Simulation-based Optimization Method Research Articles

Design of Active Suspension Controller for Ride Comfort Enhancement and Motion Sickness Mitigation

This paper presents a method for designing an active suspension controller for ride comfort enhancement and motion sickness mitigation. For this, it is necessary to design an active suspension controller, which aims to reduce the vertical acceleration and pitch rate of a sprung mass in a vehicle. A half-car vehicle model was selected. For the controller design, a static output feedback (SOF) control was selected instead of a full-state feedback control because it is hard to measure all state variables in real vehicles. With the available signals, three types of SOF controller were proposed. To determine the gains of the SOF controllers, a linear quadratic optimal control methodology and a simulation-based optimization method were adopted. To validate the proposed method, a simulation was carried out using vehicle simulation software. The simulation results show that the proposed method is quite effective for ride comfort enhancement and motion sickness mitigation.

Dynamic joint decision of matching parameters and relocation strategies in ride-sourcing systems interacting with traffic congestion

As the ride-sourcing market expands, ride-sourcing fleets have increased urban traffic congestion, and in turn, road congestion is affecting ride-sourcing operations. It is crucial to incorporate this interaction mechanism into the operational decision of ride-sourcing systems. This mechanism can be described in detail by simulation models. Applying simulation-based optimization methods to the real-time operation of ride-sourcing systems is challenging due to the limitations of simulation efficiency. To tackle this issue, this study first proposes a modular simulation model combining time-driven and event-driven mechanisms. The simulation model comprises two layers: the traffic flow module at the bottom, defined by trip-based and multi-region macroscopic fundamental diagrams and cell transmission models, and the ride-sourcing operation module at the top, which considers the interaction between the traffic congestion and ride-sourcing operation and models the random behavior of passengers and drivers. We verify the efficiency and accuracy of the simulation model. The simulation model is utilized as an evaluator of the objective functions and performance measures. Then, we integrate the Bayesian optimization, parallel sampling, and rolling horizon approaches to develop an efficient and effective simulation-based optimization framework for the dynamic joint decision-making of matching radius, matching intervals, and threshold relocation strategies. Six groups of experiments for the traffic scenario in Berlin reveal some interesting findings. (1) The matching rate first increases and then decreases with the matching radius (a concave function), while the closest study does not think so. (2) As the fleet size grows, passenger waiting time reaches its minimum and then rises because of the increased endogenous congestion generated by ride-sourcing fleets. (3) The optimal operation strategy considering endogenous congestion not only leads to an increase in the system revenue and efficiency but also leads to congestion relief, even though the latter was not our initial objective. This benefit is the most pronounced when the fleet size and background traffic are moderate.

The Proactive Base Stock Policy for Condition Based Maintenance

This paper introduces the proactive base stock policy (ProBSP) that incorporates degradation data into spare parts decision-making processes. The proposed policy aims to anticipate the demand for spare parts by proactively ordering them, instead of ordering each time one is needed. ProBSP involves two decision variables: the initial stock level and the degradation order threshold. To determine the optimal values for these variables, a simulation-based optimization method is developed. More precisely, a discrete event simulation is constructed to evaluate a single point in the parameter space, and an intelligent algorithm is employed to search for the optimal solution by efficiently exploring the solution space, thereby exploiting structural properties of ProBSP. Finally, numerical experiments are conducted to gain insights into the performance of ProBSP. These experiments reveal the effectiveness of ProBSP: the average spare parts stock level is reduced by 68%, with reductions exceeding 90% in specific scenarios.

