Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

Algorithms for Multicriteria Scheduling Problems to Minimize Maximum Late Work, Tardy, and Early

This study examines the multicriteria scheduling problem on a single machine to minimize three criteria: the maximum cost function, denoted by maximum late work (Vmax), maximum tardy job, denoted by (Tmax), and maximum earliness (Emax). We propose several algorithms based on types of objectives function to be optimized when dealing with simultaneous minimization problems with and without weight and hierarchical minimization problems. The proposed Algorithm (3) is to find the set of efficient solutions for 1//F (Vmax, Tmax, Emax) and 1//(Vmax + Tmax + Emax). The Local Search Heuristic Methods (Descent Method (DM), Simulated Annealing (SA), Genetic Algorithm (GA), and the Tree Type Heuristics Method (TTHM) are applied to solve all suggested problems. Finally, the experimental results of Algorithm (3) are compared with the results of the Branch and Bound (BAB) method for optimal and Pareto optimal solutions for smaller instance sizes and compared to the Local Search Heuristic Methods for large instance sizes. These results ensure the efficiency of Algorithm (3) in a reasonable time.

Multi-UAV surveillance implementation under hierarchical dynamic task scheduling architecture

In this paper,we consider a multi-UAV surveillance scenario where a team of unmanned aerial vehicles(UAVs)synchronously covers an area for monitoring the ground conditions.In this scenario,we adopt the leader-follower control mode and propose a modified Lyapunov guidance vector field(LGVF)approach for improving the precision of surveillance trajectory tracking.Then,in order to adopt to poor communication conditions,we propose a prediction-based synchronization method for keeping the formation consistently.Moreover,in order to adapt the multi-UAV system to dynamic and uncertain environment,this paper proposes a hierarchical dynamic task scheduling architecture.In this architecture,we firstly classify all the algorithms that perform tasks according to their functions,and then modularize the algorithms based on plugin technology.Afterwards,integrating the behavior model and plugin technique,this paper designs a three-layer control flow,which can efficiently achieve dynamic task scheduling.In order to verify the effectiveness of our architecture,we consider a multi-UAV traffic monitoring scenario and design several cases to demonstrate the online adjustment from three levels,respectively.

Scheduling method based on virtual flattened architecture for Hierarchical system-on-chip

As the technology of IP-core-reused has been widely used, a lot of intellectual property （IP） cores have been embedded in different layers of system-on-chip （SOC）. Although the cycles of development and overhead are reduced by this method, it is a challenge to the SOC test. This paper proposes a scheduling method based on the virtual flattened architecture for hierarchical SOC, which breaks the hierarchical architecture to the virtual flattened one. Moreover, this method has more advantages compared with the traditional one, which tests the parent cores and child cores separately. Finally, the method is verified by the ITC＇02 benchmark, and gives good results that reduce the test time and overhead effectively.

Single Machine Scheduling Problem with Release Dates and Two Hierarchical Criteria to Minimize Makespan and Stocking Cost

In this paper, the single machine scheduling problem with release dates and two hierarchical criteria is discussed. The first criterion is to minimize makespan, and the second criterion is to minimize stocking cost. We show that this problem is strongly NP-hard. We also give an O（n^2） time algorithm for the special case that all stocking costs of jobs in unit time are 1.

Scheduling Check-in Staff with Hierarchical Skills and Weekly Rotation Shifts

The paper aims to schedule check-in staff with hierarchical skills as well as day and night shifts in weekly rotation.That shift ensures staff work at day in a week and at night for the next week.The existing approaches do not deal with the shift constraint.To address this,the proposed algorithm firstly guarantees the day and night shifts by designing a data copy tactic,and then introduces two algorithms to generate staff assignment in a polynomial time.The first algorithm is to yield an initial solution efficiently,whereas the second incrementally updates that solution to cut off working hours.The key idea of the two algorithms is to utilize a block Gibbs sampling with replacement to simultaneously exchange multiple staff assignment.Experimental results indicate that the proposed algorithm reduces at least 15.6 total working hours than the baselines.

