Optimization Perspective Research Articles

Sustainable supply chain operations driven by ‘remanufacturing + product sharing’: operation mode selection and decision optimisation

Consumers’ attention to sustainable production and consumption is promoting innovation in sustainable supply chain operations. We build an extended sustainable supply chain with remanufacturing (and/or) product sharing, and consider three operation modes: Sustainable supply chain operation with remanufacturing but No product Sharing mode (SNS), Sustainable supply chain operation with product sharing but No Remanufacturing mode (SNR), and Sustainable supply chain operation with both product Sharing and Remanufacturing mode (SSR). How does sustainable consumption (i.e. product sharing) affect sustainable production decisions and how to choose the optimal operation mode? Our findings: The SSR mode positively reinforces the sustainability-oriented market segmentation and can be globally optimal with an integrated value-added effect of ‘remanufacturing + product sharing’. From the perspective of total benefit optimisation, when the commission rate of a sharing platform and the proportion of service consumers are higher (lower), the transformation from the SNS mode to the SSR mode (from the SNR mode to the SSR mode) can be the best operational strategy. Maximising total benefits may have a compression effect on product sharing, which can be effectively adjusted by improving consumers’ perceived value of remanufactured products. This study provides guidance for selecting combination strategies for sustainable supply chains in the context of carbon emission reduction.

Spatio-temporal optimization of construction elevator planning in high-rise building projects

Appropriate construction elevator planning is beneficial for successfully completing high-rise building projects. Construction elevator planning typically remains unchanged throughout a high-rise building project or seldom involves the designation of service days, which causes such planning not to respond effectively to variable transportation demands and adversely affects the possibility of further optimization. Another limitation is that the installation and dismantlement dates of construction elevators, which are highly important when multiple construction elevators are used, cannot be explicitly determined in construction elevator planning. To solve these weaknesses, this paper proposes an approach to assign construction elevator cabs to serve designated days and floors based on actual transportation demands from the perspective of spatio-temporal optimization with mixed-integer programming. The experimental case study results show that the presented planning can significantly reduce the total cost when compared to other planning and can provide evidence and explanation for determining the installation and dismantlement dates of construction elevators.

Research on the impact of digital transformation on the production efficiency of manufacturing enterprises: Institution-based analysis of the threshold effect

Digital transformation is the core strategic orientation in the digital economy era and is evolving into a pivotal competitive advantage among countries, industries and enterprises. Based on the analysis of its mechanism, this paper focuses on the data of A-share listed manufacturing enterprises in China from 2011 to 2021; measures and analyses the variables relating to the impact of digital transformation on the production efficiency of manufacturing enterprises; and conducts empirical tests. The research shows that digital transformation plays a significant role in promoting the production efficiency of manufacturing enterprises, and the promotion effect is more obvious for high-tech enterprises and non-state-owned enterprises than for non-high-tech enterprises and state-owned enterprises; digital transformation has a greater promotional effect on the production efficiency of manufacturing enterprises with formal institutions or informal institutions switching from low value intervals to high value intervals. Accordingly, this paper proposes accelerating the digital transformation process of manufacturing enterprises and adopting targeted policies in accordance with the principle of resource endowment. Moreover, from the perspective of institutional environment optimization in digital transformation, this paper proposes building a proper formal institutional environment and strengthening the construction of informal institutions. Our study provides new ideas for constructing enterprise digital transformation indicators. We also offer new insights into the different institutional levels of the effect of digital transformation.

Thermodynamic cycle analysis of the fuel precooled multi-mode turbine engine mode transition process: Why? When? How?

The fluctuation of engine thrust during the mode transition process is a critical concern that poses a significant risk to aircraft safety. In order to address this issue, a thermodynamic model for mode transition in a dual fuel precooled multi-mode turbine engine is established, and three key issues in the mode transition process are comprehensively examined from the perspectives of steady-state analysis and dynamic process optimization: Why? When? How? Firstly, the basis for mode transition under high Mach number conditions is proposed for the first time from exergy efficiency. The engine exergy efficiency and combustion exergy efficiency in the turbofan mode decrease as the Mach number increases, while the turbojet mode exhibits superior combustion exergy efficiency. To achieve efficient operation, it is imperative to switch from the turbofan mode to the turbojet mode. Subsequently, a novel approach is proposed for determining the mode transition range based on bidirectional cyclic correction, which effectively addresses the limitation of large thrust fluctuations at the mode transition point determined by the conventional maximum state thrust continuous method. The mode transition envelope that can achieve a smooth transition between thrust and airflow rate is determined. Finally, a mode transition method is newly put forward by integrating directional adjustment and offline optimization. Throughout the entire mode transition envelope, the maximum thrust fluctuation during the process remains below 1 %, while the maximum airflow rate change stays within 3 %, which exhibits excellent efficacy in achieving smooth transitions.