Simulation and optimization of prefabricated building construction considering multiple objectives and uncertain factors

The construction of prefabricated buildings involves complex processes, which have high uncertainties and objective conflicts, hindering the improvement of construction performance. This study aims to simultaneously optimize the activity logic and multiple construction performance objectives (concerning time, cost, quality, and CO2 emissions) of prefabricated building construction under the influence of uncertain factors. The eliminate, combine, rearrange, and simplify (ECRS) technique is combined with intelligent simulation technology to develop a simulation-based optimization method for prefabricated building construction considering multiple objectives and various uncertain factors. This method is characterized by enabling activity-logic improvements, revealing mathematical relationships between uncertain factors, activity parameters and construction performance, and achieving the trade-off optimization of construction performance objectives in various uncertain environments. A typical prefabricated building project is used for demonstrating and validating the method. The results show that the proposed method reduces the construction time by 5.73% through the ECRS technique, and further achieves a 10.53% reduction in construction cost, a 10.10% improvement in construction quality, and 60.32% CO2 emission mitigation through multiobjective optimization when only resource allocation is considered. Considering the interaction between resource allocation conditions and other three uncertain factors, the construction time is mainly affected by the supply of materials and weather conditions, while the familiarity level of work affects the construction time, cost, and CO2 emissions. The proposed method and findings of this study can facilitate robust decision-making by project managers when addressing the trade-offs between cost reduction, schedule shortening, quality improvement, and CO2 reduction under various uncertain scenarios.

A stochastic simulation-based chance-constrained programming model for optimizing watershed best management practices for nonpoint source pollution control under uncertainty

In this study, a stochastic simulation-based chance-constrained programming (SSCP) model is developed by integrating soil and water assessment tool (SWAT), generalized likelihood uncertainty estimation (GLUE), chance-constrained programming (CCP) and genetic algorithm (GA) for managing non-point source (NPS) pollution under uncertainty. The SSCP model improves upon conventional simulation-based optimization methods by effectively (i) reflecting the propagation of stochastic uncertainties from the SWAT to the optimization framework by the joint GLUE and CCP (GLUE-CCP) approach, and (ii) identifying the optimal implementation levels of best management practices (BMPs) under different constraint-violation risk levels. The SSCP is solved using a GA with parallel sampling technique after conversion into the corresponding deterministic version. The model is applied to determine the optimal types, sizes and locations of BMPs for NPS pollution control in the Hanjiang River basin, China. The results indicate that, under low-risk conditions, grassed waterways and filter strips should be implemented in the northern part of the basin and larger detention ponds of about 637.8 ∼ 850.4 ha should be installed in the northern and eastern parts of the basin; whereas under high-risk conditions, filter strips should be installed in the northeastern part of the basin and larger detention ponds of about 562.1 ∼ 749.5 ha should be installed in the northern and southwestern parts of the basin. Moreover, as the risk level decreases, stricter control of constraint violations leads to higher BMP implementation costs in order to mitigate the risk of excessive NPS pollution loads. Thus, the proposed model provides valuable insights into the selection of BMP placement schemes at various risk levels, facilitating the reduction of NPS pollution at the outlet of the Hanjiang River basin to acceptable levels while considering the tradeoff between system cost and risk.

Bi-objective simulation optimization for online feedback control of variable speed limits considering uncertain traffic demands and compliance behaviors

Abstract Variable Speed Limits (VSL) stand out as a well-established and effective strategy to alleviate traffic congestion and enhance traffic safety on motorways. It allows Variable Message Signs (VMSs) to dynamically determine the speed limits according to real-time traffic states. This paper introduces an innovative online feedback control approach designed to regulate speed limit values on Variable Message Signs (VMSs), addressing multiple bottlenecks while considering their spatiotemporal constraints. Moreover, we offline optimize the gain coefficients of this feedback control approach in the simulation-based optimization (SBO) framework. Specifically, with average and variance of space-mean speeds as bi-objectives, a stochastic SBO model considering uncertain traffic demands and compliance behaviors is established and solved by a bi-objective surrogate-based promising area search (BOSPAS) algorithm. Real-field experiments conducted in Edmond City demonstrate the well-performing bi-objectives of the proposed approach, especially in handling uncertain compliance behaviors and traffic demands. Compared with the uncontrolled scenario, the feedback control schemes with the offline optimized gain coefficients improve the average and variance of space-mean speeds by up to 16.2% and 20.8%, respectively. Meanwhile, by the comparison of detailed performances, it is found that the optimized control schemes perform better than the uncontrolled scheme from the overall and local aspects. In conclusion, this study puts forward a general framework that applies an online feedback control approach with gain coefficients optimized offline by an SBO method to deal with real-time decision-making problems under uncertainties.

