Optimization Model For Design Research Articles

Analysis and design of optimal deep neural network model for image recognition using hybrid cuckoo search with self-adaptive particle swarm intelligence

Analysis and design of optimal deep neural network model for image recognition using hybrid cuckoo search with self-adaptive particle swarm intelligence

Optimal designs for nonlinear mixed-effects models using competitive swarm optimizer with mutated agents

Optimal designs for nonlinear mixed-effects models using competitive swarm optimizer with mutated agents

A BP Neural Network Product Design Optimization Model Based on Emotional Design and Sustainable Product Design

A BP Neural Network Product Design Optimization Model Based on Emotional Design and Sustainable Product Design

Construction and application of product optimisation design model driven by user requirements

User requirements serve as the primary reference content in product design. The effective capture of crucial user requirements, followed by the development of a product technical solution aligned with these requirements, stands as a pivotal approach to enhancing design efficiency. In order to explore the problem of generating and decision-making of product technical solutions in the case of complex user demands, this study constructs a user requirements driven product optimization design model, which is used to complete the generation and decision-making of product design solutions in a more reasonable way. The model unfolds across three key stages: Firstly, a user requirements importance ranking system is crafted leveraging Kano Model and Pairwise Analysis. Next, employing the Functional Analysis System Techniques (FAST) theory and the Quality Function Deployment (QFD) theory, user requirements undergo transformation into technical solutions. Finally, these technical solutions are amalgamated into diverse technical combinations, with decisions facilitated by a game theory model to yield the optimal overall design solution. The new optimal design model reduces the influence of subjectivity and ambiguity in the process of user requirements analysis, increases the reliability of the transformation of user requirements into technical solutions, and improves the efficiency of the generation and decision-making of product design solutions under multiobjective situations. The model proposed in this study is exemplified through the development of a medicated bath water heater. Results indicate that the technical solution derived from the model surpasses similar products in terms of user satisfaction, thereby validating its feasibility.

Exploring the horizon of AI development: Navigating constraints of chips and power in the technological landscape

With the proliferation of AI technology, machine learning has emerged as a cornerstone of AI systems, facilitating pattern recognition and decision-making through robust data analysis. This encompasses various learning paradigms such as supervised, unsupervised, and reinforcement learning, all of which are indispensable for the advancement of artificial intelligence. Nevertheless, the development of AI necessitates substantial computational resources, with specialized chips serving as the linchpin, particularly in demanding tasks such as deep learning. Dedicated chip development, exemplified by GPUs and TPUs, plays a pivotal role in enhancing the performance of AI systems, notwithstanding challenges related to costs and market monopolies. Moreover, AI systems require significant power support, especially during the training of large-scale models. To address these challenges, this paper reviews the existing literature on modeling techniques aimed at enhancing the efficiency of machine learning and reducing energy consumption. This review encompasses optimal algorithm design, hardware optimization, and spatial modeling. Through the implementation of these approaches, the challenges posed by resource constraints in machine learning scenarios can be effectively mitigated, thereby fostering the continued development and application of AI technology.

A novel semi-analytical pseudo-steady state finite-conductivity model for optimum horizontal-well completion design

