A Multi-Object Genetic Algorithm for the Assembly Line Balance Optimization in Garment Flexible Job Shop Scheduling

Numerous clothing enterprises in the market have a relatively low efficiency of assembly line planning due to insufficient optimization of bottleneck stations.As a result,the production efficiency of the enterprise is not high,and the production organization is not up to expectations.Aiming at the problem of flexible process route planning in garment workshops,a multi-object genetic algorithm is proposed to solve the assembly line bal-ance optimization problem and minimize the machine adjustment path.The encoding method adopts the object-oriented path representation method,and the initial population is generated by random topology sorting based on an in-degree selection mechanism.The multi-object genetic algorithm improves the mutation and crossover operations according to the characteristics of the clothing process to avoid the generation of invalid offspring.In the iterative process,the bottleneck station is optimized by reasonable process splitting,and process allocation conforms to the strict limit of the station on the number of machines in order to improve the compilation efficiency.The effectiveness and feasibility of the multi-object genetic algorithm are proven by the analysis of clothing cases.Compared with the artificial allocation process,the compilation efficiency of MOGA is increased by more than 15%and completes the optimization of the minimum machine adjustment path.The results are in line with the expected optimization effect.

Blockchain Based Secured Load Balanced Task Scheduling Approach for Fitness Service

In recent times,the evolution of blockchain technology has got huge attention from the research community due to its versatile applications and unique security features.The IoT has shown wide adoption in various applications including smart cities,healthcare,trade,business,etc.Among these applications,fitness applications have been widely considered for smart fitness systems.The users of the fitness system are increasing at a high rate thus the gym providers are constantly extending the fitness facilities.Thus,scheduling such a huge number of requests for fitness exercise is a big challenge.Secondly,the user fitness data is critical thus securing the user fitness data from unauthorized access is also challenging.To overcome these issues,this work proposed a blockchain-based load-balanced task scheduling approach.A thorough analysis has been performed to investigate the applications of IoT in the fitness industry and various scheduling approaches.The proposed scheduling approach aims to schedule the requests of the fitness users in a load-balanced way that maximize the acceptance rate of the users’requests and improve resource utilization.The performance of the proposed task scheduling approach is compared with the state-of-the-art approaches concerning the average resource utilization and task rejection ratio.The obtained results confirm the efficiency of the proposed scheduling approach.For investigating the performance of the blockchain,various experiments are performed using the Hyperledger Caliper concerning latency,throughput,resource utilization.The Solo approach has shown an improvement of 32%and 26%in throughput as compared to Raft and Solo-Raft approaches respectively.The obtained results assert that the proposed architecture is applicable for resource-constrained IoT applications and is extensible for different IoT applications.

MTSS: multi-path traffic scheduling mechanism based on SDN

Large-scale and diverse businesses based on the cloud computing platform bring the heavy network traffic to cloud data centers.However,the unbalanced workload of cloud data center network easily leads to the network congestion,the low resource utilization rate,the long delay,the low reliability,and the low throughput.In order to improve the utilization efficiency and the quality of services(QoS)of cloud system,especially to solve the problem of network congestion,we propose MTSS,a multi-path traffic scheduling mechanism based on software defined networking(SDN).MTSS utilizes the data flow scheduling flexibility of SDN and the multi-path feature of the fat-tree structure to improve the traffic balance of the cloud data center network.A heuristic traffic balancing algorithm is presented for MTSS,which periodically monitors the network link and dynamically adjusts the traffic on the heavy link to achieve programmable data forwarding and load balancing.The experimental results show that MTSS outperforms equal-cost multi-path protocol(ECMP),by effectively reducing the packet loss rate and delay.In addition,MTSS improves the utilization efficiency,the reliability and the throughput rate of the cloud data center network.

