Optimization Framework Research Articles

Multi-objective optimization of a novel dynamic concentrated skin system considering daylight, building skin capacity, and visual space

Transmissive concentrator technology can solve the problems of expression of building skin form, maximized use of solar energy, and natural lighting inside the building that arise from integrating traditional photovoltaic technology. However, the advantages mentioned above interact and cannot be maximized to solve multiple problems simultaneously. Therefore, it becomes crucial to address the relationship between them optimally and improve the quality of the building and the environment. This study used grasshopper, ladybug, and multi-objective optimization tools to develop and analyze a transmission concentrator module integrated into an office building skin. An optimization framework for dynamic concentrating skins is constructed by modifying important design variables, resolving conflicting relationships between energy output, daylight quality, and visual space, and computing optimal solutions to multiple problems. The results show that the concentrated skin scheme, compared with the common skin and no skin scheme, can reduce the likelihood of glare by an average of 59.35% and 88.40%, respectively, and improve the useful daylight illuminance by an average of 2.40% and 16.25%, respectively, while providing the user with a visual field rate of about 80.54%, in the presence of higher energy output. In addition, thanks to the variation of the concentrating skin variables, using the recommended value of optimal interval and the most significant variables can better resolve the relationship between environment and energy and obtain optimal overall performance and solutions. This study provides data and methods for the integrating design of concentrating skin and optimizing indoor daylighting, which is expected to reduce carbon emissions.

A Framework for Aircraft Conceptual Design and Multidisciplinary Optimization

In this research, the architecture and the functionalities of the LAMBDA (Laboratory of Aircraft Multidisciplinary Knowledge-Based Design and Analysis) framework for the design, analysis, and optimization of civil aircraft are presented. The framework is developed in MATLAB R2022a and comprises a modular architecture, which gives the potential for the use of different methods and fidelities for each discipline. The methods can be selected from a set of built-in methods or custom user-defined scripts. Disciplinary modules of the LAMBDA are Requirements, Weight, Sizing, Geometry, Aerodynamics, Engine, Performance, Cost, Emission, and Optimization. This framework has been used for different types of design and optimization problems. When it is applied for the design and optimization of a novel regional TBW (Truss-Braced Wing) aircraft, the operating cost has been reduced by 7.7% in the optimum configuration compared to the base configuration.

Large-scale optimization of nonconvex MINLP refinery scheduling

Modeling and optimization of large-scale refinery scheduling problems is challenging because of their complexity and size. Herein, we propose a mathematical model to represent such problems more accurately and realistically, and a state-of-the-art optimization framework for its solution. The framework leverages the use of mathematical optimization and algorithmic methods by combining modeling approaches (process design, model decompositions), solving strategies (rescheduling, heuristics), and machine learning regression (surrogate models). An industrial-size refinery scheduling problem (2 blenders, 4 feed tanks, distillation network with 5 towers, processing network with FCC, hydrotreaters, debutanizers, superfractionator, catalytic reformer) is formulated as a hierarchical nonconvex mixed-integer nonlinear programming (MINLP) model and is successfully optimized, providing higher profitability and more efficient scheduling operations considering 12 feedstocks, 10 products and multiple scenarios for time horizon and step. Results highlight the importance of tuning scheduling parameters and employing an enhanced computer-aided framework to enable the solution of industrial refinery scheduling operations.

Genetic Algorithm-Based Framework for Optimization of Laser Beam Path in Additive Manufacturing