This paper introduces a new technique to determine the optimum horizontal-well completion design in terms of the optimum horizontal-well length, the minimum wellbore/reservoir pressure drop, and the maximum wellbore/reservoir productivity index (PI) at pseudo-steady state (PSS). Frictional-wellbore pressure losses (FWPL) are accounted for based on a new general semi-analytical PSS finite-conductivity model (FCM) for a horizontal well under a rectangular bounded reservoir producing at a constant rate. The PSS FCM is coupling a PSS infinite-conductivity model (ICM) with a frictional-wellbore model (FWM) by using their correct-discretization resolution of the wellbore (CDRW), which has never been achieved before. The new FWM is developed in a general form to be coupled easily with any general ICM and can be applied at any time of the well production life. Two field examples are presented to show the effect of different reservoir, well, and fluid properties on horizontal-well performance.The new technique is based on the optimization of dimensionless well length (LD) between the two systems (i.e., ICM and FWM) of the FCM because LD is directly proportional to the PI of the first system and inversely proportional to the PI of the second system. Therefore, there is always a minimum point on the wellbore/reservoir pressure drop curve corresponds to a maximum point on the wellbore/reservoir PI curve.Results show that: 1. LD has the key role in the wellbore/reservoir coupling model.2. FWPL is crucial if both well production rate (qw) and horizontal permeability (kh) are high, where a notable drop in FCM PSS PI occurs, recommending shortening to the horizontal-well length.3. FWPL is insignificant if qw is low and oil viscosity (μo) is high, where no decrease in FCM PSS PI occurs, recommending lengthening to the horizontal-well length.4. Wellbore segment length (ΔL) of the FWM must be small, ΔL = 0.25 ft in this study. If it is not enough small, a critical overestimation may be occurred in the optimum horizontal-well length and the maximum PSS PI of the horizontal-well completion design.

A multi-objective optimization design method for underwater composite sandwich structures with high acoustic transmission and low vibration performance

ABSTRACT To address the issue of reduced structural integrity and optimality resulting from the conventional serial design of sonar dome structures, this paper establishes a multi-objective optimal design model for composite sandwich structures, utilising the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The model's stability and reliability are subsequently validated. Upon this model, a structural optimization design technique is postulated for composite sandwich structures within the realm of Fluid-Structure Interaction (FSI) multi-physics coupling. This approach aims to optimize the lightweight, high-rigidity composite sandwich structure, enhancing its acoustic transmission and minimizing vibration performance under turbulent pressure pulsations. The suggested method notably enhances the efficiency of structural design while ensuring precision. The proposed method effectively addresses the deficiency of serially designed sonar domes by neglecting the synergistic effects among multiple disciplines. In summary, this approach offers technical support and new research perspectives for the design of sonar domes under the coupling of multiple physical fields.

Optimal Model and Algorithm Design for the Multi-Equipment Resource Collaborative Scheduling of Automated Terminals Considering the Mixing Process

Optimal Model and Algorithm Design for the Multi-Equipment Resource Collaborative Scheduling of Automated Terminals Considering the Mixing Process

MULTI-OBJECTIVE GREEN DESIGN MODEL BASED ON COSTS, CO2 EMISSIONS AND SERVICEABILITY FOR HIGH-RISE BUILDINGS WITH A MEGA-STRUCTURE SYSTEM

In light of growing environmental concerns, the reduction of CO2 emissions is increasingly vital. Particularly in the construction industry, a major contributor to global carbon emissions, addressing this issue is critical for environmental sustainability and mitigating the accelerating impacts of climate change. This study proposes the Optimal Green Design Model for Mega Structures (OGDMM) to optimise CO2 emissions, cost-effectiveness, and serviceability in highrise buildings with mega structures. The OGDMM examines the impact of each material and structural design of main members on these three critical aspects. Analytical results for high-rise buildings (120–200 m, slenderness ratio: 2.0–8.0) demonstrate that OGDMM can reduce CO2 emissions and costs by an average of 4.67% and 3.97%, respectively, without compromising serviceability. To ensure comprehensive evaluation, this study introduces five new evaluation indicators encompassing environmental, economic, and serviceability performances of high-rise buildings. Based on these criteria, optimised structural designs for high-rise buildings are classified into four categories according to slenderness ratio, leading to the formulation of corresponding design guidelines. The model’s applicability is further validated through its application to a 270-m-tall high-rise building in Korea, showing reductions in CO2 emissions and costs by 8.99% and 18.50%, respectively, while maintaining structural serviceability.