Parameter estimation in single-phase transformers via the generalized normal distribution optimizer while considering voltage and current measurements

This research addresses, from a perspective of metaheuristic optimization, the problem regarding parametric estimation in single-phase transformers while considering voltage and current measures at the terminals of the transformer and weighing linear loads. Transformer parametric estimation is modeled as a nonlinear problem in order to minimize the mean square error between the calculated voltage and current variables and the measurements taken. The nonlinearities are associated with Kirchhoff's first and second laws applied to the equivalent electrical circuit of the single-phase transformer. The nonlinear optimization problem is solved by applying a metaheuristic optimization algorithm known as the generalized normal distribution optimizer (GNDO), which uses evolution rules that allow exploring and exploiting the solution space via the classical probability function based on normal distributions. Numerical results in three test transformers of 20, 45, and 112.5 kVA demonstrate the effectiveness and robustness of the proposed GNDO approach when compared to other optimizers reported in the literature, such as the crow search algorithm, the coyote optimization algorithm, and the exact solution of the nonlinear optimization model using the fmincon solver of the MATLAB software. All numerical simulations confirm the potential of the GNDO approach to deal with complex optimization problems in engineering and science with promising results and low computational effort.

Research on the Optimization Strategy of the Content of Civic and Political Education and the Enhancement of the Effectiveness of Practical Educational Training for the Digital Teaching Environment

Abstract This paper focuses on the optimization of the content of ideological and political education from the perspective of optimization and establishes an evaluation model based on Lib-SVM for the effect of optimization. The study proposes optimization strategies for ideological and political education in colleges and universities based on VR technology at three levels. The fundamental task of establishing moral education must be adhered to, while clarifying specific tools for optimizing the content of ideological and political education with VR technology. The value leadership of mainstream consciousness is adhered to, and the framework for optimizing the content of ideological and political education through VR technology is improved. Then, on the basis of the implementation of the optimization strategy, an assessment model based on Lib-SVM was established to analyze the ten statistical data of students’ interpersonal communication ability, organizational ability, adaptability, hands-on ability, teamwork ability, innovation ability, expression ability, enhancement of the sense of responsibility, cognitive ability of the society, and experiencing the hardship of life as the source of input data, and to analyze the correlation between these ten data and the level of enhancement of the effectiveness of practical educating people. Analyzing the correlation between these ten data and the improvement level of practice education effectiveness, the length of students’ social practice has increased, and the average length of time spent on volunteering has reached 236h, and there are different degrees of improvement in the ten competencies. The evaluation of Lib-SVM of the strategy of optimizing the content of Civic and Political Education on the effectiveness of practical education was completed, which proved that the strategy carried out a superior enhancement of the effectiveness of practical education.

A Portrayal of Sliding Mode Control Through Adaptive Neuro Fuzzy Inference System With Optimization Perspective

A Portrayal of Sliding Mode Control Through Adaptive Neuro Fuzzy Inference System With Optimization Perspective

Generalized Parametric Contrastive Learning.

In this paper, we propose the Generalized Parametric Contrastive Learning (GPaCo/PaCo) which works well on both imbalanced and balanced data. Based on theoretical analysis, we observe supervised contrastive loss tends to bias on high-frequency classes and thus increases the difficulty of imbalanced learning. We introduce a set of parametric class-wise learnable centers to rebalance from an optimization perspective. Further, we analyze our GPaCo/PaCo loss under a balanced setting. Our analysis demonstrates that GPaCo/PaCo can adaptively enhance the intensity of pushing samples of the same class close as more samples are pulled together with their corresponding centers and benefit hard example learning. Experiments on long-tailed benchmarks manifest the new state-of-the-art for long-tailed recognition. On full ImageNet, models from CNNs to vision transformers trained with GPaCo loss show better generalization performance and stronger robustness compared with MAE models. Moreover, GPaCo can be applied to semantic segmentation task and obvious improvements are observed on 4 most popular benchmarks. Our code is available at https://github.com/dvlab-research/Parametric-Contrastive-Learning.

On phase recovery and preserving early reflections for deep-learning speech dereverberation.