In this study, a genetic algorithm-based laser beam (LB) path optimization method is presented to improve laser-based additive manufacturing (LBAM). To emulate the LBAM process, LB irradiation of a thin metal substrate is applied. The LB path generation is formulated as the search for the optimal sequence of LB irradiation into the cells on the substrate that minimizes the fitness function, which is composed of two components, i.e., thermal fitness and process fitness. The thermal fitness is expressed by the average thermal gradient, and a simple thermal model is developed to simulate the effects of laser-induced heat input on the temperature distribution in the substrate. The process fitness regulates the suitability of the proposed LB path for the implementation of the LBAM process. In addition to standardized tool paths (i.e., raster, spiral, etc.), novel LB path generators are proposed to define the initial population of LB path solutions. To implement a genetic algorithm-based LB path optimization, a framework is proposed, and custom initialization, crossover, and mutation operators are developed for application in LBAM. The effectiveness of the proposed approach is demonstrated through a simulation case study aiming to identify LB paths that minimize the fitness function and thus provide more suitable LB path solutions with respect to the defined fitness function. Compared with the traditional trial-and-error LB path formulations, the proposed approach provides an improved and automated method for an efficient laser beam path selection in LBAM.

Demand-Responsive Transit Service With Soft Time Windows Considering Real-Time Disruptions Based on Bounded Rationality

Demand-responsive transit (DRT) with smartphone-based applications is emerging as a flexible and sustainable mobility service, transforming urban transportation. Nevertheless, to satisfy the real-time and inconsistent demand, it is becoming increasingly important to capture the decision-making psychology of order cancellations. In this study, a two-phase optimization framework is presented in response to real-time disruptions, including order cancellations and the insertion of new real-time passengers. In contrast to random real-time demand, this paper is more concerned about the impacts of the feedback information on order cancellations. Bounded rationality is incorporated into the model to discuss the decision-making process of cancellation behaviors. With regard to the soft window, a compensation strategy is proposed to promote the profit while encouraging passengers for a long-term use. Additionally, solution algorithm based on variable neighborhood search (VNS) and rolling horizon is constructed to approach the Pareto solutions set. To testify the validity of the proposed algorithm, small-scale experiments in simplified Sioux Falls network are investigated for multiple runs. Meanwhile, a real-world case study in Beijing is explored to evaluate the system performance considering real-time disruptions. The results indicate that the dynamic DRT service can substantially improve the system profit but increase the penalty cost. The profit presents a significant improvement to 940 (renminbi) RMB as a result of the insert of real-time passengers. This study, therefore, not only provides a deeper insight into the analysis of passenger cancellation behavior but also contributes to construct a more flexible DRT service.

Derivation of Growth of Import Demand: A Time Series Analysis of Less Developed Countries

In this article, growth of import demand is estimated in a dynamic optimisation framework. Since a conventional micro-theoretic import demand function cannot capture the issue of the growth of import demand, it is derived in a macro-theoretic framework of equilibrium import demand function. Growth rate of import demand in a dynamic optimisation framework (considering utility maximisation problem) has not yet been found in the literature. The main focus of this article is to derive this. It is found that growth rate of imports is the function of the growth of domestic income or expenditure, the growth rate of relative price, which is nothing but the real exchange rate, and productivity of capital. The theoretically derived import growth equation was empirically verified for 35 less developed countries. It is observed that for most of the less developed countries, independent variables are statistically significant with expected sign. JEL Codes: F130, F14

Active learning-based optimization of structures under stochastic excitations with first-passage probability constraints

In various efforts to manage the risk of structural failures caused by stochastic excitations such as wind and earthquake loads, the first-passage probability often needs to be calculated as a reliability constraint. Estimating the first-passage probability with low computational costs is essential, especially in structural optimization requiring iterative reliability calculations for design alternatives. This paper presents a new active learning-based framework for reliability-based design optimization (RBDO) of structures under stochastic excitations. A mixture-distribution-based formulation of the first-passage probability is utilized to handle the high-dimensional sequences of stochastic excitations during the optimization. The design parameter sensitivity of the first-passage probability is introduced to use a gradient-based optimizer in the RBDO iterations. These procedures employ heteroscedastic Gaussian process-based surrogates of the logarithmic responses. An active-learning scheme identifies the best training point to reduce the computational costs in the first-passage probability calculations and optimization. The numerical examples dealing with the optimal design of an eight-story building system and a tower structure subjected to stochastic wind loads demonstrate the accuracy and efficiency of the proposed method.

