An Elite-Class Teaching-Learning-Based Optimization for Reentrant Hybrid Flow Shop Scheduling with Bottleneck Stage

Bottleneck stage and reentrance often exist in real-life manufacturing processes;however,the previous research rarely addresses these two processing conditions in a scheduling problem.In this study,a reentrant hybrid flow shop scheduling problem(RHFSP)with a bottleneck stage is considered,and an elite-class teaching-learning-based optimization(ETLBO)algorithm is proposed to minimize maximum completion time.To produce high-quality solutions,teachers are divided into formal ones and substitute ones,and multiple classes are formed.The teacher phase is composed of teacher competition and teacher teaching.The learner phase is replaced with a reinforcement search of the elite class.Adaptive adjustment on teachers and classes is established based on class quality,which is determined by the number of elite solutions in class.Numerous experimental results demonstrate the effectiveness of new strategies,and ETLBO has a significant advantage in solving the considered RHFSP.

Improved multi-scale inverse bottleneck residual network based on triplet parallel attention for apple leaf disease identification

Accurate diagnosis of apple leaf diseases is crucial for improving the quality of apple production and promoting the development of the apple industry. However, apple leaf diseases do not differ significantly from image texture and structural information. The difficulties in disease feature extraction in complex backgrounds slow the related research progress. To address the problems, this paper proposes an improved multi-scale inverse bottleneck residual network model based on a triplet parallel attention mechanism, which is built upon ResNet-50, while improving and combining the inception module and ResNext inverse bottleneck blocks, to recognize seven types of apple leaf(including six diseases of alternaria leaf spot, brown spot, grey spot, mosaic, rust, scab, and one healthy). First, the 3×3 convolutions in some of the residual modules are replaced by multi-scale residual convolutions, the convolution kernels of different sizes contained in each branch of the multi-scale convolution are applied to extract feature maps of different sizes, and the outputs of these branches are multi-scale fused by summing to enrich the output features of the images. Second, the global layer-wise dynamic coordinated inverse bottleneck structure is used to reduce the network feature loss. The inverse bottleneck structure makes the image information less lossy when transforming from different dimensional feature spaces. The fusion of multi-scale and layer-wise dynamic coordinated inverse bottlenecks makes the model effectively balances computational efficiency and feature representation capability, and more robust with a combination of horizontal and vertical features in the fine identification of apple leaf diseases. Finally, after each improved module, a triplet parallel attention module is integrated with cross-dimensional interactions among channels through rotations and residual transformations, which improves the parallel search efficiency of important features and the recognition rate of the network with relatively small computational costs while the dimensional dependencies are improved. To verify the validity of the model in this paper, we uniformly enhance apple leaf disease images screened from the public data sets of Plant Village, Baidu Flying Paddle, and the Internet. The final processed image count is 14,000. The ablation study, pre-processing comparison, and method comparison are conducted on the processed datasets. The experimental results demonstrate that the proposed method reaches 98.73% accuracy on the adopted datasets, which is 1.82% higher than the classical ResNet-50 model, and 0.29% better than the apple leaf disease datasets before preprocessing. It also achieves competitive results in apple leaf disease identification compared to some state-ofthe-art methods.

Efficient control of connected and automated vehicles on a two-lane highway with a moving bottleneck

This paper investigates the traffic flow of connected and automated vehicles(CAVs)inducing by a moving bottleneck on a two-lane highway.A heuristic rules-based algorithm(HRA)has been used to control the traffic flow upstream of the moving bottleneck.In the HRA,some CAVs in the control zone are mapped onto the neighboring lane as virtual ones.To improve the driving comfort,the command acceleration caused by virtual vehicle is restricted.Comparing with the benchmark in which the CAVs change lane as soon as the lane changing condition is met,the HRA significantly improves the traffic flow:the overtaking throughput as well as the outflow rate increases,the travel delay and the fuel consumption decrease,the comfort level could also be improved.

Performance of bottleneck shifting for remanufacturing system considering returns' quality grading

Aimed at the remanufacturing system, the effect of the uncertainty of returns＇ quality on bottleneck shifting is investigated. A novel definition of bottleneck station is presented and the probability of a station becoming a bottleneck is also given. By calculating the effective output, the effective operation time （EOT） and the ratio of EOT of each station, the system＇s current bottleneck of effective output time is determined. By calculating the probability coefficient of variation and index of bottleneck shifting, the quantitative performance of bottleneck shifting is obtained. Discrete event simulation and the experiment design method are adopted to simulate the system, in which the proportion of quality grading, repair rates and process routes are considered. The case study shows that the uncertainty of returns＇ quality greatly increases the probability of bottleneck shifting, and with the increase of the discrete degree of the returns＇ repair rate, the bottleneck shifting phenomenon is more obvious. Furthermore, bottleneck shifting is closely related to the process route of the dominating returns＇ quality grade.

Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD Environment

Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce expenses.However,this trend introduces security challenges,such as unauthorized access.Traditional access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources.This paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN method.This technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access decision-making.The proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy rate.Additionally,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern organizations.Furthermore,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management.

Analysis of Prevention and Control Bottleneck Factors of American White Moth (Hyphantria cunea)

American white moth is a remarkable worldwide quarantine pest. By the results combination of indoor incubation and field observation of American white moth in Langfang City of Hebei Province, series prevention and control bottleneck factors for the invasive agricultural pest are analyzed, such as the main operation mode of the pest against host trees, diffusion and migration charaeteristies, biological characteristics, natural enemy control, pesticide prevention and so on. The re- search aims to search for the breakthrough point of the development of environment-friendly control techniques against American white moth, which also provides the reference for further improvement of integrated pest management system.

Bottlenecked ABR信源对ABR速率算法的影响(英文)

比较研究了一种特殊信源，Ｂｏｔｔｌｅｎｅｃｋｅｄ ＡＢＲ信源在ＡＴＭ 网络中对ＡＢＲ拥塞控制算法性能的影响．Ｂｏｔｔｌｅｎｅｃｋｅｄ ＡＢＲ信源由于某些原因无法做到根据交换网络提供的信息相应改变信元发送速率．由于网络交换节点对此不了解，必然导致对Ｂｏｔｔｌｅｎｅｃｋｅｄ 信源分配的速率过大而浪费网络资源．当Ｂｏｔｔｌｅｎｅｃｋｅｄ 信源不再发生信源拥塞情况时，网络又会因为瞬间增加的速率而发生拥塞．本文对不同的ＡＢＲ拥塞控制算法进行了仿真。

一类推广的Bottleneck问题的多项式算法

本文讨论了一类Bottleneck问题,给出了判定最优解的三个定理,并在此基础上给出了一个求最优解的算法和算例,算法的复杂度为O(n2).

深度信念网络的Bottleneck特征提取方法

为了提升连续语音识别系统的识别率,提出一种基于深度信念网络的Bottleneck特征提取方法。该方法使用对比散度算法,采用无监督的预训练堆叠限制玻尔兹曼机得到网络初始化参数,进而采用反向传播算法,以最大化帧级交叉熵作为训练准则,反向迭代对网络参数进行微调。采用上下文相关的三音素模型,以音素错误率大小作为评价系统性能的准则。实验结果表明,所提出的基于深度信念网络提取的Bottleneck特征相对于传统特征更具优越性。

Genetic diversity and genetic structure of the Daurian Partridge(Perdix dauuricae) in China,assessed by microsatellite variation

The Daurian Partridge(Perdix dauuricae) is a kind of hunting bird with high economic value.Genetic diversity and structure in the Daurian Partridge were studied by analyzing eight microsatellite loci in 23 populations found throughout the range of the species in China.The objectives were to evaluate the consequences on genetic diversity and differentiation of Daurian Partridge populations and to obtain a profound genetic insight for future management decisions and for effective measures to protect and exploit Daurian Partridges.The results showed that microsatellites were polymorphic in all Daurian Partridge populations,with a high level of genetic diversity over all the loci,especially in the Qaidam Basin populations which have the highest level of diversity.Significant genetic divergence was observed among different groups as well as between populations within the same group;most pairwise FST values were highly significant.Both phylogenetic trees and Bayesian clustering analyses revealed clear differentiation among the 23 populations of the Daurian Partridge,which were classified into two genetically differentiated groups.A bottleneck analysis indicated that Daurian Partridge populations have experienced a recent bottleneck.Our study argues that the Qaidam populations,North China populations,JN population,ZJC population,and Liupan Mountain populations should be paid special attention in order to retain adequate population sizes for maintaining genetic diversity.

