Energy-balanced multiple-sensor collaborative scheduling for maneuvering target tracking in wireless sensor networks

An energy-balanced multiple-sensor collaborative scheduling is proposed for maneuvering target tracking in wireless sensor networks (WSNs). According to the position of the maneuvering target, some sensor nodes in WSNs are awakened to form a sensor cluster for target tracking collaboratively. In the cluster, the cluster head node is selected to implement tracking task with changed sampling interval. The distributed interactive multiple model (IMM) filter is employed to estimate the target state. The estimation accuracy is improved by collaboration and measurement information fusion of the tasking nodes. The balanced distribution model of energy in WSNs is constructed to prolong the lifetime of the whole network. In addition, the communication energy and computation resource are saved by adaptively changed sampling intervals, and the real-time performance is satisfactory. The simulation results show that the estimation accuracy of the proposed scheme is improved compared with the nearest sensor scheduling scheme (NSSS) and adaptive sensor scheduling scheme (ASSS). Under satisfactory estimation accuracy, it has better performance in saving energy and energy balance than the dynamic collaborative scheduling scheme (DCSS).

A Novel Collaborative Evolutionary Algorithm with Two-Population for Multi-Objective Flexible Job Shop Scheduling

Job shop scheduling(JS)is an important technology for modern manufacturing.Flexible job shop scheduling(FJS)is critical in JS,and it has been widely employed in many industries,including aerospace and energy.FJS enables any machine from a certain set to handle an operation,and this is an NP-hard problem.Furthermore,due to the requirements in real-world cases,multi-objective FJS is increasingly widespread,thus increasing the challenge of solving the FJS problems.As a result,it is necessary to develop a novel method to address this challenge.To achieve this goal,a novel collaborative evolutionary algorithmwith two-population based on Pareto optimality is proposed for FJS,which improves the solutions of FJS by interacting in each generation.In addition,several experimental results have demonstrated that the proposed method is promising and effective for multi-objective FJS,which has discovered some new Pareto solutions in the well-known benchmark problems,and some solutions can dominate the solutions of some other methods.

Solving flexible job shop scheduling problem by a multi-swarm collaborative genetic algorithm

The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborative optimization algorithm is proposed for the FJSP.Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA.Good operators are adopted and designed to ensure this algorithm to achieve a good performance.Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA.The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms.

Edge-Cloud Collaborative Optimization Scheduling with Micro-Service Architecture

The architecture of edge-cloud cooperation is proposed as a compromising solution that combines the advantage of MEC and central cloud. In this paper we investigated the problem of how to reduce the average delay of MEC application by collaborative task scheduling. The collaborative task scheduling is modeled as a constrained shortest path problem over an acyclic graph. By characterizing the optimal solution, the constrained optimization problem is simplified according to one-climb theory and enumeration algorithm. Generally, the edge-cloud collaborative task scheduling scheme performance better than independent scheme in reducing average delay. In heavy workload scenario, high blocking probability and retransmission delay at MEC is the key factor for average delay. Hence, more task executed on central cloud with abundant resource is the optimal scheme. Otherwise, transmission delay is inevitable compared with execution delay. MEC configured with higher priority and deployed close to terminals obtain more performance gain.

Cost-Effective Task Scheduling for Collaborative Cross-Edge Analytics

Collaborative cross-edge analytics is a new computing paradigm in which Internetof Things (IoT) data analytics is performed across multiple geographically dispersededge clouds. Existing work on collaborative cross-edge analytics mostly focuses on reducingeither analytics response time or wide-area network (WAN) traffic volume. In thiswork, we empirically demonstrate that reducing either analytics response time or networktraffic volume does not necessarily minimize the WAN traffic cost, due to the price heterogeneityof WAN links. To explicitly leverage the price heterogeneity for WAN cost minimization,we propose to schedule analytic tasks based on both price and bandwidth heterogeneities.Unfortunately, the problem of WAN cost minimization underperformance constraintis shown non-deterministic polynomial (NP)-hard and thus computationally intractablefor large inputs. To address this challenge, we propose price- and performanceawaregeo-distributed analytics (PPGA) , an efficient task scheduling heuristic that improvesthe cost-efficiency of IoT data analytic jobs across edge datacenters. We implementPPGA based on Apache Spark and conduct extensive experiments on Amazon EC2to verify the efficacy of PPGA.

Cyber-physical Collaborative Restoration Strategy for Power Transmission System Considering Maintenance Scheduling

In order to improve the ability of power transmission system to cope with compound faults on the communication side and power side,a cyber-physical collaborative restoration strategy is proposed.First,according to the information system’s role in fault diagnosis,remote control of equipment maintenance and automatic output adjustment of generator restoration,a cyber-physical coupling model is proposed.On this basis,a collaborative restoration model of power transmission system is established by studying interactions among maintenance schedule paths,information system operation,and power system operation.Based on power flow linearization and the large M-ε method,the above model is transformed into a mixed integer linear programming model,whose computational burden is reduced further by the clustering algorithm.According to the parameters of IEEE39 New England system,the geographic wiring diagram of the cyber-physical system is established.Simulation results show the proposed restoration strategy can consider the support function of the information system and space-time coordination of equipment maintenance at both sides comprehensively to speed up load recovery progress.