Latent Crossover for Data-Driven Multifidelity Topology Design

Abstract Topology optimization is one of the most flexible structural optimization methodologies. However, in exchange for its high level of design freedom, typical topology optimization cannot avoid multimodality, where multiple local optima exist. This study focuses on developing a gradient-free topology optimization framework to avoid being trapped in undesirable local optima. Its core is a data-driven multifidelity topology design (MFTD) method, in which the design candidates generated by solving low-fidelity topology optimization problems are updated through a deep generative model and high-fidelity evaluation. As its key component, the deep generative model compresses the original data into a low-dimensional manifold, i.e., the latent space, and randomly arranges new design candidates over the space. Although the original framework is gradient free, its randomness may lead to convergence variability and premature convergence. Inspired by a popular crossover operation of evolutionary algorithms (EAs), this study merges the data-driven MFTD framework and proposes a new crossover operation called latent crossover. We apply the proposed method to a maximum stress minimization problem in 2D structural mechanics. The results demonstrate that the latent crossover improves convergence stability compared to the original data-driven MFTD method. Furthermore, the optimized designs exhibit performance comparable to or better than that in conventional gradient-based topology optimization using the P-norm measure.

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.

AI-assisted selection of mating pairs through simulation-based optimized progeny allocation strategies in plant breeding

Emerging technologies such as genomic selection have been applied to modern plant and animal breeding to increase the speed and efficiency of variety release. However, breeding requires decisions regarding parent selection and mating pairs, which significantly impact the ultimate genetic gain of a breeding scheme. The selection of appropriate parents and mating pairs to increase genetic gain while maintaining genetic diversity is still an urgent need that breeders are facing. This study aimed to determine the best progeny allocation strategies by combining future-oriented simulations and numerical black-box optimization for an improved selection of parents and mating pairs. In this study, we focused on optimizing the allocation of progenies, and the breeding process was regarded as a black-box function whose input is a set of parameters related to the progeny allocation strategies and whose output is the ultimate genetic gain of breeding schemes. The allocation of progenies to each mating pair was parameterized according to a softmax function, whose input is a weighted sum of multiple features for the allocation, including expected genetic variance of progenies and selection criteria such as different types of breeding values, to balance genetic gains and genetic diversity optimally. The weighting parameters were then optimized by the black-box optimization algorithm called StoSOO via future-oriented breeding simulations. Simulation studies to evaluate the potential of our novel method revealed that the breeding strategy based on optimized weights attained almost 10% higher genetic gain than that with an equal allocation of progenies to all mating pairs within just four generations. Among the optimized strategies, those considering the expected genetic variance of progenies could maintain the genetic diversity throughout the breeding process, leading to a higher ultimate genetic gain than those without considering it. These results suggest that our novel method can significantly improve the speed and efficiency of variety development through optimized decisions regarding the selection of parents and mating pairs. In addition, by changing simulation settings, our future-oriented optimization framework for progeny allocation strategies can be easily implemented into general breeding schemes, contributing to accelerated plant and animal breeding with high efficiency.

Quantifying the efficacy of voltage protocols in characterising ion channel kinetics: A novel information-theoretic approach.

Voltage-clamp experiments are commonly utilised to characterise cellular ion channel kinetics. In these experiments, cells are stimulated using a known time-varying voltage, referred to as the voltage protocol, and the resulting cellular response, typically in the form of current, is measured. Parameters of models that describe ion channel kinetics are then estimated by solving an inverse problem which aims to minimise the discrepancy between the predicted response of the model and the actual measured cell response. In this paper, a novel framework to evaluate the information content of voltage-clamp protocols in relation to ion channel model parameters is presented. Additional quantitative information metrics that allow for comparisons among various voltage protocols are proposed. These metrics offer a foundation for future optimal design frameworks to devise novel, information-rich protocols. The efficacy of the proposed framework is evidenced through the analysis of seven voltage protocols from the literature. By comparing known numerical results for inverse problems using these protocols with the information-theoretic metrics, the proposed approach is validated. The essential steps of the framework are: (i) generate random samples of the parameters from chosen prior distributions; (ii) run the model to generate model output (current) for all samples; (iii) construct reduced-dimensional representations of the time-varying current output using proper orthogonal decomposition (POD); (iv) estimate information-theoretic metrics such as mutual information, entropy equivalent variance, and conditional mutual information using non-parametric methods; (v) interpret the metrics; for example, a higher mutual information between a parameter and the current output suggests the protocol yields greater information about that parameter, resulting in improved identifiability; and (vi) integrate the information-theoretic metrics into a single quantitative criterion, encapsulating the protocol's efficacy in estimating model parameters.

