Simulation-based Optimization Approach Research Articles

Design optimisation of roadside safety roller barriers

Safety roller barrier (SRB) is an innovative road safety device that utilises foam rollers to absorb and convert shock energy into rotational energy, showcasing its remarkable ability to minimise the risk and severity of casualties in road accidents. In this study, a simulation-based optimisation approach was employed to obtain the optimal design for the SRB. Quasi-static and dynamic mechanical properties of the construction materials were experimentally obtained. SRB finite element (FE) models were then created based on installed systems in multiple Australian cities. The model was validated using full-scale TL4 crash tests according to the specifications outlined in the Manual for Assessment of Safety Hardware (MASH). Subsequently, a parametric study was conducted to explore the influence of design parameters such as post spacing, rail height, and roller number. Finally, a parallel Bayesian optimisation algorithm was applied to re-design the SRB system with enhanced performance and reduced cost. This optimisation process yielded several sets of optimal parameters, paving the way for a more effective and economically viable SRB design.

Optimal sizing and energy management strategy for an office building with photovoltaic, heat pump, thermal tank, and electric vehicle under time-of-use tariff

The increasing demand for sustainable energy solutions is driving the integration of various renewable energy technologies. Integrating electric vehicle batteries, photovoltaics, heat pumps, and thermal tanks into building energy systems offers a promising approach to enhancing energy flexibility and efficiency. However, research on such integrated energy systems is still in its infancy. Optimizing the configuration and operation of the system may lead to significant cost savings and improved energy management. A coupled model and simulation-based optimization approach are used to optimize the thermal tank capacity and key operational parameters to minimize the annual total cost. The results show that the integrated energy system reduces the annual total cost by 36.35% and monthly electricity bills by up to 69.26% compared to the base system. As for energy flexibility, only 13.91% of electricity demand is supplied by the grid during peak periods, and the monthly load coefficient, load cover ratio, and photovoltaic self-consumption ratio of the integrated energy system are significantly improved. This study demonstrates the economic and operational benefits of integrating various renewable energy technologies into building energy systems and provides new insights into their design and optimization.

Viable Vitreous Grafts of Whole Porcine Menisci for Transplant in the Knee and Temporomandibular Joints.

The shortage of suitable donor meniscus grafts from the knee and temporomandibular joint (TMJ) impedes treatments for millions of patients. Vitrification offers a promising solution by transitioning these tissues into a vitreous state at cryogenic temperatures, protecting them from ice crystal damage using high concentrations of cryoprotectant agents (CPAs). However, vitrification's success is hindered for larger tissues (>3 mL) due to challenges in CPA penetration. Dense avascular meniscus tissues require extended CPA exposure for adequate penetration; however, prolonged exposure becomes cytotoxic. Balancing penetration and reducing cell toxicity is required. To overcome this hurdle, a simulation-based optimization approach is developed by combining computational modeling with microcomputed tomography (µCT) imaging to predict 3D CPA distributions within tissues over time accurately. This approach minimizes CPA exposure time, resulting in 85% viability in 4-mL meniscal specimens, 70% in 10-mL whole knee menisci, and 85% in 15-mL whole TMJ menisci (i.e., TMJ disc) post-vitrification, outperforming slow-freezing methods (20%-40%), in a pig model. The extracellular matrix (ECM) structure and biomechanical strength of vitreous tissues remain largely intact. Vitreous meniscus grafts demonstrate clinical-level viability (≥70%), closely resembling the material properties of native tissues, with long-term availability for transplantation. The enhanced vitrification technology opens new possibilities for other avascular grafts.

Multi-objective optimisation of a logistics area in the context of factory layout planning

ABSTRACT The manufacturing factory layout planning process is commonly supported by the use of digital tools, enabling creation and testing of potential layouts before being realised in the real world. The process relies on engineers’ experience and inputs from several cross-disciplinary functions, meaning that it is subjective, iterative and prone to errors and delays. To address this issue, new tools and methods are needed to make the planning process more objective, efficient and able to consider multiple objectives simultaneously. This work suggests and demonstrates a simulation-based multi-objective optimisation approach that assists the generation and assessment of factory layout proposals, where objectives and constraints related to safety regulations, workers’ well-being and walking distance are considered simultaneously. The paper illustrates how layout planning for a logistics area can become a cross-disciplinary and transparent activity, while being automated to a higher degree, providing objective results to facilitate informed decision-making.