Mathematical Modeling and a Multiswarm Collaborative Optimization Algorithm for Fuzzy Integrated Process Planning and Scheduling Problem

Considering both process planning and shop scheduling in manufacturing can fully utilize their complementarities,resulting in improved rationality of process routes and high-quality and efficient production. Hence,the study of Integrated Process Planning and Scheduling (IPPS) has become a hot topic in the current production field. However,when performing this integrated optimization,the uncertainty of processing time is a realistic key point that cannot be neglected. Thus,this paper investigates a Fuzzy IPPS (FIPPS) problem to minimize the maximum fuzzy completion time. Compared with the conventional IPPS problem,FIPPS considers the fuzzy process time in the uncertain production environment,which is more practical and realistic. However,it is difficult to solve the FIPPS problem due to the complicated fuzzy calculating rules. To solve this problem,this paper formulates a novel fuzzy mathematical model based on the process network graph and proposes a MultiSwarm Collaborative Optimization Algorithm (MSCOA) with an integrated encoding method to improve the optimization. Different swarms evolve in various directions and collaborate in a certain number of iterations. Moreover,the critical path searching method is introduced according to the triangular fuzzy number,allowing for the calculation of rules to enhance the local searching ability of MSCOA. The numerical experiments extended from the well-known Kim benchmark are conducted to test the performance of the proposed MSCOA. Compared with other competitive algorithms,the results obtained by MSCOA show significant advantages,thus proving its effectiveness in solving the FIPPS problem.

Quantitative evaluation of multi-process collaborative operation in steelmaking–continuous casting sections

The quantitative evaluation of multi-process collaborative operation is of great significance for the improvement of production planning and scheduling in steelmaking–continuous casting sections(SCCSs). However, this evaluation is difficult since it relies on an in-depth understanding of the operating mechanism of SCCSs, and few existing methods can be used to conduct the evaluation, due to the lack of full-scale consideration of the multiple factors related to the production operation. In this study, three quantitative models were developed, and the multiprocess collaborative operation level was evaluated through the laminar-flow operation degree, the process matching degree, and the scheduling strategy availability degree. Based on the evaluation models for the laminar-flow operation and process matching levels, this study investigated the production status of two steelmaking plants, plants A and B, based on actual production data. The average laminar-flow operation(process matching) degrees of SCCSs were obtained as 0.638(0.610) and 1.000(0.759) for plants A and B, respectively, for the period of April to July 2019. Then, a scheduling strategy based on the optimization of the furnace-caster coordinating mode was suggested for plant A. Simulation experiments showed higher availability than the greedy-based and manual strategies. After the proposed scheduling strategy was applied,the average process matching degree of the SCCS of plant A increased by 4.6% for the period of September to November 2019. The multi-process collaborative operation level was improved with fewer adjustments and interruptions in casting.

Bilateral Collaborative Optimization for Cloud Manufacturing Service

Manufacturing service composition of the supply side and scheduling of the demand side are two important components of Cloud Manufacturing,which directly affect the quality of Cloud Manufacturing services.However,the previous studies on the two components are carried out independently and thus ignoring the internal relations and mutual constraints.Considering the two components on both sides of the supply and the demand of Cloud Manufacturing services at the same time,a Bilateral Collaborative Optimization Model of Cloud Manufacturing(BCOM-CMfg)is constructed in this paper.In BCOM-CMfg,to solve the manufacturing service scheduling problem on the supply side,a new efficient manufacturing service scheduling strategy is proposed.Then,as the input of the service composition problem on the demand side,the scheduling strategy is used to build the BCOM-CMfg.Furthermore,the Cooperation Level(CPL)between services is added as an evaluation index in BCOM-CMfg,which reveals the importance of the relationship between services.To improve the quality of manufacturing services more comprehensively.Finally,a Self-adaptive Multi-objective Pigeon-inspired Optimization algorithm(S-MOPIO)is proposed to solve the BCOM-CMfg.Simulation results show that the BCOM-CMfg model has advantages in reliability and cost and S-MOPIO can solve BCOM-CMfg effectively.