Implications of Context Effects in Consumer Utility Models for Optimal Product Design and Differentiation

Abstract Consumer choice models used in optimal product design typically ignore potential context effects by assuming the utility of each product is independent of the attributes of other products in the choice set. We characterize implications of context effects for profit-optimal designs by deriving the first-order conditions of the design problem under alternative utility formulations, and we propose a utility function that incorporates context effects and has well-defined optimal design solutions for all products in the choice set. We then conduct a discrete choice survey experiment of automobile options and find statistically significant context-effect parameters and superior out-of-sample prediction when context-effect parameters are used in both logit and mixed logit models. These results suggest that context effects can be important in engineering design contexts and have the potential to affect optimal design differentiation.

A novel decoupled approach combining invertible cross-entropy method with Gaussian process modeling for reliability-based design and topology optimization

Design optimization considering the presence of uncertainties in parameters poses an extremely challenging problem. The source of difficulties comes with reliability-based formulations, where addressing the probabilistic problem exhausts the large computing efforts for failure estimations of the structure violating limit-state functions (LSFs). This paper proposes a novel decoupled approach for effectively solving reliability-based design optimization (RBDO) problems, namely an invertible cross-entropy (iCE) method advantageously combined with a Gaussian process regression (GPR) model, termed as iCE-GPR. The GPR model is applied to approximate the spectrum of LSFs under random parameters. Furthermore, to enhance the accurate prediction of the system failure probability, an active learning process is applied to systematically refine the GPR model by adding new learning points in the region with the largest uncertainty and high-reliability sensitivity through the maximization of an expected feasibility function (EFF). Based on the updated GPR model, the failure probability is estimated by a cost-effective cross-entropy (CE) method without any calls to the actual performance function. To perform the decoupling optimization process with the reliability analysis, the novel iCE, based on the CE method, is developed to update the most probable point (MPP) assigned for the next deterministic optimization process in determining the new optimal design. The method iteratively performs the deterministic optimization process based on the MPP underpinning LSFs sequentially updated by the active learning process. The proposed iCE-GPR method fast-converges the optimal design and significantly alleviates computational burdens associated with reliability analyses. The proposed method is also applied to solve a reliability-based topology optimization (RBTO) problem. Four numerical examples for both the RBDO and RBTO problems are provided to illustrate efficiency and robustness of the proposed iCE-GPR method.

A Parameter Optimization Design and Numerical Calculation Method for Circular Heliostat Field

Solar energy is a low-carbon, eco-friendly, clean energy source that is easily converted into heat and electricity, making the study of solar thermal power generation technology of great significance and with broad application prospects. To improve the performance of solar power plants, it is necessary to reasonably arrange the light reflectors. Both denser heliostats are needed to ensure enough light can be concentrated into the solar collector, thereby improving the power of the power station; at the same time, waste of material must be prevented, striving to increase output thermal power per unit mirror area. This paper is based on the background of the 2023 CUMCM Problem A. Firstly, it analyzes parameters such as the optical efficiency of the original heliostat field, the annual average output power, and the annual average thermal power output per unit mirror area. Secondly, based on the data provided in the appendix, it analyzes the distribution pattern of the heliostat field and establishes an optimization design model for the heliostat field. Next, the optimization design model of the heliostat field is solved using discrete numerical calculation methods. Finally, according to the results of the model solution, the placement positions of the heliostats, the size of the mirrors, and the installation height are adjusted to improve the annual average thermal power output per unit mirror area, thereby achieving an optimized design of the heliostat field.