Digital twin-based reinforcement learning framework: application to autonomous mobile robot dispatching

ABSTRACT This paper proposes a new framework for embedding an Intelligent Digital Twin (DT) in a production system with the objective of achieving more efficient real-time production planning and control. For that purpose, the Intelligence Layer is based on Reinforcement Leaning (RL) and Deep RL (DRL) algorithms. The use of this control instead of parametric simulation-based optimization approach allows to benefit from the separation between the training and execution phase. To ensure consistency and reusability, this work presents a standardized framework, based on a formal methodology, that specifies how the various components of the DT-based RL architecture interact over time to achieve essential real-time concurrency and synchronization aspects. Experiments are conducted in a small-scale production system where material handling operations are performed by an Autonomous Mobile Robot (AMR) in an Industry 4.0 Laboratory. Results showed how synchronized state updates between the Physical and Cyber World are used within the Decision Layer to ensure real-time response for the AMR dispatching requests. Finally, to deal with continuous and high-dimensional state spaces, the Deep Q-Network is implemented. The findings of an extensive computational study reveal that implementing the DT-based DRL solution leads to improved efficiency and robustness when compared to conventional dispatching rules.

Exploring Heuristic and Optimization Approaches for Elevator Group Control Systems

This paper undertakes an examination of elevator car dispatching methods in response to hall calls. Firstly, our study focuses on the establishment of a representation of an elevator group control system as a finite-state machine to understand the dynamics of elevator group control. Secondly, two primary heuristics are explored, with the first advocating directional continuity unless the highest or lowest floor has been reached, while the second permits direction change upon completing the final call, regardless of floor extremes. Identified inefficiencies in these heuristic solutions lead us to explore enhanced alternatives. Consequently, we delve into genetic algorithm (GA) and simulated annealing (SA) methodologies. Our focus initially centers on devising solution representations and determining fitness evaluations for both approaches. We employ a simulation-based optimization approach to identify the optimal parameter values for both simulated annealing and genetic algorithms. A subsequent comparative analysis is conducted to ascertain the most effective approach among these diverse solutions. A comparative analysis reveals that the GA-based approach significantly outperforms both existing heuristics and the SA-based method in minimizing average passenger waiting time at the cost of longer computational time.

Enhancing the effectiveness of bioaerosol disinfection in indoor environments by optimizing far-UVC lamp locations based on Markov chain model

Far-ultraviolet C (far-UVC) light is an effective and safe disinfection method for bioaerosol control in occupied indoor environments. The installation location of a far-UVC lamp strongly influences the spatial distribution of far-UVC irradiance, and thus the effectiveness of bioaerosol disinfection. To assist the design process, this study developed a fast prediction approach based on the Markov chain model for optimizing the installation locations of far-UVC lamps in order to enhance the disinfection effectiveness for indoor bioaerosol control. Experiments were conducted in an environmental chamber to validate the proposed simulation-based optimization approach. The results show that the proposed method can correctly predict the disinfection efficiency when compared with experimental data, and optimizing the installation location of the far-UVC lamp increased the disinfection efficiency by 54 % compared with the worst location. As an application, the validated method was then used to design the installation location of a far-UVC lamp in a real conference room. The results show that installing the far-UVC lamp in the optimal location can increase the disinfection efficiency by 48 % compared with the worst installation location. Therefore, optimizing the far-UVC lamp location using the proposed Markov chain model can enhance the effectiveness of bioaerosol disinfection in indoor environments.

On-demand last-mile distribution network design with omnichannel inventory

E-commerce delivery deadlines are getting increasingly tight, driven by a growing ‘I-want-it-now’ instant gratification mindset of consumers and the desire of online and omnichannel retailers to capitalize on the growth of on-demand e-commerce. On-demand deliveries with delivery deadlines as tight as one or two hours force companies to rethink their last-mile distribution network, since tight delivery deadlines require decentralization of order picking and inventory holding to ensure close proximity to consumers. This fundamentally changes the strategic design process of last-mile distribution networks. We study the impact of incorporating inventory order-up-to level decisions into the strategic design process of last-mile distribution networks with tight delivery deadlines. We develop an approximate inventory model by including an estimate of the cost of late delivery and additional transportation due to local stock-outs in a newsvendor formulation. Such local stock-outs require an order to be delivered from a more distant facility, which may lead to late delivery and additional transportation cost. We integrate our approximate inventory model and a location–allocation mixed-integer program that determines optimal facility locations, associated order-up-to inventory levels, and fleet composition, into a metamodel simulation-based optimization approach. Our numerical analyses demonstrate that pooling the additional online inventory with brick-and-mortar (B&M) inventories leads to cannibalization by the B&M network and higher B&M service levels. However, the pooling benefits to the online network outweigh the cost of inventory cannibalization. Furthermore, we show under which circumstances omnichannel retailers may have an incentive to consolidate online inventory in specific B&M facilities.