An Advanced Dynamic Scheduling for Achieving Optimal Resource Allocation

Cloud computing distributes task-parallel among the various resources.Applications with self-service supported and on-demand service have rapid growth.For these applications,cloud computing allocates the resources dynami-cally via the internet according to user requirements.Proper resource allocation is vital for fulfilling user requirements.In contrast,improper resource allocations result to load imbalance,which leads to severe service issues.The cloud resources implement internet-connected devices using the protocols for storing,communi-cating,and computations.The extensive needs and lack of optimal resource allo-cating scheme make cloud computing more complex.This paper proposes an NMDS(Network Manager based Dynamic Scheduling)for achieving a prominent resource allocation scheme for the users.The proposed system mainly focuses on dimensionality problems,where the conventional methods fail to address them.The proposed system introduced three–threshold mode of task based on its size STT,MTT,LTT(small,medium,large task thresholding).Along with it,task mer-ging enables minimum energy consumption and response time.The proposed NMDS is compared with the existing Energy-efficient Dynamic Scheduling scheme(EDS)and Decentralized Virtual Machine Migration(DVM).With a Network Manager-based Dynamic Scheduling,the proposed model achieves excellence in resource allocation compared to the other existing models.The obtained results shows the proposed system effectively allocate the resources and achieves about 94%of energy efficient than the other models.The evaluation metrics taken for comparison are energy consumption,mean response time,percentage of resource utilization,and migration.

An Adaptive Genetic Algorithm-Based Load Balancing-Aware Task Scheduling Technique for Cloud Computing

Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among researchers.Task scheduling algorithms are responsible for the allocation of the tasks among the computing resources for their execution,and an inefficient task scheduling algorithm results in under-or over-utilization of the resources,which in turn leads to degradation of the services.Therefore,in the proposed work,load balancing is considered as an important criterion for task scheduling in a cloud computing environment as it can help in reducing the overhead in the critical decision-oriented process.In this paper,we propose an adaptive genetic algorithm-based load balancing(GALB)-aware task scheduling technique that not only results in better utilization of resources but also helps in optimizing the values of key performance indicators such as makespan,performance improvement ratio,and degree of imbalance.The concept of adaptive crossover and mutation is used in this work which results in better adaptation for the fittest individual of the current generation and prevents them from the elimination.CloudSim simulator has been used to carry out the simulations and obtained results establish that the proposed GALB algorithm performs better for all the key indicators and outperforms its peers which are taken into the consideration.

Distributed Graph Database Load Balancing Method Based on Deep Reinforcement Learning

This paper focuses on the scheduling problem of workflow tasks that exhibit interdependencies.Unlike indepen-dent batch tasks,workflows typically consist of multiple subtasks with intrinsic correlations and dependencies.It necessitates the distribution of various computational tasks to appropriate computing node resources in accor-dance with task dependencies to ensure the smooth completion of the entire workflow.Workflow scheduling must consider an array of factors,including task dependencies,availability of computational resources,and the schedulability of tasks.Therefore,this paper delves into the distributed graph database workflow task scheduling problem and proposes a workflow scheduling methodology based on deep reinforcement learning(DRL).The method optimizes the maximum completion time(makespan)and response time of workflow tasks,aiming to enhance the responsiveness of workflow tasks while ensuring the minimization of the makespan.The experimental results indicate that the Q-learning Deep Reinforcement Learning(Q-DRL)algorithm markedly diminishes the makespan and refines the average response time within distributed graph database environments.In quantifying makespan,Q-DRL achieves mean reductions of 12.4%and 11.9%over established First-fit and Random scheduling strategies,respectively.Additionally,Q-DRL surpasses the performance of both DRL-Cloud and Improved Deep Q-learning Network(IDQN)algorithms,with improvements standing at 4.4%and 2.6%,respectively.With reference to average response time,the Q-DRL approach exhibits a significantly enhanced performance in the scheduling of workflow tasks,decreasing the average by 2.27%and 4.71%when compared to IDQN and DRL-Cloud,respectively.The Q-DRL algorithm also demonstrates a notable increase in the efficiency of system resource utilization,reducing the average idle rate by 5.02%and 9.30%in comparison to IDQN and DRL-Cloud,respectively.These findings support the assertion that Q-DRL not only upholds a lower average idle rate but also effectively curtails the average response time,thereby substantially improving processing efficiency and optimizing resource utilization within distributed graph database systems.

Review of Load Balancing Mechanisms in SDN-Based Data Centers

With the continuous expansion of the data center network scale, changing network requirements, and increasing pressure on network bandwidth, the traditional network architecture can no longer meet people’s needs. The development of software defined networks has brought new opportunities and challenges to future networks. The data and control separation characteristics of SDN improve the performance of the entire network. Researchers have integrated SDN architecture into data centers to improve network resource utilization and performance. This paper first introduces the basic concepts of SDN and data center networks. Then it discusses SDN-based load balancing mechanisms for data centers from different perspectives. Finally, it summarizes and looks forward to the study on SDN-based load balancing mechanisms and its development trend.