ENERGY-SAVING SCHEDULING FOR LTE MULTICAST SERVICES

In the Long Term Evolution(LTE)downlink multicast scheduling,Base Station(BS)usually allocates transmit power equally among all Resource Blocks(RBs),it may cause the waste of transmit power.To avoid it,this paper put forward a new algorithm for LTE multicast downlink scheduling called the Energy-saving based Inter-group Proportional Fair(EIPF).The basic idea of EIPF is to calculate an appropriate transmitting power for each group according to its data rate respectively,and then follow the inter-group proportional fair principle to allocate RBs among multicast groups.The results of EIPF simulation show that the proposed algorithm not only can reduce the transmit power of BS effectively but also improve the utilization rate of energy.

Multi-subject and multi-objective integrated optimization system and implementation strategy for energy-saving renovation of the existing residential buildings

The core of the healthy and orderly operation of the existing residential building energy-saving renovation market lies in the exploration of the implementation mechanism of multi-subject and multi-objective integrated optimization.The multi-agent and multi-objective integrated optimization system framework is a powerful tool to guide the scientific decision-making of the market core structural entities in the future market practice. This paper analyzes the practical dilemma of energy-saving renovation of theexisting residential buildings in China, summarizes the practical experience of multi-subject and multi-objective integrated optimization of energy-saving renovation of the existing residential buildings in foreign countries, and puts forward beneficial practical enlightenment on the basis of comparison at home and abroad;The design principles of the target integrated optimization system have established a multi-subject and multi-objective integrated optimization system framework for the energy-saving renovation of the existing residential buildings, from six aspects: government guidance, trust consensus, value co-creation, risk sharing, revenue sharing, and social responsibility sharing. This paper proposes a multi-subject and multi-objective integrated practice strategy, in order to promote the efficient and orderly development of China's existing residential building energy-saving renovation market.

Wind Turbine Optimal Preventive Maintenance Scheduling Using Fibonacci Search and Genetic Algorithm

Maintenance scheduling is essential and crucial for wind turbines (WTs) to avoid breakdowns andreduce maintenance costs. Many maintenance models have been developed for WTs’ maintenance planning, suchas corrective, preventive, and predictive maintenance. Due to communities’ dependence on WTs for electricityneeds, preventive maintenance is the most widely used method for maintenance scheduling. The downside tousing this approach is that preventive maintenance (PM) is often done in fixed intervals, which is inefficient. In thispaper, a more detailed maintenance plan for a 2 MW WT has been developed. The paper’s focus is to minimize aWT’s maintenance cost based on a WT’s reliability model. This study uses a two-layer optimization framework:Fibonacci and genetic algorithm. The first layer in the optimization method (Fibonacci) finds the optimal numberof PM required for the system. In the second layer, the optimal times for preventative maintenance and optimalcomponents to maintain have been determined to minimize maintenance costs. The Monte Carlo simulationestimates WT component failure times using their lifetime distributions from the reliability model. The estimatedfailure times are then used to determine the overall corrective and PM costs during the system’s lifetime. Finally,an optimal PM schedule is proposed for a 2 MW WT using the presented method. The method used in this papercan be expanded to a wind farm or similar engineering systems.

Hierarchical resource allocation for integrated modular avionics systems

Recently the integrated modular avionics （IMA） architecture which introduces the concept of resource partitioning becomes popular as an alternative to the traditional federated architecture. A novel hierarchical approach is proposed to solve the resource allocation problem for IMA systems in distributed environments. Firstly, the worst case response time of tasks with arbitrary deadlines is analyzed for the two-level scheduler. Then, the hierarchical resource allocation approach is presented in two levels. At the platform level, a task assignment algorithm based on genetic simulated annealing （GSA） is proposed to assign a set of pre-defined tasks to different processing nodes in the form of task groups, so that resources can be allocated as partitions and mapped to task groups. While yielding to all the resource con- straints, the algorithm tries to find an optimal task assignment with minimized communication costs and balanced work load. At the node level, partition parameters are optimized, so that the computational resource can be allocated further. An example is shown to illustrate the hierarchal resource allocation approach and manifest the validity. Simulation results comparing the performance of the proposed GSA with that of traditional genetic algorithms are presented in the context of task assignment in IMA systems.