Designing Porous Structure with Optimized Topology using Machine Learning

Biomedical engineering relies on topology optimization to refine material distribution, crucial for lightweight, high-performance prostheses and orthoses. Advanced manufacturing techniques like additive manufacturing can then be used to create these intricate designs layer by layer, ensuring precision and customization. However, conventional numerical simulation-based topology optimization methods can be time-consuming and resource-intensive, especially as the design domain expands. To overcome this issue, machine learning models are investigated for their ability to perform topology optimization. The results indicate a significant decrease in computation time, along with comparable optimization performance to conventional methods.

A decomposition-based optimization method for integrated vehicle charging and operation scheduling in automated container terminals under fast charging technology

The increasing utilization of battery-powered automated guided vehicles in automated container terminals, has an important consequence on terminal cost and efficiency. How to tackle integrated vehicle charging and operation scheduling problem to maintain high terminal performance is prominent for sustainable port operation. In this paper, fast charging technology is investigated, and a mixed integer programming model for this complicated scheduling problem is constructed, which aims to reduce charging cost and penalty cost related to makespan, and includes sequence-related constraints, time-related constraints and energy-related constraints. A decomposition-iteration algorithm is proposed to solve this problem, and furthermore it is combined with a simulation-based optimization method to address practical-sized instances. Numerical experiments on real-world cases are conducted to verify the efficiency and effectiveness of the proposed solution algorithm. Insightful managerial implications are derived by comparative analysis on charging rules and charging facility locations, and sensitivity analysis on charging power, charging facility configuration and vehicle configuration. Experimental results provide valuable references for terminal managers to make configuration and scheduling decisions for battery-powered vehicle transporting systems.

Simulation and optimization of transfer system for ore terminal with complex waterways

Terminal towing and barging operations play a critical role in cargo transfer within ports, especially in complex waterways. Scheduling of barges and tugs is influenced by various factors, such as speed, and the operation planning must consider the constraints of channel traffic. Additionally, the transfer system involves constructing and operating berths, transshipment equipment, barges, tugboats, and other resources. Effective scheduling and resource planning are crucial for achieving sustainability. To illustrate this point, we analyze the estuary barging process in a real-world case. We utilize the Anylogic platform to simulate the entire process of ore terminal loading, river transportation, and sea transshipment. Furthermore, we employ simulation-based optimization methods to enhance the system’s performance. Our case study has yielded valuable management insights, including: (1) Key variables related to shipping speeds and resource allocation are identified and optimized; (2) A specific tug dispatch rule has been verified to outperform other options in terms of efficiency and emissions; (3) The potential benefits of constructing and enabling an additional terminal have been identified. Our exploration provides quantitative support for decision-making related to vessel scheduling and resource planning in engineering projects for ore terminals.

CFD simulation and optimization of ventilation for the layout of community architecture inspired by fishbone form

The rise in urbanization has led to an increase in high-density communities. Our study drew inspiration from the fishbone structure and applied a bionic architectural layout to optimize the wind environment within the pedestrian areas of these communities. Additionally, our study delved into the intrinsic principles and simulation-based optimization methods for the wind environment in the pedestrian areas of the fishbone bionic architectural complex. Using Computational Fluid Dynamics (CFD) simulation technology, our study simulated the building models both before and after the community renovation. Furthermore, our study simulated nine architectural complex models proposed by our study, considering the architectural layout and facade height within the community. By verifying and comparing the simulation results, our study observed that the fishbone bionic architectural form significantly improved the wind environment within the community’s pedestrian areas. Regarding the facade height, our study discovered that taller architectural units should be positioned away from the prevailing wind direction to enhance the airflow within the architectural complex. This research carries substantial implications for the renovation and planning of residential communities, particularly in large urban settings with a substantial population.