Service Scheduling Based on Edge Computing for Power Distribution IoT

With the growing amounts of multi-micro grids,electric vehicles,smart home,smart cities connected to the Power Distribution Internet of Things(PD-IoT)system,greater computing resource and communication bandwidth are required for power distribution.It probably leads to extreme service delay and data congestion when a large number of data and business occur in emergence.This paper presents a service scheduling method based on edge computing to balance the business load of PD-IoT.The architecture,components and functional requirements of the PD-IoT with edge computing platform are proposed.Then,the structure of the service scheduling system is presented.Further,a novel load balancing strategy and ant colony algorithm are investigated in the service scheduling method.The validity of the method is evaluated by simulation tests.Results indicate that the mean load balancing ratio is reduced by 99.16%and the optimized offloading links can be acquired within 1.8 iterations.Computing load of the nodes in edge computing platform can be effectively balanced through the service scheduling.

Dynamic Balance Control Based on an Adaptive Gain-scheduled Backstepping Scheme for Power-line Inspection Robots

This paper presents an adaptive gain-scheduled backstepping control(AGSBC) scheme for the balance control of an underactuated mechanical power-line inspection(PLI) robotic system with two degrees of freedom and a single control input.First, a nonlinear dynamic model of the balance adjustment process of the PLI robot is constructed, and then the model is linearized at a nominal equilibrium point to overcome the computational infeasibility of the conventional backstepping technique. Second, to solve generalized stabilization control issue for underactuated systems with multiple equilibrium points,an equilibrium manifold linearized model is developed using a scheduling variable, and then a gain-scheduled backstepping control(GSBC) scheme for expanding the operational area of the controlled system is constructed. Finally, an adaptive mechanism is proposed to counteract the impact of external disturbances. The robust stability of the closed-loop system is ensured by Lyapunov theorem. Simulation results demonstrate the effectiveness and high performance of the proposed scheme compared with other control schemes.

Optimal Scheduling of Air Conditioners for Energy Efficiency

Energy saving is one of the most important research hotspots, by which operational expenditure and CO2 emission can be reduced. Optimal cooling capacity scheduling in addition to temperature control can improve energy efficiency. The main contribution of this work is modeling the telecommunication building for the fabric cooling load to schedule the operation of air conditioners. The time series data of the fabric cooling load of the building envelope is taken by simulation by using Energy Plus, Building Control Virtual Test Bed （BCVTB）, and Matlab. This pre-computed data and other internal thermal loads are used for scheduling in air conditioners. Energy savings obtained for the whole year are about 4% to 6% by simulation and the field study, respectively.

Metaheuristic Based Resource Scheduling Technique for Distributed Robotic Control Systems

The design of controllers for robots is a complex system that is to be dealt with several tasks in real time for enabling the robots to function independently.The distributed robotic control system can be used in real time for resolving various challenges such as localization,motion controlling,mapping,route planning,etc.The distributed robotic control system can manage different kinds of heterogenous devices.Designing a distributed robotic control system is a challenging process as it needs to operate effectually under different hardware configurations and varying computational requirements.For instance,scheduling of resources(such as communication channel,computation unit,robot chassis,or sensor input)to the various system components turns out to be an essential requirement for completing the tasks on time.Therefore,resource scheduling is necessary for ensuring effective execution.In this regard,this paper introduces a novel chaotic shell game optimization algorithm(CSGOA)for resource scheduling,known as the CSGOA-RS technique for the distributed robotic control system environment.The CSGOA technique is based on the integration of the chaotic maps concept to the SGO algorithm for enhancing the overall performance.The CSGOA-RS technique is designed for allocating the resources in such a way that the transfer time is minimized and the resource utilization is increased.The CSGOA-RS technique is applicable even for the unpredicted environment where the resources are to be allotted dynamically based on the early estimations.For validating the enhanced performance of the CSGOA-RS technique,a series of simulations have been carried out and the obtained results have been examined with respect to a selected set of measures.The resultant outcomes highlighted the promising performance of the CSGOA-RS technique over the other resource scheduling techniques.