Multi-objective Optimization Framework for Trade-Off Among Pedestrian Delays and Vehicular Emissions at Signal-Controlled Intersections

AbstractTraffic congestion has several adverse effects on urban traffic networks. Increased travel times of vehicles, with the addition of excessive greenhouse emissions, can be listed as harmful effects. To address these issues, transportation engineers aim to reduce private car usage, reduce travel times through different control strategies, and mitigate harmful effects on urban networks. In this study, we introduce an innovative approach to optimizing traffic signal control settings. This methodology takes into account both pedestrian delays and vehicular emissions. Non-dominated sorting genetic algorithm-II and Multi-objective Artificial Bee Colony algorithms are adopted to solve the multi-objective optimization problem. The vehicular emissions are modeled through the MOVES3 emission model and integrated into the utilized microsimulation environment. Initially, the proposed framework is tested on a hypothetical test network, followed by a real-world case study. Results indicate a significant improvement in pedestrian delays and lower emissions.

Integrated optimisation of strategic planning and service operations for urban air mobility systems

With the rapid development of novel vehicle technologies, electric vertical take-off and landing (eVTOL) vehicles are becoming a new transport servicing model to achieve better urban air mobility (UAM) systems. The UAM systems can efficiently utilise low-altitude airspace resources, providing a solution to alleviate congestion in urban ground traffic. This paper delves into an integrated optimisation problem aimed at addressing decision-making processes related to the strategic planning and service operation of UAM systems, considering both demand uncertainty and spatial equity. The problem encompasses various decision components, including parking stand numbers at vertiports and vertistops, eVTOL fleet sizing as well as eVTOL fleet allocation and operations. Additionally, we introduce a spatial equity metric and establish a bi-objective optimisation model to balance the trade-off between service profitability and spatial equity considerations. We transform the bi-objective optimisation model to a tractable single-objective formulation using ε-constraint approach and linearisation technique. In this paper, we employ a scenario-based robust optimisation framework that incorporates the interval robust method to capture the demand uncertainty, enhancing resilience against uncertain factors. We evaluate the model performance using a small-scale example and further validate the proposed model through a real-world case study. Numerical analysis results demonstrate that the scenario-based robust optimisation framework can ensure the robustness of decision-making against the effect of uncertain conditions. Furthermore, numerical experiments reveal a trade-off between profitability and spatial equity, potentially requiring a partial sacrifice of profit to attain a desired equity level. Finally, we propose valuable policy recommendations to guide the decision-making processes of UAM service providers.

Machine Learning-Driven Design Optimization of Buckling-Induced Quasi-Zero Stiffness Metastructures for Low-Frequency Vibration Isolation.

Metastructures, artificial arrangements of micro/macrostructures, possess unique properties and are of significant interest in aerospace, stealth technology, and various other applications. Recent studies have focused on quasi-zero stiffness metastructures, providing an outstanding vibration isolation capability. However, existing methods are constrained to low preloads and lack the consideration of structural analysis, despite their intended use in practical structures. This study introduces metastructures with quasi-zero stiffness characteristics under high preloads by inducing local buckling. An optimization framework combining deep reinforcement learning and finite-element analysis is employed to derive an optimal model that considers both structural safety and quasi-zero stiffness characteristics. To validate the optimization results, quasi-zero stiffness metastructures are fabricated via 3D printing, and compression and vibration experiments are conducted. The fabricated metastructures exhibit quasi-zero stiffness characteristics under a high target preload along with outstanding vibration reduction performance, even in the low-frequency range.