In indoor environments, reverberation often distorts clean speech. Although deep learning-based speech dereverberation approaches have shown much better performance than traditional ones, the inferior speech quality of the dereverberated speech caused by magnitude distortion and limited phase recovery is still a serious problem for practical applications. This paper improves the performance of deep learning-based speech dereverberation from the perspectives of both network design and mapping target optimization. Specifically, on the one hand, a bifurcated-and-fusion network and its guidance loss functions were designed to help reduce the magnitude distortion while enhancing the phase recovery. On the other hand, the time boundary between the early and late reflections in the mapped speech was investigated, so as to make a balance between the reverberation tailing effect and the difficulty of magnitude/phase recovery. Mathematical derivations were provided to show the rationality of the specially designed loss functions. Geometric illustrations were given to explain the importance of preserving early reflections in reducing the difficulty of phase recovery. Ablation study results confirmed the validity of the proposed network topology and the importance of preserving 20 ms early reflections in the mapped speech. Objective and subjective test results showed that the proposed system outperformed other baselines in the speech dereverberation task.

Adaptive HEVC Video Steganography With High Performance Based on Attention-Net and PU Partition Modes

With the increasing popularity of digital video, video steganography has become a hot research topic in the field of covert communication and privacy protection. The existing prediction unit (PU) based video steganography often tends to result in large bit rate increase, which is also easily noticeable to the steganography analyst. To solve this problem, an adaptive steganography for HEVC video based on attention-net and PU partition modes is proposed. First, the distortion of modified PUs is analyzed from the perspective of rate distortion optimization at the group of pictures (GOP) level, and we find that modifying PU will lead to distortion accumulation and abnormal bitrate increase. Therefore, an adaptive distortion function based on the improved rate distortion cost is designed, and the embedding distortion is minimized by using Syndrome-Trellis Code (STC) steganography coding. Meanwhile, a super-resolution convolutional neural network with non-local sparse attention-net filter is proposed to replace the in-loop filter in HEVC to reconstruct the reference frame, thereby reducing the bitrate cost and improving the visual quality of stego-video. Experimental results show that the proposed algorithm can achieve superior perceptual quality and bitrate performance comparing with the sate-of-the-art works.

Organic electrodes based on redox-active covalent organic frameworks for lithium batteries.

Electroactive organic materials have received much attention as alternative electrodes for metal-ion batteries due to their high theoretical capacity, resource availability, and environmental friendliness. In particular, redox-active covalent organic frameworks (COFs) have recently emerged as promising electrodes due to their tunable electrochemical properties, insolubility in electrolytes, and structural versatility. In this Highlight, we review some recent strategies to improve the energy density and power density of COF electrodes for lithium batteries from the perspective of molecular design and electrode optimisation. Some other aspects such as stability and scalability are also discussed. Finally, the main challenges to improve their performance and future prospects for COF-based organic batteries are highlighted.

Joint or not: unpacking the decision-making mechanism of pandemic control between two nations – from the perspective of cost optimization

Joint control has been proposed to alleviate the negative spillover of the pandemic. The authority faces a decision dilemma on whether to undertake joint control, which is overlooked by the scientific community. This paper, aiming to clarify the decision-making mechanism under cost optimization, draws on the optimization theory to find the optimal strategies and start time under independent and joint scenarios with an improved infectious disease model. Besides, a simulation analysis, based on the survey in Ruili – a border area between Myanmar and China, is employed. The crucial findings highlight that (1) The control measures contribute to reducing the peak number or peak time of confirmed patients under optimal decision. (2) As the delay of start time, the epidemic control for both countries undergoes from validity to failure. (3) More cost-savings come with earlier joint control but with uneven distribution. Joint strategy mitigates the cost for the one with severe epidemics, but the effect is insignificant for the other. This study, concentrating on cost optimization, provides a novel insight into pandemic control. The results enrich the decision analysis in the context of pandemic joint governance while providing inter-regional control support for the decision-maker to tackle various epidemics.