Heuristic Scheduling Algorithms for Allocation of Virtualized Network and Computing Resources

Cloud computing technology facilitates computing-intensive applications by providing virtualized resources which can be dynamically provisioned. However, user’s requests are varied according to different applications’ computation ability needs. These applications can be presented as meta-job of user’s demand. The total processing time of these jobs may need data transmission time over the Internet as well as the completed time of jobs to execute on the virtual machine must be taken into account. In this paper, we presented V-heuristics scheduling algorithm for allocation of virtualized network and computing resources under user’s constraint which applied into a service-oriented resource broker for jobs scheduling. This scheduling algorithm takes into account both data transmission time and computation time that related to virtualized network and virtual machine. The simulation results are compared with three different types of heuristic algorithms under conventional network or virtual network conditions such as MCT, Min-Min and Max-Min. e evaluate these algorithms within a simulated cloud environment via an abilenenetwork topology which is real physical core network topology. These experimental results show that V-heuristic scheduling algorithm achieved significant performance gain for a variety of applications in terms of load balance, Makespan, average resource utilization and total processing time.

Parallel scheduling strategy of web-based spatial computing tasks in multi-core environment

In order to improve the concurrent access performance of the web-based spatial computing system in cluster,a parallel scheduling strategy based on the multi-core environment is proposed,which includes two levels of parallel processing mechanisms.One is that it can evenly allocate tasks to each server node in the cluster and the other is that it can implement the load balancing inside a server node.Based on the strategy,a new web-based spatial computing model is designed in this paper,in which,a task response ratio calculation method,a request queue buffer mechanism and a thread scheduling strategy are focused on.Experimental results show that the new model can fully use the multi-core computing advantage of each server node in the concurrent access environment and improve the average hits per second,average I/O Hits,CPU utilization and throughput.Using speed-up ratio to analyze the traditional model and the new one,the result shows that the new model has the best performance.The performance of the multi-core server nodes in the cluster is optimized;the resource utilization and the parallel processing capabilities are enhanced.The more CPU cores you have,the higher parallel processing capabilities will be obtained.

基于遗传算法的时间敏感网络调度方法

随着网络技术的进步,车载网、工业物联网以及5G超高可靠低时延通信(uRLLC)等应用都需要时间敏感网络(TSN)来保证超低延时的确定性数据传输。TSN流量调度需要快速且精确的调度算法,现有的精确式求解方法复杂度高,在大规模联合调度时无法满足实时性。文中设计了一种性能更优的路由优化遗传算法(Routing-GA),结合路由和流量调度约束,能通过优化路由来提高调度算法求解效率,为链路负载均衡调度提供服务。该策略增加了调度的求解空间以及求解灵活性,具备元启发式算法的快速求近最优解特点,能够简单有效地处理大规模TSN路由约束联合调度问题。Routing-GA以时间敏感流最小端到端时延作为优化目标,联合考虑路由和TSN约束,并针对TSN传输问题特性提供一种低复杂度、高效率和高拓展性的遗传算法编码方式。此外,为了提高调度算法的性能,提出针对路由长度及链路负载均衡进行优化的交叉变异机制。实验结果表明所实现的Routing-GA能有效减少端到端时延,显著提高求解质量,进化率可以达到24.42%,平均只需要传统遗传算法(GA)迭代运行时间的12%,从而有效提高了算法的求解性能,满足TSN调度的约束要求。

改进文化基因算法求解双资源约束柔性作业车间调度问题

针对具有机器和工人的双资源约束柔性作业车间调度问题,以最小化最大完工时间为目标构建调度模型,并设计一种改进文化基因算法对其进行求解。由于该调度问题需要同时考虑工序排序、机器选择及工人选择3个子问题,故采用三层序列编码。考虑传统解码方式存在收敛速度慢、收敛不完全的弊端,设计一种扩展型插入式主动解码方式,以提高算法的收敛速度;针对进化算法易陷入局部最优的缺陷,设计一种基于负载平衡的机器和工人再分配算子,增强算法的全局搜索能力,对种群中的优秀个体采用改进变邻域搜索以提高算法的局部寻优能力。最后,利用仿真算例及航空设备生产实例进行实验,验证所提算法求解双资源约束调度问题的有效性。

