Simulation-based Optimization Research Articles

A Scoping Review on Simulation-Based Design Optimization in Marine Engineering: Trends, Best Practices, and Gaps

AbstractThis scoping review assesses the current use of simulation-based design optimization (SBDO) in marine engineering, focusing on identifying research trends, methodologies, and application areas. Analyzing 277 studies from Scopus and Web of Science, the review finds that SBDO is predominantly applied to optimizing marine vessel hulls, including both surface and underwater types, and extends to key components like bows, sterns, propellers, and fins. It also covers marine structures and renewable energy systems. A notable trend is the preference for deterministic single-objective optimization methods, indicating potential growth areas in multi-objective and stochastic approaches. The review points out the necessity of integrating more comprehensive multidisciplinary optimization methods to address the complex challenges in marine environments. Despite the extensive application of SBDO in marine engineering, there remains a need for enhancing the methodologies’ efficiency and robustness. This review offers a critical overview of SBDO’s role in marine engineering and highlights opportunities for future research to advance the field.

Smart management of pressure regulating stations to maximize hydrogen injection in a gas distribution network

Gas distribution networks traditionally maintain constant pressure setpoints at regulating stations, ensuring a consistent supply to end-users with a high safety margin. However, the prospect of hydrogen injection in the gas networks necessitates rethinking operational strategies. This study examines the environmental and fluid-dynamic implications of implementing a smart pressure management approach in regulating stations within a distribution network with a localized hydrogen injection. The research introduces a novel simulation-based optimization algorithm, incorporating a steady-state fluid-dynamic model with hydrogen tracking, used to optimize the pressure setpoints in the stations. The algorithm is applied to a two-pressure levels looped gas network with multiple gas supply nodes and a single hydrogen injection. The test case results reveal that utilizing dynamic pressure setpoints leads to a 22% increase in hydrogen penetration compared to standard operation. Therefore, this study highlights that smart pressure modulation in a gas network with hydrogen injection can unlock a latent decarbonization potential, while simultaneously adhering to safety limits.

A simulation-based optimization evaluating process and feed system effect in plastic injection molding

A simulation-based optimization evaluating process and feed system effect in plastic injection molding

Supply Chain Inventory Optimization With Multiple Types of Customers and Equal Priorities: A Study Using Heuristic and Simulation Methods

In this study, the optimal inventory policies for a supply chain with multiple types of customers is investigated to minimize the total inventory cost. Each customer type has specific demand patterns in terms of ordering frequency and order quantity; moreover, there is no difference in priority among the customers. Due to the computational complexity of this optimization problem, we develop a reduced form of the original problem and propose a heuristic that is easily implementable by practitioners. In addition, a simulation model is developed on the same problem setting as a benchmark. To illustrate the implementation and the effectiveness of the proposed heuristic, a numerical example is studied, followed by a sensitivity analysis of the key parameters. The numerical results show that our heuristic provides a comparable performance as simulation-based optimization (a 2.6% difference), and the sensitivity analysis shows a reliable robustness of the model (less than 5.2% difference).

Robust simulation-based optimization for multiobjective problems with constraints

Robust simulation-based optimization for multiobjective problems with constraints

A review on simulation based multi-objective optimization of space layout design parameters on building energy performance

Improving the energy performance of buildings is crucial for environmental protection, energy savings, and a better living environment. The growing emphasis on sustainable building practices has led to an increased focus on optimizing space layout design parameters to enhance building energy performance. This review explores the application of simulation-based multi-objective optimization techniques in the context of studying the impact of space layout design on building energy efficiency. The integration of advanced simulation tools with optimization algorithms allows for a comprehensive analysis of multiple conflicting objectives like energy performance, user comfort as well as cost factor. The review begins by outlining the key parameters influencing building energy performance, including spatial configurations, orientation, and space perimeter variables. Subsequently, it delves into the various simulation tools employed to model the complex interactions between these parameters and their effects on energy performance. The integration of energy simulation software is highlighted as a crucial step towards achieving accurate and realistic assessments. In summary, this review delivers a comprehensive overview of the state-of-the-art methods in simulation-based multi-objective optimization for studying space layout design parameters and their impact on building energy performance, offering insights for researchers, practitioners, and policymakers in the field of sustainable architecture. There is a requirement for a comprehensive multi-objective framework for complex structures in the investigation of building energy performance giving more focus on reducing the cooling load and optimization of space layout along with envelope parameters.