Single-Loop Sampling for Estimating Failure-Probability Upper-Bound Function

Under random-interval mixed uncertainties of structures, failure-probability upper-bound function (FPUBF), which varies with the distribution parameters of random inputs, can not only provide the influence of distribution parameters on the failure-probability upper bound (FPUB), but also contribute to decoupling a reliability-based design optimization model. Although FPUBF can be estimated by repeatedly evaluating FPUBs at different distribution parameter realizations, it suffers from unaffordable computational cost resulting from this double-loop framework. To address this issue, this paper proposes a single-loop sampling strategy (SL) to estimate FPUBF at arbitrary realizations in the interested distribution parameter region. Instead of the huge computational cost of a double-loop framework, the SL estimates the entire FPUBF only by one simulation analysis. Moreover, importance sampling (IS) variance reduction technique is introduced, and a single-loop IS probability density function (PDF), or SL-IS-PDF, is constructed to more efficiently estimate FPUBF by reducing the required size of the candidate sample pool. For approximating the optimal SL-IS-PDF and identifying the states of candidate samples efficiently, the double-loop adaptive Kriging model of performance function is introduced to further reduce the number of performance function evaluations. A numerical example and two composite structure examples are employed to verify the accuracy, efficiency, and feasibility of the proposed methods.

A resilience optimization design model considering surcharge for large diameter shield tunnel

Large-diameter shield tunnels face challenges owing to the complex strata and surcharges. The robustness of ground disturbance during excavation and resilience during operation in the design model must be considered. This involves establishing a damage evolution law for existing shield tunnels after surcharging and proposing a resilience design method for the entire cycle. In this study, the design process was initiated by utilizing existing borehole survey information and incorporating stratum uncertainty. Subsequently, a ground surcharge was applied to evaluate the changes in the force and deformation at different design points after surcharging. A resilience assessment was conducted in accordance with the specified criteria, and the results indicated that a majority of the design points exhibited robustness within acceptable limits after the surcharge. The original excavation design demonstrated satisfactory initial performance in terms of robustness. However, the resilience after surcharging was suboptimal, exhibiting a value of only 0.65. To enhance the resilience, the design must modify the reinforcing rate to achieve a higher resilience state of 0.8. By considering both the robustness of the design and its resilience after surcharging, modifications can be made to align the design with real operational conditions, which may help improve the overall resilience.

Optimizing injection molding parameters to minimize and prediction potential for flashing defects

The injection molding process is a manufacturing process that can produce products in a short time in large quantities, in the injection molding process the factor of setting process parameters plays a significant role in product quality, so it requires special treatment. The purpose of this study is to find the optimal parameters in the injection moulding process of yogurth container lid with polypropylene material, so that the process can reduce the incidence of flashing defects that result in the emergence of initial waste in the industrial environment. The method used in this research is to create a Response Surface Methodology Box-Behnken Design (RSM - BBD) optimization model and an Artificial Neural Network (ANN) model approach in analyzing optimal parameters and predicting the appearance of flashing defects in a designed cycle. The results obtained from this research are the optimal parameters from the RSM and ANN model recommendations, namely the clamping force setting of 70 tons, holding time 0.1 seconds, and holding pressure. The ANN model provides the highest level of prediction accuracy with an R2 value of 100% and a prediction error rate of 7.9689E-09. In comparison, the RSM model obtains a prediction accuracy level with an R2 of 71% with an error rate of 0.24315.

Optimization design for the installation position of hold bracket in railway bogie system by uniform design method

ABSTRACT The sensor hold bracket design is an essential element for running safety in the railway vehicle bogie braking system. The installation position of the hold bracket in the bogie system affects the brake sensing performance. This paper aims to determine the optimal installation position of the hold bracket. Three installation angles and one geometry dimension of a holding bracket are selected for the control factors. The uniform design of the experiment method creates a set of experiments. The surrogate models from input and output data are obtained and compared by applying Kriging interpolation and radial basis functions. The final optimal design models of the holding bracket for two surrogate models are determined using the genetic algorithm. The results show that the optimized parameters could substantially improve the installation angle error of the pressure switch from 4° to 0.04°, which aligns with expectations. Finally, to confirm the effect of the control parameters and their reliability and validity, the impact of the pressure switch is expected when the optimal hold bracket model is installed in the railway vehicle bogie system. As a result, the optimal design procedure can improve and reduce the risk of sensor failure during train operation.