Multi-objective optimization of sandwich structures for reinforcing composite fuselages

This study introduces a simulation-based optimization approach that combines a multi-objective genetic algorithm and the finite element method to design sandwich structures to reinforce composite fuselages. The sandwich structure has been parametrized with a set of mixed integer-continuous variables representing the polymer core thickness, the number and weight of laminated fiberglass layers, and size and position of the structure. Automatic scripting builds the geometry of the structure, then mounts it in the fuselage, and finally creates a conformal mesh. During the optimization, each reinforced fuselage is subjected to several load cases specified by the CS-22 EASA design standard, determining the worst load condition. Two objectives were considered: increasing the reinforced fuselage’s minimum fiber stress safety factor for improved safety and reducing the number of layers of the sandwich structure to minimize manufacturing costs. Structural constraints were the mass of the sandwich structure and buckling and flexo-torsional deformation of the reinforced fuselage. Results show an efficient material arrangement due to the reduced number of layers in the sandwich structure and a stress safety factor surpassing that set by the standard. Finally, the reinforcement of a glider fuselage to incorporate a retractable electric propulsion system is presented as a real-world industrial application.

Techno-economic analysis and process optimization of a PET chemical recycling process based on Bayesian optimization

Chemical recycling has gained interest as a promising option to reduce the consumption of fossil feedstocks and relieve environmental issues concerning plastic waste. In this study, we present a preliminary techno-economic analysis of a process design for chemical recycling of polyethylene terephthalate (PET). A conceptual base case design was developed for a continuous catalytic PET depolymerization process. The process design was optimized to minimize total annualized cost (TAC) based on a simulation-based Bayesian optimization approach. A novel interface was utilized to manipulate the process variables, and to retrieve simulation and economic evaluation results. Through five trials of optimization, the process design was improved by about 4.2∼4.4 % in terms of TAC. Sensitivity analyses investigating the effect of process parameters and price variables on the minimum selling price of main product, p-xylene, are presented. The feedstock PET flake price was identified as the most critical factor affecting the economic viability of the process.

Simulation-Based Headway Optimization for the Bangkok Airport Railway System under Uncertainty

The ever-increasing demand for intercity travel, as well as competition among all modes of transportation, is an unavoidable reality that today’s urban rail transit system must deal with. To meet this problem, urban railway companies must try to make better use of their existing plans and resources. Analytical approaches or simulation modeling can be used to develop or change a rail schedule to reflect the appropriate passenger demand. However, in the case of complex railway networks with several interlocking zones, analytical methods frequently have drawbacks. The goal of this article is to create a new simulation-based optimization model for the Bangkok railway system that takes into account the real assumptions and requirements in the railway system, such as uncertainty. The common particle swarm optimization (PSO) technique is combined with the developed simulation model to optimize the headways for each period in each day. Two different objective functions are incorporated into the models to consider both customer satisfaction by reducing the average waiting time and railway management satisfaction by reducing needed energy usage (e.g., reducing operating trains). The results obtained using a real dataset from the Bangkok railway system demonstrate that the simulation-based optimization approach for robust train service timetable scheduling, which incorporates both passenger waiting times and the number of operating trains as equally important objectives, successfully achieved an average waiting time of 11.02 min (with a standard deviation of 1.65 min) across all time intervals.

Probability-based visual comfort assessment and optimization in national fitness halls under sports behavior uncertainty

Sports behavior uncertainty is a main cause of mismatch between simulated and actual visual comfort in sports spaces, which has not been properly addressed in existing studies. This study proposes a multi-phase and multi-objective optimization approach to improve visual comfort in national fitness halls by better considering sports behavior uncertainty. Specifically, a surrogate-based multi-objective optimization workflow is developed for fast optimization of visual comfort. Then, a probabilistic behavior model is created to predict player's occupancy probability in all possible view scenes during sports, and based on which the probabilistic visual comfort is introduced to assess player's realistic visual comfort taking account of the occupancy probability. The sport of tennis in a parametric national fitness hall is selected as the case study scenario. Results indicated that the proposed response surface model outperformed artificial neural network model in predicting visual comfort, while this approach saved 35.43% of total computational time versus the traditional simulation-based multi-objective optimization approach in this case study; 296 Pareto solutions out of 1758 design alternatives were quickly identified, among which the final optimum solution indicated an approximately 200% better visual comfort against the worst one; Sports behavior uncertainty was found to affect player's visual comfort assessment in tennis sports, presenting variances on different court locations under two court layouts. This study contributes new insights of probabilistic visual comfort assessment under sports scenarios and explores the potential of court location and layout arrangement for multi-purpose usage consideration in national fitness halls.