A data-driven multi-objective optimization framework for determining the suitability of hydrogen fuel cell vehicles in freight transport

In order to evaluate suitability of battery electric vehicles (BEVs) and hydrogen fuel cell vehicles (HFCVs) in freight transport systems, this paper proposes a data-driven and simulation-based multi-objective optimization method to deploy charging/refueling facilities for BEVs/HFCVs. The model considers three objectives, namely minimizing total system cost, maximizing service reliability, and minimizing greenhouse gas (GHG) emissions. In particular, a data-driven micro-simulation approach is developed to simulate the operation of freight transport systems with different vehicle and facility types based on the analysis of a one-week Global Positioning System (GPS) trajectory dataset containing 63,000 freight vehicles in Beijing. With the model, we compare the suitability of BEVs and HFCVs within three typical scenarios, i.e., BEVs coupled with Charging Stations (BEV-CS), BEVs coupled with Battery Swap Stations (BEV-BSS), and HFCVs coupled with Hydrogen Refueling Stations (HFCV-HRS). The results suggest that BEV-CS has the lowest total system cost: its system cost is 62.5% and 90.3% of the costs in BEV-BSS and HFCV-HRS, respectively. BEV-BSS has the lowest delay time: its delay time is 62.1% and 86.0% of the delay times in BEV-CS and HFCV-HRS, respectively. HFCV-HRS has the lowest GHG emissions: its emissions are 37.3% and 46.9% of the emissions in BEV-CS and BEV-BSS, respectively. The results are expected to be helpful for policy making and infrastructure planning in promoting the development of alternative fuel vehicles.

An inventory data-driven model for predictive-reactive production scheduling

Scheduling is a complex task due to the need to optimise multiple competing objectives and react to unpredictable events that may occur during production execution. The strategy of predictive-reactive scheduling can be used to reconcile the conflict between the original schedule and the current shop floor situation. This study seeks to present an inventory data-driven predictive-reactive production scheduling model that supports the evolving concepts of the Industry 4.0. Periodically, a machine learning technique provides predictive scheduling considering a best-case scenario according to an established Key Performance Indicator (KPI). Then, material non-availability causes disruptions in production, which triggers the Simulation-Based Optimization (SBO) method to handle these events. Thus, SBO provides a reactive schedule with the best set of priority rules to sequence jobs on each machine according to the data on the shop floor. This model was validated with a real case study using data collected from a metal-mechanical company. Considering the service level KPI, the results showed that the model is able to find a better solution in the compared scenarios. Therefore, even in a dynamic and stochastic scenario, with machine breakdowns, quality problems, raw material delays, and accuracy issues, the model proved efficient in mitigating these variations’ effects.

Pre-Swirl Ducts, Pre-Swirl Fins and Wake-Equalizing Ducts for the DTC Hull: Design and Scale Effects

A pre-swirl fin (PSF), pre-swirl duct (PSD) and wake-equalizing duct (WED) energy-saving devices (ESD) are designed for the Duisburg Test Case (DTC). To this aim, a simulation-based design optimization method, combining RANSE analyses (ship resistance) with BEM calculations (unsteady propeller performances) in a simplified optimization process realized through a parametric description of ESD geometries, was employed. Fully resolved RANSE analyses were used to validate the outcomes of this affordable design process, which identifies devices capable of saving energy in the delivered power for this type of challenging test case by up to 2.6%. Comparisons with model-scale calculations, furthermore, permit us to discuss the influence of each appendage in different flowfields (model- and full-scale, as well as under the action of the simplified or the resolved propeller) and the reliability of the full-scale extrapolation methods recently proposed for these types of devices.

