter for the dielectric sheet can effectively increase the tolerance to the length of the pill-box window circular waveguide. Therefore, an over-size pill-box window would be a practicable approach to improve the performance compared to the traditional pillow-box in high-frequency bands. This paper describes, in detail, the theoretical design, simulation optimization and experimental process of this improved pill-box window. An over-size pill-box window suitable for G band VEDs was successfully developed. The experimental result in the 215–225 GHz band is that the maximum transmission loss is −1 dB, and the overall transmission loss is close to −0.5 dB. The overall reflection is less than −11 dB.

Collaborative design between architecture and structure of large complex ice shell based on air-ribbed inflatable mold: A case of ice restaurant

In 2019, the ice restaurant at the 21st Harbin Ice and Snow World is one of the representative works of contemporary Chinese ice shell architecture. Through the summary of practical experience of the ice restaurant. The design thinking and practice method of coordination between architecture and structure of large complex ice shell are elaborated in detail. The logic and advantages of air-ribbed inflatable mold construction method, numerical simulation optimization process based on Grasshopper platform and structural performance calculation of ABAQUS platform are included. The feasibility of collaborative design method in the design of ice shell building is proved. It promotes the development of ice shell construction. On the basis, the future development direction is prospected.

LED Wristbands for Cell-Based Crowd Evacuation: An Adaptive Exit-Choice Guidance System Architecture.

Cell-based crowd evacuation systems provide adaptive or static exit-choice indications that favor a coordinated group dynamic, improving evacuation time and safety. While a great effort has been made to modeling its control logic by assuming an ideal communication and positioning infrastructure, the architectural dimension and the influence of pedestrian positioning uncertainty have been largely overlooked. In our previous research, a cell-based crowd evacuation system (CellEVAC) was proposed that dynamically allocates exit gates to pedestrians in a cell-based pedestrian positioning infrastructure. This system provides optimal exit-choice indications through color-based indications and a control logic module built upon an optimized discrete-choice model. Here, we investigate how location-aware technologies and wearable devices can be used for a realistic deployment of CellEVAC. We consider a simulated real evacuation scenario (Madrid Arena) and propose a system architecture for CellEVAC that includes: a controller node, a radio-controlled light-emitting diode (LED) wristband subsystem, and a cell-node network equipped with active Radio Frequency Identification (RFID) devices. These subsystems coordinate to provide control, display, and positioning capabilities. We quantitatively study the sensitivity of evacuation time and safety to uncertainty in the positioning system. Results showed that CellEVAC was operational within a limited range of positioning uncertainty. Further analyses revealed that reprogramming the control logic module through a simulation optimization process, simulating the positioning system’s expected uncertainty level, improved the CellEVAC performance in scenarios with poor positioning systems.

Waste load allocation under uncertainty using game theory approach and simulation-optimization process

AbstractThis article aims to present a new methodology for waste load allocation (WLA) in a riverine system considering the uncertainty and achieve the lowest amount of inequity index, cost, and fuzzy risk of standard violation. To find a surface of undominated solutions, a new modified PAWN method, initially designed for sensitivity analysis, was developed and coupled with a simulation-optimization process using multi-objective particle swarm optimization (MOPSO) algorithm, to consider the uncertainty of all affecting variables and parameters by using their probability distribution. The proposed methodology applied to Sefidrood River in the northern part of Iran. Graph model for conflict resolution (GMCR) as a subset of game theory was implemented to attain a compromise on WLA among the stakeholders of a river system's quality in Iran: Department of Environment, Municipal Waste Water, and Private Sector. Some undominated solutions were used in GMCR model and modeling the conflict among decision makers reveals that their preferences and the status quo do not lead to a solely stable equilibrium; thus the intervention of a ruler as arbitrator leads them to reach a compromise on a scenario that has a median FRVS and cost. Sensitivity analysis was done using the PAWN method to assess the sensitivity of three intended objectives to all variables and parameters.

Multi-objective Freshwater Management in Coastal Aquifers Under Uncertainty in Hydraulic Parameters

This paper proposes a novel stochastic framework for groundwater quantity and quality management in aquifers threatened by saltwater intrusion. In this methodology, a finite difference SEAWAT code is linked with an optimization model to solve density-dependent groundwater flow equations considering different patterns of pumping rates. To reduce the computational time of the simulation–optimization process especially when there are a high number of decision variables, a modular evolutionary polynomial regression (MEPR) model is developed and coupled with the optimization algorithm. The info-gap theory is utilized to evaluate the robustness of optimal scenarios incorporating the uncertainty of hydraulic conductivity (k) of the heterogeneous aquifer. For each management scenario proposed by the simulation–optimization model, values of robustness and opportuneness indices are computed based on utility functions of different agricultural sectors. The results of applying the proposed method to the Qom aquifer in Iran show that coupling MEPR model with the simulation–optimization model considering the uncertainty of the aquifer parameter k could provide a reliable management scenario with a comparatively low computational cost.