A bi-objective model for job-shop scheduling problem to minimize both energy consumption and makespan

The issue of reducing energy consumption for the job-shop scheduling problem in machining systems is addressed, whose dual objectives are to minimize both the energy consumption and the makespan. First, the bi- objective model for the job-shop scheduling problem is proposed. The objective function value of the model represents synthesized optimization of energy consumption and makespan. Then, a heuristic algorithm is developed to locate the optimal or near optimal solutions of the model based on the Tabu search mechanism. Finally, the experimental case is presented to demonstrate the effectiveness of the proposed model and the algorithm.

An Improved Genetic Algorithm for Berth Scheduling at Bulk Terminal

Berth and loading and unloading machinery are not only the mainfactors that affecting the terminal operation, but also the main starting point ofenergy saving and emission reduction. In this paper, a genetic Algorithm Framework is designed for the berth allocation with low carbon and high efficiency atbulk terminal. In solving the problem, the scheduler’s experience is transformedinto a regular way to obtain the initial solution. The individual is represented as achromosome, and the sub-chromosomes are encoded as integers, the roulettewheel method is used for selection, the two-point crossing method is used forcross, and the exchange variation method is used for variation in the procedureof designing the Algorithm. Considering the complexity of berth schedulingproblem and the diversity of constraints and boundary conditions, the geneticalgorithm combines with system simulation to get the final scheme of berthallocation. This model and algorithm are verified to be practical by analyzingmultiple sets of examples of shorelines with different lengths. When comparedwith the traditional algorithms in three aspects which includes berth offsetdistance, departure delay cost and energy consumption of portal crane, the resultindicates that the improved algorithm is more effective and feasible. The studywill help to lower energy consumption and resource waste, reduce environmentalpollution, and provide a reference for low-carbon, green and sustainable development of the terminal.

港口大规模冷箱负荷群用电的一致性分层优化调度方法

为解决港口大量冷藏集装箱负荷群优化调度面临的优化效果与计算效率难题,该文提出冷箱集群分层迭代调度架构及多智体制冷效率一致性优化策略。建立考虑热动态过程的冷箱负荷用电模型,并根据用电特性将冷箱聚类为集群,降低冷箱控制维度与信息交互量级。建立冷箱动态电价与集群用电功率迭代优化的预调度模型,提出冷箱制冷效率主从一致性的功率动态分配算法,冷箱个体根据电价、温度、制冷限值主动响应预调度策略,实现大规模冷箱自趋优运行和负荷功率有序转移。以日照港为算例,所提方法可将用电成本降低12.5%,计算效率提升4倍,优化结果与全局优化的偏差仅为0.5%,实现了大规模冷箱群高效优化。

光伏光热一体化建筑热负荷能效分层调度仿真

光伏光热一体化系统中涉及多种能源,包括太阳能和热能等。由于不同能源的产生、存储和使用特性不同,加之系统内部的复杂性,使得实现多能源协同调度变得复杂。为了获取满意的建筑热负荷能效分层协同调度结果,提出一种光伏光热一体化建筑热负荷能效分层协同调度方法。研究光伏光热一体化建筑中热负荷的热量传递过程,对热负荷自身和热惯性展开分析,获取热负荷热惯性大小和室内热量需求两者的关系。以最大[火用]效率和最小调度成本为目标,分别建立光伏光热一体化建筑热负荷能效上层和下层协同调度模型。通过量子粒子群算法对模型展开分析计算,确定最优建筑热负荷能效分层协同调度方案。实验分析表明,所提方法的热负荷能效分层协同调度效果好,且节能效益高。