Maximizing load capacity factor through a carbon-neutral environment via a simulation of carbon peak

Carbon emissions are a global issue, especially among manufacturers, and China is facing many problems to lower such pollutants due to its goal of attaining carbon neutrality. This research applies a mathematical model to achieve the desired goal of decreasing CO2 with the innovation of carbon neutrality tools and by moving towards high quality under the load capacity factor (LCF). We adopt a simulation-based optimization approach at different carbon peaks and manipulate various scenarios in order to find the best decisions for carbon neutrality tools. The results indicate that the green innovation fund, CO2 emission tax, and CO2 emission cap have significant roles in maximizing LCF. First, the green innovation fund plays a crucial part in reducing carbon emissions. Second, a CO2 emission tax allows manufacturers to produce less than the green innovation fund and CO2 emission cap. Third, the CO2 emission cap strategy conversely allows manufacturers to produce more than the CO2 emission tax strategy. This study provides reference for policymakers to achieve carbon neutrality and for the government to determine the best carbon-neutral tools under carbon peak. Lastly, the findings help manufacturers maximize LCF and profit and clean up the environment.

Fleet sizing and charging infrastructure design for electric autonomous mobility-on-demand systems with endogenous congestion and limited link space

Autonomous vehicles are to revolutionize the way urban mobility demands are served, and they are most likely to be powered by electricity. To accurately quantify the benefits of replacing existing mobility services with autonomous electric vehicles, electric autonomous mobility-on-demand (EAMoD) systems need to be evaluated and designed with the congestion effects they may cause taken into account. In this work, the congestion effects are depicted through a discrete event simulation model with state-dependent link travel speed and limited link space that allow endogenous congestion to emerge, spread, and dissipate across the entire road network. Three mathematical models are integrated into the simulation model to optimally match vehicles with waiting requests, relocate empty vehicles to potential high-demand areas, and assign low charge vehicles to charging stations based on the workloads of the charging stations. Based on the simulation model, we apply Bayesian optimization to jointly design the fleet size and charging facility configuration considering the endogenous congestion incurred by the daily operation of autonomous electric vehicles. Contraction hierarchies are adopted to route vehicles to perform assigned tasks in real-time. The proposed solution method is tested on Manhattan below 60th street, which corresponds to the potential congestion pricing zone in New York City. Experiment results show that the proposed simulation-based optimization approach is computationally tractable, and can find a satisfactory solution to the fleet sizing and charging infrastructure design problem within a tight computation budget. Excluding congestion effects at system design stage would lead to up to 14% passenger loss due to long assignment waiting time. Although the charging facility cost accounts for only 1.8% of the total cost, the number of chargers can indirectly affect the percentage of passengers that can be served through its efficiency in recharging vehicles. A charging facility configuration aligned with the fleet size can help to improve service quality and vehicle utilization rate.

Impacts of photovoltaic and energy storage system adoption on public transport: A simulation-based optimization approach

Photovoltaic and energy storage system (PESS) adoption in public transport (PT) can offer a promising alternative towards reducing the charging and carbon emission costs of transit agencies. However, the quantitative impacts of PESS on operational cost, carbon emission cost, bus scheduling, and energy management in PT remain unclear. This study is performed with the aim of filling this research gap by presenting an impact analysis framework incorporating multisource datasets and a simulation-based optimization approach. A simulation is designed to capture bus scheduling and energy management on a day-to-day transit operation. A surrogate-based optimization method is adopted to determine an optimal PESS configuration for battery electric bus (BEB) charging stations. A case study is performed by introducing PESS at BEB charging stations in Beijing, China, leveraging historical weather and bus operational data. Compared with conventional charging stations, the novel transit system with PESS reduces the annual charging costs and carbon emissions of a single bus route in this case study by 17.6% and 8.8% on average, respectively. The results suggest that the recycling electricity price of PV generation is the key to influencing charging costs and carbon emissions. Battery capacities also impact the scheduling pattern of PESS considerably. Further comparison across five bus routes in Beijing reveals that the deployment locations of PESS are key decisions influencing the operational cost and carbon emissions in PT at the network level when the investment budget is limited.