Optimization of dynamic poly-generation system and evaluation of system performance in building application

A decentralized small-scale poly-generation system has attracted lots of attention nowadays. In this study, the poly-generation system is optimized and evaluated for the building application. To reduce the high computational cost of dynamic simulation and multi-objective global optimization process, a hybrid simulation model has been newly proposed. The new hybrid model is developed with the validation and verification of each system component. Based on the developed model, the meta-model is optimized for improving the multi-criteria indices (energy, environment, and economy). For the optimization, the critical design variables are selected as the rated electrical power of the prime mover, the volume of thermal storage system, the cooling/heating capacity ratio supplied by electrically driven systems, and maximum heat power from the storage system. As a result of optimization and decision making, the most effective point from Pareto frontier is determined. In addition, sensitivity analysis and feasibility evaluation are conducted to investigate the effectiveness of the poly-generation system when integrated with the building in South Korea. The results show that primary energy consumption is reduced by 22.9%, CO2 emissions by 14.2%, and annualized total cost by 6.0%, respectively. Through the specific economic investigation, the payback year is calculated as 6.2. Finally, the performance that greatly affects multi-criteria indices is further discussed in terms of utilization rate, load coverage, and self-sufficiency ratio.

Design of a TTC Antenna Using Simulation and Multiobjective Evolutionary Algorithms

The design of a Compact Dual-band Equatorial helix antenna using Computational Electromagnetic Methods together with multiobjective optimization algorithms is presented. These antennas are used for Telemetry, Tracking, and Control of satellites from the terrain base station. In order to optimize the parameters an antenna, a simulation–optimization process is shown along a real case study. The parameters of the antenna that fulfills the radiation patterns needed for the communication are obtained using a simulation tool called MONURBS together with two well-known multiobjective algorithms: NSGA-II and SPEA-2. In this paper, a comparison with previous designs and the antenna prototype is presented, showing that this approach can obtain multiple valid solutions and accelerate the design process.

Simulation Optimization for Complex Multi-Domain Physical Systems Based on Partial Resolving

The iterative process of simulation optimization is a time-consuming task, as it involves executing the main simulation program in order to evaluate the optimal constraints and objective functions repeatedly according to the values of tuner parameters. Parameter optimization for a model of a multi-domain physical system based on Modelica is a typical simulation optimization problem. Traditionally, each simulation during each iterative step needs resolve all the variables in all the mass differential-algebraic equations (DAE) generated from the simulation model through constructing and traversing the solving dependency graph of the model. In order to improve the efficiency of the simulation optimization process, a new method named partial simulation resolving algorithm based on the set of input parameters and output variables for complex simulation model was proposed. By using this algorithm, a minimum solving graph (MSG) of the simulation model was built according to the set of parameters, constraints, and objective functions of the optimization model. The simulation during the optimization iterative process needs only to resolve the variables on the MSG, and therefore this method could decrease the simulation time greatly during every iterative step of the optimization process. As an example, the parameter optimization on economy of fuel for a heavy truck was realized to demonstrate the efficiency of this solving strategy. This method has been implemented in MWorks—a Modelica-based simulation platform.

Application of Mixed-Integer Nonlinear Optimization Programming Based on Ensemble Surrogate Model for Dense Nonaqueous Phase Liquid Source Identification in Groundwater

Groundwater contamination source identification (GCSI) is critical for taking effective measures to protect groundwater resources, assess risks, mitigate disasters, and design remediation strategies. Simulation–optimization techniques have been effective tools for GCSI. However, previous studies have applied individual surrogate models when replacing simulation models, rather than making efforts to combine various methods to improve the approximation accuracy of the surrogate model over the simulation model. In this study, the kernel extreme learning machine (KELM) model was proposed to enhance the surrogate model, and to approach GCSI problems, especially those of dense nonaqueous phase liquid-contaminated aquifers, more effectively. In addition, a kriging model and a support vector regression (SVR) model were built and compared with the KELM model, and various ensemble surrogate (ES) modeling techniques were applied to establish four ES models. Results showed that the KELM model was more accurate than the kriging and SVR models; however, the ES models performed much better than the three individual surrogate models. The most precise ES model increased the certainty coefficient (R2) to 0.9837, whereas limiting the maximum relative error to 13.14%. Finally, a mixed-integer nonlinear programming optimization model was established to identify the groundwater contamination source in terms of location and release history, and simultaneously assess aquifer parameters. The ES model developed in this article could reasonably predict the system response under given operation conditions. Replacement of the simulation model by the ES model considerably reduced the computation burden of the simulation–optimization process and simultaneously achieved high computation accuracy.