基于TTNT数据链多址接入协议的多机协同任务调度方法

为提高无人机蜂群作战中无人机信息交互、资源共享和任务协同的能力,文章基于TTNT数据链SPMA协议,设计高动态变化拓扑下的分层分簇无人机蜂群模型和与之匹配的层次资源与任务调度模型,并对其消息传输进行优化。通过OMNeT++仿真平台,以TTNT数据链中的数据传输标准对信息端到端时延和网络吞吐量进行不同机间传输距离下的对比仿真实验。其结果表明,该方法满足SPMA协议对于消息传输时延的要求,并且引入SPMA协议后可以有效减少网络数据传输冲突,提高了系统约18%的网络吞吐量,提升了无人机信息交互、资源共享和协同作战的能力,对实际无人机蜂群协同任务调度研究具有可参考性。

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。

一种用户聚集场景下的层级计算卸载策略

现有的边缘计算研究集中于无线资源分配和计算资源分配,而对用户聚集场景下的边缘协同问题研究较少。针对此问题提出一种用户聚集场景下的层级计算卸载策略。该策略采用FSA-MCS解决多重基站信号覆盖下的用户终端卸载节点选择问题;设计一种层级结构的启发式区域计算卸载算法,能够有效提高用户聚集场景下限定时间内完成的任务数量,并减少了用户卸载总时延。实验结果表明,与其他卸载方法相比,该策略的用户任务在限定时间内成功计算完成率最高提高32.7百分点,卸载总时延最高减少43.3%。

响应电网频率的电车充电站分层实时调度

针对充电站内大量电动汽车负荷的实时调度问题,提出了一种分层式的实时调度策略,分别以充电站效益、指导功率追踪以及综合考虑效益和追踪三种方案作为上层优化目标,采用粒子群算法进行寻优得到充电站负荷的优化值.基于上层优化的结果,提出基于用户充电需求紧迫程度并计及电网实时频率的充电功率分配方法为电动汽车制定充电方案.最后通过算例验证了该分层实时调度策略的有效性,能够在满足电动汽车充电需求的基础上,满足充电站与电网的经济运行.

大规模水能富集电网短期多目标嵌套多能互补运行方式研究

在“双碳”的背景下,亟需深入研究如何促进多能互补系统内不同能源之间的协同作用,促进能源深度融合。针对这一问题,在考虑水能富集地区大规模电网不同断面输送电任务的情况下,提出一种区分内外层,逐层递进优化并尽可能消纳风光清洁能源,且兼顾全网源荷匹配程度的多目标嵌套多能互补模型,两层均采用混合整数线性规划算法(MILP)进行求解。内层立足于断面角度,以通道利用率最大化为目标,将水风光打捆进行互补优化,使其出力最大化,尽可能占满通道。外层立足于全网角度,以源荷匹配最大化为目标,采用全网水风光电源联合调节后的剩余负荷波动最小来表达源荷匹配目标,使剩余负荷平稳。本文以西南某水能富集地区大规模电网为研究对象,包含304座水电站,12个断面,选取夏秋丰水期和冬春枯水期风光峰谷差最大日、风光平均发电日共4个典型日进行模拟计算,得到电网丰水期平均通道利用率约为80%,枯水期平均通道利用率约为40%,丰枯期内全网剩余负荷波动率均小于5%。得出以下结论:在夏秋丰水期,水风光系统可为电网提供更多电能,在冬春枯水期,水风光系统出力过程更为稳定。在风光出力平稳时期可为电网提供更多电能,同时使得电网运行更为稳定。本次研究成果对大规模水能富集地区电网多能互补优化调度运行提供参考。

基于差分进化算法和交叉算子的电力企业应急物资多目标分层调度方法

为实现电力系统应急故障的高效抢修,降低故障风险,提出基于差分进化算法和交叉算子的电力企业应急物资多目标分层调度方法。该方法结合电力企业全局调度需求,确定电力企业应急物资多目标上层调度目标函数和下层调度目标函数,同时设计对应的约束条件;采用差分进化算法求解双层调度目标函数,并且为保证解的多样性和算法收敛性,引入进化过程信息优化算法变异算子的变异概率,以此保证目标函数的求解效果。测试结果显示:反世代距离和散布性分别在0.034和0.28以下;结合应急物资供应点位置进行应急物资调配路径规划;应急物资调度的公平性、资源覆盖满意度均在0.92以上;调度后,电力系统的风险固结函数结果均在0.14以下。