An enhanced k-[formula omitted] SST model to predict airflows around isolated and urban buildings

The goal of this research is to improve and validate a Reynolds Averaged Navier–Stokes (RANS) turbulence model to perform accurate Computational Fluid Dynamics (CFD) simulations of the urban wind flow. The k-ω SST model is selected for calibration since its blended formulation holds remarkable optimization potential and has increased relevancy in recent studies in the field. A simulation-based optimization approach recalibrates the model closure constants by minimizing the prediction error of wind pressure coefficients on an isolated cubical building because this scenario contains many salient features observed in the flow in actual urban areas. The optimization procedure ensures both the coherence of calibrated model constants involved in the wall function formulations and the relationship between them to satisfy the flow horizontal homogeneity of the atmospheric boundary layer. The tuned closure coefficients increase momentum diffusion in the wake, resulting in shorter and more accurate predictions of the reattachment lengths. Validation case studies with wind tunnel measurement data from various urban scenarios were addressed to comprehensively assess the adaptability of the optimal set of coefficients reached. The results confirm that CFD predictions with the optimized model are consistently in closer agreement with experimental data than the standard version of k-ω SST. The root mean square errors are reduced by about 75% in pressure, 40% in velocity, and 20% in turbulent kinetic energy.

Design and analysis of pumpjet propulsors using CFD-based optimization

The design of pumpjet propulsors (PJP) is addressed through a simulation-based design optimization approach built on a parametric description of the main geometrical characteristics of the system, a RANSE solver with mixing plane interface capabilities and a genetic algorithm. Both Rotor/Stator (PJP-R/S) and Stator/Rotor (PJP-S/R) configurations are considered and optimal designs from a multi-objective optimization process aimed at maximizing the propulsive efficiency at the lowest possible cavitation inception index are compared to a reference ducted propeller with decelerating nozzle (Gaggero et al., 2012), which served as baseline during the design. Detached Eddy Simulations (DES) were finally carried out to highlight, in addition to the performance improvements provided by the PJPs, also the influence of the rotor/stator/nozzle interactions on the vortical structures shed by the propulsor.

An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems

In today’s uncertain and competitive market, where manufacturing enterprises are subjected to increasingly shortened product lifecycles and frequent volume changes, reconfigurable manufacturing system (RMS) applications play significant roles in the success of the manufacturing industry. Despite the advantages offered by RMSs, achieving high efficiency constitutes a challenging task for stakeholders and decision makers when they face the trade-off decisions inherent in these complex systems. This study addresses work task and resource allocations to workstations together with buffer capacity allocation in an RMS. The aim is to simultaneously maximize throughput and to minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach supports decision makers with knowledge discovery to further understand RMS design. In particular, this study presents a customized SMO approach combined with a novel flexible pattern mining method for optimizing an RMS and conducts post-optimal analyses. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision support and production planning of an RMS.

Development of a simulation-based optimization approach to integrate the decisions of maintenance planning and safety stock determination in deteriorating manufacturing systems

In this article, a stochastic model is presented to schedule the maintenance tasks and control the inventory simultaneously in an unreliable deteriorating production system. The optimal planning of these aspects improves the productivity of the production system and reduces the costs. The shift time of the states follows a general continuous distribution. The time of maintenance is also considered as a random variable, and a buffer stock is maintained to avoid possible shortage and satisfy the demand during the performance of maintenance. The purpose of this study is to integrate the decisions of safety stock determination and maintenance scheduling to minimize the expected total cost of the system. Due to the complexity of the equations of the model, at first, a genetic algorithm (GA) is employed for optimization. To validate the GA solutions, they are compared with those obtained by a grid search algorithm. It is found that the differences between the optimal values of the objective function (ECT) in GA and grid search are almost small and, in some cases, only 5.58% and 2.03%. This result indicates the validity of the solutions obtained by the GA. In the next step, to speed up the run time of the proposed GA, a Monte Carlo simulation approach is developed, and the GA is combined with the simulation approach. In particular, the fitness function of each chromosome is obtained using the proposed simulation approach. In order to examine the performance of the proposed simulation approach, the results of simulation are compared with the findings on optimization by the GA. This comparison indicates that the difference between the values of ECT calculated through the GA and simulation is averagely 4.55%, which is negligible. The results denote the appropriate performance of the model and the efficiency of the proposed hybrid approach. Finally, comprehensive sensitivity analyses are performed regarding the parameters of the model.

A simulation-based optimization approach for poultry axial exhaust fans to fulfill aerodynamic and mechanical service constraints

A simulation-based optimization approach for poultry axial exhaust fans to fulfill aerodynamic and mechanical service constraints