考虑多能负荷波动实时平衡的综合能源系统多时间尺度优化调度

综合能源系统内负荷的不确定性及多能源响应负荷波动的时间尺度不同为优化调度结果的可靠性及系统对负荷波动的响应带来了挑战。确定日前、日内、实时三阶段优化调度模式,在日内调度中对当日影响系统运行的随机事件进行分类并将其量化影响反馈至调度模块;对系统内热/冷负荷端口进行能流分析,通过监测供回水压、水温等数据,快速响应热/冷负荷波动,并在实时调度中调整机组出力;建立日前、日内、实时三阶段优化调度模型,提出基于多能负荷波动快速响应的综合能源系统多时间尺度优化调度方法。以中国南方某城市综合能源系统冬季运行模式为例进行数值分析。结果表明,所提方法对计划性负荷调整及突发小幅负荷波动均有良好效果,能有效提升系统供能收益和用户的用能舒适度。

面向异构处理平台任务调度的麻雀优化算法

针对当前异构信号处理平台中各处理器任务数量分配不均衡、处理器性能发挥不完全以及系统运行效率低的问题,文中提出一种面向异构处理平台的麻雀优化算法。该算法利用了麻雀算法较强的全局寻优能力和麻雀种群内部的高效工作机制。在经典麻雀算法基础上,文中提出了符合任务调度的二进制异或编解码规则,将离散的任务分配方案映射为连续的麻雀位置信息。将处理器负载均衡指数作为适应度函数,选取每次迭代中的最优解;在麻雀遍历任务时,采用任务优先级分流排序策略。对通信密集型任务和计算密集型任务采取不同的计算式得到更符合任务特点的遍历顺序,生成随机任务图,并将所提算法同ICPA(Improved Critical Path Algortthm)算法进行对比。仿真结果表明,相比于ICPA算法,所提算法的负载均衡指数平均优化率为60%,各处理器负载情况更加均衡,能更好地发挥异构处理平台的整体效能。

深度学习编译器模型训练负载均衡优化方法

对于计算密集型的人工智能(AI)训练应用,其计算图网络结构更加复杂,数据加载、计算图的任务划分以及任务调度的负载均衡性都会成为影响计算性能的关键因素。为了使深度学习编译器中模型训练应用的任务调度达到负载均衡的状态,提出了三种计算图负载均衡优化方法:第一,通过自动建立数据加载与模型训练的高效流水实现中央处理器和后端计算设备的负载均衡,提高了系统整体能效;第二,通过计算图的分层优化技术,实现计算图在后端设备执行调度时的负载均衡;最后,通过自动建立层间的高效流水提高后端设备的资源利用率。实验结果表明,计算图负载均衡优化方法实现了训练任务到底层硬件设备自动映射过程中系统的负载均衡,与Tensorflow、nGraph等传统的深度学习框架和编译器相比,在不同模型训练中通过任务调度负载均衡优化技术分别获得了2%~10%的性能提升,同时能够使系统整体的能耗降低10%以上。

带负载均衡的混合算法求解分布式异构作业车间调度问题

针对以最小化最大完工时间为目标的分布式异构作业车间调度问题(DHJSP),本文提出了一种新的混合遗传禁忌搜索算法.首先,综合考虑工厂的工件总负载与最大机器负载,提出了一种新的工厂负载表达方式.其次,针对DHJSP总工序数不定的特性,提出以最小化最大工厂负载为目标快速确定初始工件分配方案,并验证了方法的高效性.然后,新设计了两种考虑负载均衡的单工件转移邻域结构,根据工序调度的结果对工件分配方案进行局部搜索.最后,因DHJSP缺少标准算例和相关算法,在分布式同构作业车间调度问题(DJSP)上与现有算法进行对比,所提算法在TA算例的480个问题上更新了420个问题的最优解,其余60个问题取得了同等最优解.在随机生成的3个不同规模的异构算例中,所提算法也均取得了较好解,验证了所提方法的优越性.

城市轨道交通车辆均衡修检修制度和计划性检修制度对比分析

[目的]为了尽可能降低城市轨道交通车辆购置费和运维成本,我国部分城市目前已开展均衡修检修制度,因此有必要将车辆均衡修检修制度和原有计划性检修制度进行对比分析。[方法]以重庆轨道交通为例,分别介绍了两种检修制度下的检修修程和检修周期,并对两种检修制度下的车辆上线率和检修列位进行了对比分析。[结果及结论]相较传统计划性检修制度,均衡修检修制度下每列列车每年扣车时间减少了19 d,且所需检修列位数增加了18.5%。结果表明,城市轨道交通车辆均衡修检修制度更为合理。