Simulation-based optimization considering energy consumption for assisted station locations to enhance flex-route transit

As an emerging mode of public transit, flex-route transit (FRT) is regarded as the key to reducing energy consumption and promoting sustainable development in low-density suburban cities. However, its performance in cases with unexpectedly high demand levels is usually unsatisfactory. To address this issue, a simulation-based optimization method is proposed for assisted station locations in FRT and to reduce the system costs, including that for energy consumption. First, considering the uncertain travel demands of passengers, this location problem is modelled as a simulation-based optimization (SO) problem based on Monte Carlo simulation (MCS). Then, a multiple metamodel-based efficient global optimization (MMEGO) algorithm is proposed to effectively solve the SO problem. The numerical results demonstrate that the proposed MMEGO algorithm can obtain good results with lower computing cost and stronger robustness than counterpart algorithms (EGO and MISK). Finally, simulation tests of FRT based on data from the operation of 320 buses in Shenzhen city and the related OD data are conducted to test the feasibility of the proposed method. The results show that the proposed optimization method can be used to identify suitable locations for assisted stations and reduce the total system cost by 26.6% compared with that for an FRT system with unoptimized assisted station locations.

Agent-based simulation and optimization of hybrid flow shop considering multi-skilled workers and fatigue factors

In the past few decades, more and more studies have begun to consider the impact of human factors on manufacturing systems. This paper studies a hybrid flow shop scheduling problem considering multi-skilled workers and fatigue factors. An agent-based simulation system is established to cope with the uncertainties in the worker fatigue model. Furthermore, this paper proposes a novel simulation-based optimization (SBO) framework, which combines genetic algorithm (GA) and reinforcement learning (RL) to address the hybrid flow shop scheduling problem. Numerical experiments are conducted on several instances with different production configurations. In particular, a pharmaceutical production facility is modeled as a hybrid flow shop to demonstrate the feasibility and effectiveness of the proposed SBO method.

Dual-objective building retrofit optimization under competing priorities using Artificial Neural Network

Building retrofit has received renewed interests in recent years, driven by energy-savings and indoor environmental quality goals. Digital technologies such as building performance simulation and optimization algorithms have been used to identify optimal retrofit schemes, yet the existing approaches are limited by the slow running speed of physics-based models and sub-optimal results. This study describes a novel framework, the Building Performance Optimization using Artificial Neural Network (BPO-ANN), which can automatically identify optimal building retrofit schemes. A robust Artificial Neural Network model was developed and validated as a surrogate to rapidly assess building performances, which was then connected to a genetic algorithm in search of Pareto optimal. The impact of key design attributes on building performances have been assessed using sensitivity analysis. The BPO-ANN framework has been tested in a high-performing campus building in Northern China under two competing objectives: building energy demand and occupant thermal comfort. It can automatically identify optimal design schemes, which were expected to achieve an energy-savings of 4% and reduce the annual thermal discomfort percentage by 4%. Sensitivity analysis suggested that window-to-wall ratio and HVAC setpoint have contributed the most to the performances of the campus building, followed by the roof U-value and wall U-value. The study has contributed methodologically to simulation-based optimization method, with novelties in the use of neural network algorithms to accelerate the otherwise time-consuming physics-based simulation models. It has also contributed a robust procedure in the tuning of hyperparameters in neural network models, with marked improvements in model prediction and computational efficiency.

Spatial iterative coordination for parallel simulation-based optimization of large-scale traffic signal control

Applying simulation-based optimization to city-scale traffic signal optimization can be challenging due to the large search space resulting in high computational complexity. A divide-and-conquer approach can be used to partition the problem and optimized separately, which leads to faster convergence. However, the lack of coordination among the partial solutions may yield a poor-quality global solution. In this paper, we propose a new method for simulation-based optimization of traffic signal control, called spatially iterative coordination for parallel optimization (SICPO), to improve coordination among the partial solutions and reduce synchronization between the partitioned regions. The traffic scenario is simulated to obtain the interactions, which is used to spatially decompose the scenario into regions and identify interdependencies between the regions. Based on the regions, the problem is divided into subproblems which are optimized separately. To coordinate between the subproblems, the interactions between partial solutions are synchronized in two ways. First, multiple iterations of the optimization process can be executed to coordinate the partial solutions at the end of each optimization process. Second, the partial solutions can also be coordinated among the regions by synchronizing the trips across the regions. To reduce computational complexity, parallelism can be applied on two levels: each region is optimized concurrently, and each solution for a region is evaluated in parallel. We demonstrate our method on a real-world road network of Singapore, where SICPO converges to an average travel time 21.6% faster than global optimization at 62.8× shorter wall-clock time.