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.

Neural network-based surrogate modeling and optimization of a multigeneration system

Multi-Objective Optimization (MOO) poses a computational challenge, particularly when applied to physics-based models. As a result, only up to three objectives are typically involved in simulation-based optimization. To go beyond this number, Surrogate Models (SMs) need to replace such high-fidelity models. In this exploratory study, the objectives are to perform comprehensive regression surrogate modeling and to conduct MOO for a Multi-Generation System (MGS). The most suitable SM was chosen among four neural-network models: Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and an ensemble model developed through brute-force search using the three aforementioned models. The final model was found to be superior to others, achieving R2 values ranging from 0.9830 to 0.9999. Next, an optimization problem with six conflicting objectives was defined and performed at four distinct values of Direct Normal Irradiation (DNI), a time-dependent feature. This aimed to provide multi-criteria decision-making information based on atmospheric transparency. As a result, new understandings were gained: (I) exergy efficiency, production cost, and freshwater production rate were found to be highly influenced by DNI, and (II) the critical range of operation was observed within the DNI interval of 100 to 400 W/m2. Furthermore, we compared the result of the six-objective optimization with that of the bi-objective optimization obtained in our simulation-based study and found that all objectives showed improvements ranging from 1.9% to 12.7%. Finally, based on the findings obtained in the present study, some practical recommendations were put forward for applying the proposed methodology to similar MGSs.

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.

Design and optimization of boil-off gas recondensation process by recovering waste cold energy in LNG regasification terminals

In any LNG regasification terminal, boil-off gas (BOG) generation is unavoidable. BOG being predominantly methane, a sustainable and cost-effective BOG management process for any given LNG terminal conditions is of utmost necessary from both economic and environmental perspectives. LNG has substantial amount of cold energy, which is completely wasted when LNG is regassified using air or water at ambient conditions. In this study, we have developed a superstructure encompassing various process integration options to recondense the generated BOG completely using LNG cold energy. The proposed superstructure is optimized using a simulation-based optimization framework to minimize the total annualized cost for the BOG management process. We have performed several case studies to investigate the impact of different LNG terminal conditions on the BOG management process. Results obtained from our proposed framework not only yields the optimal configuration for BOG recondensation process but also provides the optimal operating conditions for any given LNG terminal condition. Our results show that variations in LNG compositions and regasification pressures do not influence the optimal configuration but they do influence the optimal process operating conditions. The amount of BOG to be recondensed strongly influences both optimal BOG management configuration as well as the process operating conditions.

An Optimal Computing Allocation Strategy for Swarm-Based Algorithm in Simulation-Based Optimization Problem with Stochastic Constraint

For realizing smart manufacturing, recent studies have been focusing on the capabilities of digital twins (DTs) that offer a virtual portrayal of a process or system and thereby facilitate real-time monitoring, analysis, and optimization. DTs construct a “digital model” of factories by using advanced technologies such as smart sensors, Internet of Things (IoT), artificial intelligence (AI) techniques, and optimization processes for executing critical decisions for enhancing productivity and predicting future events. Therefore, simulation-based optimization techniques play key supporting roles in the decision-making process of the DT shop. Thus, to enhance competitiveness in industrial companies, discourse on properly designing simulation-based optimization techniques begets significant study. This study aims to address a simulation-based optimization problem with stochastic constraint (SO[Formula: see text] using a hybrid particle swarm optimization (PSO). Given the large solution space, the PSO supports design space exploration. However, since all solutions must be verified for feasibility, the optimal computing allocation strategy (OCAS) strategy for SO[Formula: see text] is proposed to allocate the computing budget efficiently. OCAS is an improved version of optimal computing budget allocation for constrained optimization (OCBA-CO), specifically designed for complex systems with an expansive design space that possesses multiple local optimal solutions. During a typical PSO algorithm searching process, OCBA-CO is applied separately for each generation, resulting in a significant number of replications merely distinguishing similar solutions. To overcome this problem, our study proposes the PSO[Formula: see text], which combines PSO with OCAS to avoid wasting simulation runs on similar solutions. Two quantitative experiments are performed to assess the efficacy of PSO[Formula: see text] compared to other competitors. The simulation results indicate that the PSO[Formula: see text] algorithm presents a competitive and superior solution in terms of both quality and searching efficiency.