Research on urban slow traffic planning from the perspective of system optimization

Research on urban slow traffic planning from the perspective of system optimization

Organ registration from partial surface data in augmented surgery from an optimal control perspective

We address the problem of organ registration in augmented surgery, where the deformation of the patient’s organ is reconstructed in real-time from a partial observation of its surface. Physics-based registration methods rely on adding artificial forces to drive the registration, which may result in implausible displacement fields. In this paper, we look at this inverse problem through the lens of optimal control, in an attempt to reconstruct a physically consistent surface load. The resulting optimization problem features an elastic model, a least-squares data attachment term based on orthogonal projections, and an admissible set of surface loads defined prior to reconstruction in the mechanical model. After a discussion about the existence of solutions, we analyse the necessary optimality conditions and use them to derive a suitable optimization algorithm. We implement an adjoint method and we test our approach on multiple examples, including the so-called Sparse Data Challenge . We obtain very promising results, that illustrate the feasibility of our approach with linear and nonlinear models.

Анализ состояния гармонизации дисциплин направления 09.03.01 "Информатика и вычислительная техника"

The article studies the issue of harmonious state of disciplines involved in the educational process of the direction 09.03.01 "Informatics and Computer Science" with the profile "Automated Systems of Information Processing and Control". The information about the methods of key information processing from files with working programs of disciplines is given. The result of data processing is demonstrated. Further perspectives of optimization of students' learning process by building a detailed model of educational program, which will allow to estimate the current level of harmonization with greater accuracy, as well as to supplement the initial model with new useful information, are described.

A Unified Model for Large-Scale Inexact Fixed-Point Iteration: A Stochastic Optimization Perspective

A Unified Model for Large-Scale Inexact Fixed-Point Iteration: A Stochastic Optimization Perspective

Efficient energy transport in constant-voltage triboelectric nanogenerator-based power units

The energy transport in TENG-based power units is investigated and a real-time intelligent energy optimization system (RIEOS) is built to achieve efficient energy transport from a system integration optimization perspective.

Distributed Optimal Consensus Control of Constrained Multi-Agent Systems: A Non-Separable Optimization Perspective

Distributed Optimal Consensus Control of Constrained Multi-Agent Systems: A Non-Separable Optimization Perspective

Spillover effects of volatility between the Chinese stock market and selected emerging economies in the middle east: A conditional correlation analysis with portfolio optimization perspective

In recent years, the rapid transmission of information and interconnectedness of global financial markets have amplified the convergence and influence among them. Consequently, the occurrence of spillover effects in one market can significantly impact other markets. Accurately identifying and understanding these spillover effects is crucial for effectively managing and controlling market fluctuations. This research aims to measure and analyze the spillover effects between China's stock market and selected emerging economies in the Middle East, with a focus on exploring diversification opportunities. The analysis encompasses three distinct time periods, including the overall period from May 1, 2005, to May 31, 2023. The sub-periods consist of the first sub-period from May 1, 2005, to October 31, 2009, and the second sub-period from December 1, 2010, to May 31, 2023. Multivariate Generalized Heterogeneous Autoregression (MGARCH) is employed in this study to examine the spillover effects between China's economy and the emerging economies under consideration. The Granger causality analysis reveals a unidirectional causality running from the Chinese stock market to Jordan, as well as from the UAE to China throughout the entire observation period. However, no spillover effects are found between China and Saudi Arabia in either direction during any of the periods. Notably, a two-way causality is detected between the Chinese and UAE markets in the second sub-period. Furthermore, MGARCH results indicate no spillover effects from China to the emerging economies during the overall period, first sub-period, or second sub-period. The findings of this research offer valuable insights for investment portfolio managers in the Chinese economy, who may consider the examined emerging economies as potential destinations for risk diversification.

Perception-Distortion Balanced Super-Resolution: A Multi-Objective Optimization Perspective.

High perceptual quality and low distortion degree are two important goals in image restoration tasks such as super-resolution (SR). Most of the existing SR methods aim to achieve these goals by minimizing the corresponding yet conflicting losses, such as the l1 loss and the adversarial loss. Unfortunately, the commonly used gradient-based optimizers, such as Adam, are hard to balance these objectives due to the opposite gradient decent directions of the contradictory losses. In this paper, we formulate the perception-distortion trade-off in SR as a multi-objective optimization problem and develop a new optimizer by integrating the gradient-free evolutionary algorithm (EA) with gradient-based Adam, where EA and Adam focus on the divergence and convergence of the optimization directions respectively. As a result, a population of optimal models with different perception-distortion preferences is obtained. We then design a fusion network to merge these models into a single stronger one for an effective perception-distortion trade-off. Experiments demonstrate that with the same backbone network, the perception-distortion balanced SR model trained by our method can achieve better perceptual quality than its competitors while attaining better reconstruction fidelity. Codes and models can be found at https://github.com/csslc/EA-Adam.