Optimal design and experiments of a novel bobbin thread-hooking mechanism with RRSC (revolute–revolute–spherical–cylindrical) spatial four-bar linkage

Abstract. To solve the problem of low supply of bobbin thread and frequent bobbin changes existing in current embroidery machines, a novel bobbin thread-hooking mechanism with an RRSC (revolute–revolute–spherical–cylindrical) spatial four-bar linkage is proposed which can achieve a large number and continuous supply of bobbin thread. Based on the analysis of the chain stitch formation principle, the design requirements for motion trajectory and posture of the hooking mechanism are proposed. The methods of direction cosine matrix and mechanism identity condition are applied in kinematics modeling and analysis of the RRSC mechanism. The optimization design model and computer-aided software are developed, then the human–computer interaction optimization method is used to obtain optimal solutions to meet working requirements. The physical prototype is machined and a test bench is built. The virtual simulation and prototype high-speed camera kinematics tests are conducted. The results verify the correctness of mechanism design and show good application feasibility of the mechanism.

Innovative supply chain network design with two-step authentication and environmentally-friendly blockchain technology

AbstractBlockchain Technology (BT) has the potential to revolutionize supply chain management by providing transparency, but it also poses significant environmental and security challenges. BT consumes energy and emits carbon gases, affecting its adoption in Supply Chains (SCs). The substantial energy demand of blockchain networks contributes to carbon emissions and sustainability risks. Moreover, for secure and reliable transactions, mutual authentication needs to be established to address security concerns raised by SC managers. This paper proposes a tri-objective optimization model for the simultaneous design of the SC-BT network, considering a two-step authentication process. The model considers transparency caused by BT members, emissions of BT, and costs related to BT and SC design. It also takes into account uncertainty conditions for participating BT members in the SC and the range of transparency, cost, and emission targets. To solve the model, a Branch and Efficiency (B&E) algorithm equipped with BT-related criteria is developed. The algorithm is implemented in a three-level SC and produces cost-effective and environmentally friendly outcomes. However, the adoption of BT in the SC can be costly and harmful to the environment under uncertain conditions. It is worth mentioning that implementing the proposed algorithm from our article in a three-level SC case study can result in a significant cost reduction of over 16% and an emission reduction of over 13%. The iterative nature of this algorithm plays a vital role in achieving these positive outcomes.

A Decision Support Model for Barriers and Optimal Strategy Design in Sustainable Humanitarian Supply Chain Management

Sustainable humanitarian supply chain (SHSC) management enables effective and efficient responses to natural and human-made disasters. Existing literature falls short of offering decision support (DS) models to address the barriers and strategies to designing SHSC. To this end, this study develops a DS model that identifies and prioritizes the barriers to SHSC and determines optimal strategies for mitigating those barriers. This study adopted both qualitative and quantitative approaches. As part of the qualitative approach, a field study was applied using in-depth interviews to determine the barriers and corresponding strategies, while under the quantitative approach, a quality function deployment (QFD) integrated optimization technique was used to prioritize barriers and determine optimal strategies to mitigate the SHSC barriers. The study found that a lack of contingency planning, the prevalence of corruption and political interference, and a lack of social and environmental awareness are the most important barriers, while logistics outsourcing, supply chain (SC) performance management, and SC flexibility are the most essential strategies. We also found that our DS model is highly flexible and can be adapted under different scenarios, which makes the model applicable to different contexts. This study has a significant contribution to literature and practice. We developed a novel decision model that captured cost savings and leveraged both cost and time savings from interrelated strategies to determine the best optimal strategy while applying QFD-integrated optimization modeling. The paper’s findings will assist humanitarian SC managers in designing an effective, efficient, and sustainable humanitarian SC.

Simulation–optimization model for the structural design of cantilever retaining walls

Simulation–optimization model for the structural design of cantilever retaining walls