基于区分性准则的Bottleneck特征及其在LVCSR中的应用

基于深层神经网络中间层的Bottleneck(BN)特征由于可以采用传统的混合高斯模型-隐马尔可夫建模(Gaussian mixture model-hidden Markov model,GMM-HMM),在大规模连续语音识别中获得了广泛的应用。为了提取区分性的BN特征,本文提出在使用传统的BN特征训练好GMM-HMM模型之后,利用最小音素错误率(Minimum phone error,MPE)准则来优化BN网络参数以及GMM-HMM模型参数。该算法相对于其他区分性训练算法而言,采用的是全部数据作为一个大的数据包,而不是小的包方式来训练深度神经网络,从而可以大大加快训练速度。实验结果表明,优化后的BN特征提取网络比传统方法能获得9%的相对词错误率下降。

Dynamic Prediction Method of Production Logistics Bottleneck Based on Bottleneck Index

In modern manufacturing pattern, there are many uncertain factors in the modern manufacturing process, such as changes of product attribute, changes of manufacturing resources＇ state, and so on, which cause production logistics bottleneck frequently shift, and make decisions of production planning and control based on formed bottleneck deviated from practical production process. Considering these factors, present researches mainly apply afterwards control to optimize production process to passively adapt to bottleneck changes If the direction of bottleneck shifting can be accurately forecasted, the transition from afterwards control of chasing bottleneck to beforehand control can be realized. Therefore, aiming at the phenomenon of production logistics bottleneck shifting under uncertain manufacturing circumstances, this paper starts off with dynamic property of capability and requirement and then builds the concepts of bottleneck degree and bottleneck index to describe dynamic bottleneck characteristic of production unit; taken production capability, production load and quality assurance capability into consideration, mathematical model of bottleneck index is established to measure bottleneck degree accurately, consequently, quantitative research on mechanism of production logistics shifting is achieved. Based on bottleneck index, the prediction model of production logistics bottleneck is founded to predict dynamic change of bottleneck accurately. Finally, an example of forecasting and monitoring the production logistics bottleneck in one manufacturing shop is given to testify the validation and practicability of the prediction method.

Modified bottleneck-based heuristic for large-scale job-shop scheduling problems with a single bottleneck

A modified bottleneck-based （MB） heuristic for large-scale job-shop scheduling problems with a welldefined bottleneck is suggested, which is simpler but more tailored than the shifting bottleneck （SB） procedure. In this algorithm, the bottleneck is first scheduled optimally while the non-bottleneck machines are subordinated around the solutions of the bottleneck schedule by some effective dispatching rules. Computational results indicate that the MB heuristic can achieve a better tradeoff between solution quality and computational time compared to SB procedure for medium-size problems. Furthermore, it can obtain a good solution in a short time for large-scale jobshop scheduling problems.

An automatic identification algorithm for freeway bottleneck based on loop detector data

A bottleneck automatic identification algorithm based on loop detector data is proposed. The proposed algorithm selects the critical flow rate as the trigger variable of the algorithm which is calculated by the road conditions the level of service and the proportion of trucks.The process of identification includes two parts. One is to identify the upstream of the bottleneck by comparing the distance between the current occupancy rate and the mean value of the occupancy rate and the variance of the occupancy rate.The other process is to identify the downstream of the bottleneck by calculating the difference of the upstream occupancy rate with that of the downstream.In addition the algorithm evaluation standards which are based on the time interval of the data the detection rate and the false alarm rate are discussed.The proposed algorithm is applied to detect the bottleneck locations in the Shanghai Inner Ring Viaduct Dabaishu-Guangzhong road section.The proposed method has a good performance in improving the accuracy and efficiency of bottleneck identification.

Bottlenecks and Countermeasures of High-Penetration Renewable Energy Development in China

China has become the world’s largest producer and consumer of energy,and ranks first in its wind and solar power installation capacity.However,serious wind and solar curtailment in China has significantly hindered the development and utilization of renewable energy.To address problems in the consumption of renewable energy,this paper analyzes four key factors affecting the capacity of power generated from renewable energy sources:power balance,power regulation performance,transmission capacity,and load level.Focusing on these bottlenecks,we propose seven solutions:centralized and distributed development of renewable energy,improving the peak-load regulation flexibility of thermal power,increasing the proportion of gas turbines and pumped-hydropower storage,construction of transmission channels and a flexible smart grid developing demand response and virtual power plants,adopting new energy active support and energy storage,and establishing appropriate policies and market mechanisms.The Chinese Government and energy authorities have issued a series of policies and measures,and in the past three years,China has had remarkable achievements in the adoption of renewable energy.The rate of idle wind capacity decreased from 17%in 2016 to 7%in 2018,and that of solar decreased from 10%in 2016 to 3%in 2018.