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.

Dimensional accuracy and simulation-based optimization of polyolefins and biocopolyesters for extrusion-based additive manufacturing and steam sterilization.

Polyolefins exhibit robust mechanical and chemical properties and can be applied in the medical field, e.g. for the manufacturing of dentures. Despite their wide range of applications, they are rarely used in extrusion-based printing due to their warpage tendency. The aim of this study was to investigate and reduce the warpage of polyolefins compared to commonly used filaments after additive manufacturing (AM) and sterilization using finite element simulation. Three types of filaments were investigated: a medical-grade polypropylene (PP), a glass-fiber reinforced polypropylene (PP-GF), and a biocopolyester (BE) filament, and they were compared to an acrylic resin (AR) for material jetting. Square specimens, standardized samples prone to warpage, and denture bases (n = 10 of each group), as clinically relevant and anatomically shaped reference, were digitized after AM and steam sterilization (134 °C). To determine warpage, the volume underneath the square specimens was calculated, while the deviations of the denture bases from the printing file were measured using root mean square (RMS) values. To reduce the warpage of the PP denture base, a simulation of the printing file based on thermomechanical calculations was performed. Statistical analysis was conducted using the Kruskal-Wallis test, followed by Dunn's test for multiple comparisons. The results showed that PP exhibited the greatest warpage of the square specimens after AM, while PP-GF, BE, and AR showed minimal warpage before sterilization. However, warpage increased for PP-GF, BE and AR during sterilization, whereas PP remained more stable. After AM, denture bases made of PP showed the highest warpage. Through simulation-based optimization, warpage of the PP denture base was successfully reduced by 25%. In contrast to the reference materials, PP demonstrated greater dimensional stability during sterilization, making it a potential alternative for medical applications. Nevertheless, reducing warpage during the cooling process after AM remains necessary, and simulation-based optimization holds promise in addressing this issue.

A carbon neutral supply chain management by considering emission-risk minimization and green purchasing through optimal decision-making

This study addresses critical gaps in supply chain management (SCM) by integrating emission-risk minimization (ERM), green purchasing (GP), and profit maximization (PM). The research focuses on the optimal behaviors of manufacturers, agents, and retailers within the SCM framework to achieve carbon neutrality and reduce carbon dioxide emissions (CO2e). This study considers Guangdong province, China, a region facing challenges in optimizing energy systems and meeting CO2e reduction targets. Simulation-based optimization techniques within mathematical models are employed. A design of experiment (DOE) method was used to explore the dynamics of key variables in the SCM environment. Results reveal optimal behaviors for manufacturers, agents, and retailers, demonstrating the ideal values for profit and pricing decisions. Manufacturers optimize production quantity, achieving CO2e reduction and PM through ERM. Agents exhibit a strong commitment to GP practices, enhancing PM and carbon-neutral goals. Retailers get more PM than manufacturers and agents, contributing to a clean environment. Interestingly, retailers make contributions to the clean environment without considering ERM and GP in SCM. The study contributes novel insights by addressing the identified gap in SCM research, emphasizing the joint consideration of ERM, GP, and PM. This research assists manufacturers, agents, and retailers in terms of PM for economic objectives. It cleans the environment through carbon-neutral SCM in society.