LP-OPTIMA: A Framework for Prescriptive Maintenance and Optimization of IoT Resources for Low-Power Embedded Systems.

Low-power embedded systems have been widely used in a variety of applications, allowing devices to efficiently collect and exchange data while minimizing energy consumption. However, the lack of extensive maintenance procedures designed specifically for low-power systems, coupled with constraints on anticipating faults and monitoring capacities, presents notable difficulties and intricacies in identifying failures and customized reaction mechanisms. The proposed approach seeks to address the gaps in current resource management frameworks and maintenance protocols for low-power embedded systems. Furthermore, this paper offers a trilateral framework that provides periodic prescriptions to stakeholders, a periodic control mechanism for automated actions and messages to prevent breakdowns, and a backup AI malfunction detection module to prevent the system from accessing any stress points. To evaluate the AI malfunction detection module approach, three novel autonomous embedded systems based on different ARM Cortex cores have been specifically designed and developed. Real-life results obtained from the testing of the proposed AI malfunction detection module in the developed embedded systems demonstrated outstanding performance, with metrics consistently exceeding 98%. This affirms the efficacy and reliability of the developed approach in enhancing the fault tolerance and maintenance capabilities of low-power embedded systems.

Optimal supervisory control of discrete event systems for cyclic tasks

In this paper, we investigate the problem of optimal supervisory control for cyclic tasks in the context of discrete-event systems (DES). We consider the completion of each single task as the visit of a marked state, and overall control objective is to complete tasks cyclically in the sense that marked states are visited infinitely often. Following the standard optimal supervisory control framework, two types of costs, disable cost and occurrence cost, are considered. However, instead of considering the standard accumulated total cost or the average cost per event, we consider the measure for the control performance using the average cost per task. We show that such an optimality measure is more suitable for tasks that need to be completed cyclically. Our goal is to design a live and non-blocking supervisor such that the average cost per task in the worst-case is minimized. To solve the problem, we propose a game-theoretical approach by converting the optimal control problem as a two-player graph game. Structural properties of the converted game are discussed. In particular, we show that this game can be solved by a set of mean payoff decision problems, for which effective algorithms exist. Our problem can be considered as a special instance of the general ratio-game in the literature. However, by exploring new structural property for this problem, we achieve superior computational efficiency when compared to the conventional solution designed for more general problem formulations. Illustrative examples are provided to demonstrate the proposed algorithm.

Model-based optimal control strategy for multizone VAV air-conditioning systems for neutralizing room pressure and minimizing fan energy consumption

Control strategies for variable air volume (VAV) air-conditioning systems play a pivotal role in ensuring indoor environmental quality and energy efficiency. However, conventional approaches, such as static pressure reset (SPR) control, focus on managing indoor air temperature without considering the room pressure, which can lead to unbalanced room pressure and undesirable air leakage. Moreover, with the application of prevalent building pressure control strategies, such as airflow tracking control, to multizone VAV systems, neutralization of the room pressure is difficult across multiple zones in VAV systems. Therefore, this study introduces a model-based optimal control strategy for multizone VAV air-conditioning systems. The proposed strategy uses a multiobjective optimization framework to regulate fan frequencies and damper openings on both the supply and return sides. This holistic approach facilitates the simultaneous control of the indoor air temperature and room pressure while minimizing fan energy consumption. To assess the effectiveness of the proposed strategy, four control strategies were tested using a Python-based simulation testbed. The results demonstrate that the proposed strategy effectively maintains the indoor air temperature, neutralizes room pressure, and reduces fan energy consumption, thereby contributing to the overall efficiency of the VAV system. Moreover, the results highlight the limitations associated with combining airflow tracking control with SPR control for room pressure regulation in multizone VAV systems. This highlights the importance of adopting a model-based approach to address the complexities of concurrent room pressure and indoor air temperature control.