Identification and characteristics analysis of bottlenecks on urban expressways based on floating car data

Identifying bottlenecks and analyzing their characteristics are important tasks to city traffic management authorities.Although the speed difference was proposed for the bottleneck identification in the existing research,the use of a secondary indicator has not been fully discussed.This paper strived to develop a method to identify the bottleneck on expressways by using the massive floating car data(FCD)in Beijing.First,the speed characteristics of bottlenecks on expressway were analyzed based on the speed contour map.The results indicated that there was a significant difference between speeds on the bottleneck and downstream links when a bottleneck was observed.The speed difference could indeed be used as the primary indicator to identify the bottleneck.However,it was also shown that a sufficiently large speed difference does not necessitate an activation of a bottleneck.The speed-at-capacity was then used as the secondary indicator to distinguish the real bottleneck from the non-bottleneck speed difference.Second,a practical method for identifying the bottleneck on expressways was developed based on the speed difference and the speed-at-capacity.Finally,the method was applied to identifying the bottlenecks of the 3rd Outer Ring Expressway in Beijing.The duration,affected distance,delay and cause were used to evaluate and analyze the bottlenecks.

A CAPACITY EXPANSION PROBLEM WITH BUDGET CONSTRAINT AND BOTTLENECK LIMITATION

This paper considers a capacity expansion problem with budget constraint. Suppose each edge in the network has two attributes: capacity and the degree of difficulty. The difficulty degree of a tree T is the maximum. degree of difficulty of all edges in the tree and the cost for coping with the difficulty in a tree is a nondecreasing function about the difficulty degree of the tree. The authors need to increase capacities of some edges so that there is a spanning tree whose capacity can be increased to the maximum extent, meanwhile the total cost for increasing capacity as well as overcoming the difficulty in the spanning tree does not exceed a given budget D*. Suppose the cost for increasing capacity on each edge is a linear function about the increment of capacity, they transform this problem into solving some hybrid parametric spanning tree problems([1]) and propose a strongly polynomial algorithm.

Bottleneck Prediction Method Based on Improved Adaptive Network-based Fuzzy Inference System (ANFIS) in Semiconductor Manufacturing System

Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semiconductor fabrication has long been a hot research direction in automation. Bottleneck is the key factor to a SM system, which seriously influences the throughput rate, cycle time, time-delivery rate, etc. Efficient prediction for the bottleneck of a SM system provides the best support for the consequent scheduling. Because categorical data (product types, releasing strategies) and numerical data (work in process, processing time, utilization rate, buffer length, etc.) have significant effect on bottleneck, an improved adaptive network-based fuzzy inference system (ANFIS) was adopted in this study to predict bottleneck since conventional neural network-based methods accommodate only numerical inputs. In this improved ANFIS, the contribution of categorical inputs to firing strength is reflected through a transformation matrix. In order to tackle high-dimensional inputs, reduce the number of fuzzy rules and obtain high prediction accuracy, a fuzzy c-means method combining binary tree linear division method was applied to identify the initial structure of fuzzy inference system. According to the experimental results, the main-bottleneck and sub-bottleneck of SM system can be predicted accurately with the proposed method.

Bottleneck算法的改进与仿真

分析了Bottleneck算法的不足,指出在路段较为拥挤的情况下该算法中因不具备预测机制而产生的时滞会引起控制误差以及主线拥挤时会造成交通流大幅波动等,产生不足的原因是算法的两个限制条件过于严格,进而提出了改进方案。改进算法中对两个限制条件适当放宽,在此基础上以宁杭高速(江苏段)一期为背景进行了仿真,并与原算法进行了比较。仿真结果表明,改进后的算法能更快更平稳地使交通流达到稳定状态,且具有较强的鲁棒性。

A data-driven method to predict future bottlenecks in a remanufacturing system with multi-variant uncertainties

The remanufacturing system is remolding the manufacturing industry by bringing scrapped products back to such a condition that reintegrated performance is just as good as new.The remanufacturing environment is featured by a far deeper level of uncertainty than new manufacturing,such as probabilistic routing files,and highly variable processing time.The stochastic disturbances result in the production bottlenecks,which constrain the productivity of the job shop.The uncertainties in the remanufacturing process cause the bottlenecks to shift when the workshop is processing.Considering this outstanding problem,many researchers try to optimize the production process to mitigate dynamic bottlenecks toward a balanced state.This paper proposes a data-driven method to predict bottlenecks in the remanufacturing system with multi-variant uncertainties.Firstly,discrete event simulation technology is applied to establish a simulation model of the remanufacturing production line and calculate the bottleneck index to identify bottlenecks.Secondly,a data-driven method,auto-regressive moving average(ARMA)model is employed to predict the bottlenecks in the system based on real-time data captured by the Arena software.Finally,the proposed prediction method is verified on real data from the automobile engine remanufacturing production line.