Simulation-Based Reliability Design Optimization Method for Industrial Robot Structural Design

Robots are main elements in Industry 4.0. Research on the design optimization of robots has a great significance in manufacturing industries. There inevitably exist various uncertainties in robot design that have an important influence on the reliability of robots. At present, the design optimization of robots considering the uncertainties is mainly focused on joints design and trajectory optimization. However, for the structural design of robots, deterministic design optimization still plays a leading role. In this paper, a simulation-based reliability design optimization method is proposed to improve the reliability of robots’ structural design. In the proposed method, the Latin hypercube sampling (LHS), computer simulation, response surface method (RSM) and SORA (Sequential Optimization and Reliability Assessment) algorithm are integrated to complete the structural design of the robot. Firstly, samples of the uncertainty design variables were obtained by LHS, and then, the reliability performance constraint functions were firstly constructed through the RSM in which the joint simulation of MTLAB and ANSYS was adopted. Afterwards, the reliability design optimization model was established on the basis of the probabilistic reliability theory. At last, the SORA algorithm was employed to realize the optimization. The design optimization problems of the big arm and the small arm of a 6 Kg industrial robot were considered to verify the proposed method. The results showed that the weights of the big arm and the small arm were, respectively, reduced by 7.73% and 25.70% compared with those of the original design, and the design was more effective in ensuring the reliability requirements compared with the deterministic optimization. Moreover, the results also demonstrated that the proposed method has a better computational efficiency compared with the reliability design optimization of the double-loop method.

Development of a Simulation-Based Optimization Method for Wind Farm Layout on Complex Terrain

To improve the efficiency of wind power plants, multiple wind turbines must be placed in a way that generates a large amount of power while satisfying various constraints. For complex terrain such as mountainous regions in Japan, long simulation runs are required in order to calculate how much power is generated and determine whether the constraints are satisfied. Consequently, the manual processes and conventional simulation methods for deciding wind farm layouts are time-consuming. To automate and accelerate the process of wind farm placement, we propose a simulation-based optimization method for wind farm layout. In this method, simulation data are used to construct a prediction model, based on which the wind farm layout is then obtained by the optimization method. We conducted numerical experiments to evaluate our method and confirmed that it could find a wind farm layout that satisfied all given constraints and increased power generation by more than 30%, whereas the manual method could not find a layout that satisfied all the constraints in the same amount of time.

A simulation-based optimization method for facility layout considering the AGV path

Aiming at the problem of facility layout in intelligent workshops, a collaborative optimization method of facility layout and AGV path planning based on discrete event simulation (DES) is proposed. This method can increase the production capacity of the workshop by solving the workshop open-field layout problem (OFLP). First, to simplify the complex constraints of OFLP, a mathematical programming model is established. Second, a hybrid algorithm is proposed to solve the optimal facility layout and the corresponding multiple AGV paths. Then, the DES is used to calculate the logistics data of the facility layout, and the capacity is taken as the algorithm objective function. Finally, a practical intelligent workshop layout problem is taken as a research case. The calculation results show that the capacity of the workshop is increased by 54% under the optimized layout. Through the special nonlinear integer programming mathematical model and discrete simulation modelling, the optimization method can take into account the complex dynamic logistics process and multiple AGV conflicts and give full play to the flexible and efficient characteristics of open-field layouts. The final workshop layout can not only be close to the actual situation but also have excellent performance.