Future of precision manufacturing: Integrating advanced metrology and intelligent monitoring for process optimization

Precision manufacturing is undergoing a transformative evolution fueled by the integration of advanced metrology techniques and intelligent monitoring systems. This abstract explores the future trajectory of precision manufacturing through the convergence of these technologies, focusing on their synergistic role in process optimization. Advanced metrology techniques, including high-resolution imaging, laser scanning, and non-contact surface measurement, provide unprecedented levels of accuracy and detail in capturing dimensional data. These techniques enable manufacturers to precisely analyze component geometry, surface finish, and tolerances, facilitating the production of parts with exceptional precision and quality. Moreover, the integration of metrology within the manufacturing process allows for real-time feedback, enabling rapid adjustments and corrections to ensure adherence to design specifications. Intelligent monitoring systems complement advanced metrology by continuously collecting data from various sensors embedded within manufacturing equipment. These systems utilize artificial intelligence (AI) and machine learning algorithms to analyze vast amounts of data in real-time, detecting anomalies, predicting equipment failures, and optimizing process parameters. By leveraging data-driven insights, manufacturers can enhance production efficiency, minimize downtime, and reduce scrap rates. The synergy between advanced metrology and intelligent monitoring extends beyond quality control to encompass holistic process optimization. Through the seamless integration of these technologies, manufacturers can achieve unparalleled levels of precision, efficiency, and flexibility in their operations. For instance, real-time metrology feedback combined with AI-driven monitoring enables adaptive manufacturing processes that dynamically adjust parameters based on changing environmental conditions or material properties. Furthermore, the future of precision manufacturing lies in the adoption of a digital twin approach, where virtual replicas of physical manufacturing systems are created and synchronized with real-time data. This enables predictive maintenance, virtual prototyping, and simulation-based optimization, leading to significant cost savings and accelerated innovation cycles. The future of precision manufacturing hinges on the integration of advanced metrology and intelligent monitoring technologies. By harnessing the synergies between these innovations, manufacturers can achieve unprecedented levels of precision, efficiency, and agility, driving forward the evolution of manufacturing in the digital era.

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.

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.

Hyperparameter Tuning with Gaussian Processes for Optimal Abstraction Control in Simulation-based Optimization of Smart Semiconductor Manufacturing Systems

Smart manufacturing utilizes digital twins that are virtual forms of their production plants for analyzing and optimizing decisions. Digital twins have been mainly developed as discrete-event models (DEMs) to represent the detailed and stochastic dynamics of productions in the plants. The optimum decision is achieved after simulating the DEM-based digital twins under various what-if decision candidates; thus, simulation acceleration is crucial for rapid optimum determination for given problems. For the acceleration of discrete-event simulations, adaptive abstraction-level conversion approaches have been previously proposed to switch active models of each machine group between a set of DEM components and a corresponding lookup table-based mean-delay model during runtime. The switching is decided by detecting the machine group’s convergence into (or divergence from) a steady state. However, there is a tradeoff between speedup and accuracy loss in the adaptive abstraction convertible simulation (AACS), and inaccurate simulation can degrade the quality of the optimum (i.e., the distance between the calculated optimum and the actual optimum). In this paper, we propose a simulation-based optimization (SBO) that optimizes the problem based on a genetic algorithm (GA) while tuning specific hyperparameters (related to the tradeoff control) to maximize the speedup of AACS under a specified accuracy constraint. For each individual, the proposed method distributes the overall computing budget for multiple simulation runs (considering the digital twin’s probabilistic property) into the hyperparameter optimization (HPO) and fitness evaluation. We propose an efficient HPO method that manages multiple Gaussian process models (as speedup-estimation models) to acquire promising optimal hyperparameter candidates (that maximize the simulation speedups) with few attempts. The method also reduces each individual’s exploration overhead (as the population evolves) by estimating each hyperparameter’s expected speedup using previous exploration results of neighboring individuals without actual simulation executions. The proposed method was applied to optimize raw-material releases of a large-scale manufacturing system to prove the concept and evaluate the performance under various situations.

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.