Optimization of splash lubrication in the gearbox considering heat transfer performance

Despite recognizing the importance of cooling within gear systems, there is a lack of clear design guidelines to minimize churning losses while enhancing heat dissipation performance. This study proposes a reliable approach to modeling using a steady-state approximation methodology based on heat-flow coupling for multiphysics around shrouded gears. Computational analyses reveal a distinctive multiphase flow induced by pressure gradients. The efficacy of the proposed methodology is empirically verified through its ability to characterize lubricant distribution and churning losses in splash lubrication systems. Furthermore, an innovative shroud optimization framework is introduced to regulate friction and collision between fluid particles and gear surfaces, enhancing splash lubrication performance. A genetic algorithm is employed within this framework to obtain shroud configurations for gears with a higher wall heat transfer coefficient (WHTC) and lower resistance torque. Implementing the optimal shroud design achieves a trade-off in terms of a notable 13.7% reduction in resistance torque alongside 11.6% and 4% increments in WHTC for the tooth root face and tooth face, respectively. This signifies enhanced reliability in multiphysics governing gearbox functionality, thereby providing valuable guidance for the design of shrouded gear systems.

Automatic lift path planning of prefabricated building components using semantic BIM, improved A* and GA

PurposePrefabricated building has been widely applied in the construction industry all over the world, which can significantly reduce labor consumption and improve construction efficiency compared with conventional approaches. During the construction of prefabricated buildings, the overall efficiency largely depends on the lifting sequence and path of each prefabricated component. To improve the efficiency and safety of the lifting process, this study proposes a framework for automatically optimizing the lifting path of prefabricated building components using building information modeling (BIM), improved 3D-A* and a physic-informed genetic algorithm (GA).Design/methodology/approachFirstly, the industry foundation class (IFC) schema for prefabricated buildings is established to enrich the semantic information of BIM. After extracting corresponding component attributes from BIM, the models of typical prefabricated components and their slings are simplified. Further, the slings and elements’ rotations are considered to build a safety bounding box. Secondly, an efficient 3D-A* is proposed for element path planning by integrating both safety factors and variable step size. Finally, an efficient GA is designed to obtain the optimal lifting sequence that satisfies physical constraints.FindingsThe proposed optimization framework is validated in a physics engine with a pilot project, which enables better understanding. The results show that the framework can intuitively and automatically generate the optimal lifting path for each type of prefabricated building component. Compared with traditional algorithms, the improved path planning algorithm significantly reduces the number of nodes computed by 91.48%, resulting in a notable decrease in search time by 75.68%.Originality/valueIn this study, a prefabricated component path planning framework based on the improved A* algorithm and GA is proposed for the first time. In addition, this study proposes a safety-bounding box that considers the effects of torsion and slinging of components during lifting. The semantic information of IFC for component lifting is enriched by taking into account lifting data such as binding positions, lifting methods, lifting angles and lifting offsets.

Production planning optimization framework for integrated refinery, ethylene and aromatics industrial chains considering environmental performance

Nonlinear modeling for the processing units and enterprise-wide optimization are essential to improve the overall profitability of production planning in the petrochemical enterprise. This study proposed a novel data-driven modeling approach for the ethylene plant and enterprise-wide production planning optimization framework for integrated the refinery, the ethylene plant and the aromatics complex considering environmental impact. The data-driven model was developed for different feedstocks in the ethylene plant to better predict product yield. The integrated model increased potential profitability of the petrochemical enterprise through considering the material balances of the intermediate products between the refinery, the ethylene plant and the aromatics complex. The integrated model planned utilization of dry gas resources, which reduced the waste of C2 and C3 resources and increased the production of olefin products. The enterprise-wide multi-period production planning model was constructed to optimize the planning operation of the refinery, the ethylene plant and the aromatics complex simultaneously, which was solved with the commercial optimization solver SBB. An industrial scenario was investigated to demonstrate the performance of the proposed model. Computational results for several cases indicated that integrated approach achieves a significant improvement in overall profit and provide valuable guidance for emission reduction.