Multi-Agent System for Real Time Planning Using Collaborative Agents

Autonomous agents are an important area of research in the sense that they are proactive, and include: goal-directed and communication capabilities. Furthermore each goals of the agent are constantly changing in a dynamic environment. Part of the challenge is to automate the process corresponding to each agent in order that they find their own objectives. Agents do not have to work individually, but can work with others and develop a coordinated group of actions. These agents are highly appreciated, when real time problems are involved, meaning that an agent must be able to react within a specific time interval, considering external events. Our work focuses on the design of a multi-agent architecture consisting of autonomous agents capable of acting through a goal-directed with: a) constraints, b) real-time, and c) with incomplete knowledge of the environment. This paper shows a model of collaborative agents architecture that share a common knowledge source, allowing knowledge of the environment;where we analyze it and its changes, choosing the most promising way for achieving the goals of the agent, in order to keep the whole system working, even if a fault occurs.

Sensor Scheduling Algorithm Target Tracking-Oriented

Target tracking is a challenging problem for wireless sensor networks because sensor nodes carry limited power recourses. Thus, scheduling of sensor nodes must focus on power conservation. It is possible to extend the lifetime of a network by dynamic clustering and duty cycling. Sensor Scheduling Algorithm Target Tracking-oriented is proposed in this paper. When the target occurs in the sensing filed, cluster and duty cycling algorithm is executed to scheduling sensor node to perform taking task. With the target moving, only one cluster is active, the other is in sleep state, which is efficient for conserving sensor nodes’ limited power. Using dynamic cluster and duty cycling technology can allocate efficiently sensor nodes’ limited energy and perform tasks coordinately.

Collaborative Clustering Parallel Reinforcement Learning for Edge-Cloud Digital Twins Manufacturing System

To realize high-accuracy physical-cyber digital twin(DT)mapping in a manufacturing system,a huge amount of data need to be collected and analyzed in real-time.Traditional DTs systems are deployed in cloud or edge servers independently,whilst it is hard to apply in real production systems due to the high interaction or execution delay.This results in a low consistency in the temporal dimension of the physical-cyber model.In this work,we propose a novel efficient edge-cloud DT manufacturing system,which is inspired by resource scheduling technology.Specifically,an edge-cloud collaborative DTs system deployment architecture is first constructed.Then,deterministic and uncertainty optimization adaptive strategies are presented to choose a more powerful server for running DT-based applications.We model the adaptive optimization problems as dynamic programming problems and propose a novel collaborative clustering parallel Q-learning(CCPQL)algorithm and prediction-based CCPQL to solve the problems.The proposed approach reduces the total delay with a higher convergence rate.Numerical simulation results are provided to validate the approach,which would have great potential in dynamic and complex industrial internet environments.

计及UPFC最优配置的电力系统鲁棒调度协同优化策略

统一潮流控制器(UPFC)应用于潮流调控时,计及UPFC调控参数的交流潮流计算是非凸、非线性问题,且多台装置间的非线性交叉耦合特性也会直接影响优化配置方案。为此,基于UPFC的潮流调控特性,构建了计及UPFC的松弛型交流潮流二阶锥规划模型;计及风电的不确定性,协同考虑UPFC的规划和电力系统的调度问题,建立了计及UPFC最优配置的电力系统鲁棒协同优化模型,并采用列和约束生成算法进行求解。以IEEE RTS-24节点系统为算例进行仿真分析,结果表明所提协同优化策略有效提升了UPFC配置方案的适应性,提高了系统运行经济性和风电消纳能力,增强了系统运行调控的灵活性。

考虑柔性设备加工能力的综合调度算法

现有柔性综合调度研究中,没有考虑设备系统的协同加工能力,从而降低了设备系统高密加工和快速加工能力。针对此问题,文章将柔性设备可进行加工的工序数作为优化对象、以竞争资源较为紧张的设备资源为优化要素,提出了考虑柔性设备加工能力的综合调度算法(ISA-CPCFE):首先,采用优先调度层级较高与加工时长较短的工序的策略,提高了工序并行调度的力度;其次,提出一种最小化调度标尺与动态调整柔性设备优先级的策略,进一步提高了设备紧凑调度的力度。最后,将ISA-CPCFE算法与基于剪枝分层、基于设备驱动、基于实际路径、基于逆序层优先的算法进行对比实验。结果表明:ISA-CPCFE算法实现了复杂产品加工时间更短、柔性设备系统整体利用率更高的优化目标,调度效果更优。

考虑源-荷协同风险的水光互补系统日前优化调度

光电高比例渗透阶段会加剧水光互补系统的源-荷协同难度,并引发弃电风险。由此提出一种考虑源-荷协同日前优化调度方法。首先基于波形分段与出力分区提取光伏出力定性特征,引入耦合云生成光电不确定性场景;然后选取波动量、弃电量评价指标建立日前调度模型,采用波形分阶补偿策略保障负荷跟踪能力与控制弃电风险,并编制发电计划;最后根据实际案例的场景集合求解系统实时运行过程。结果表明:耦合云模型能够在波形与出力两个维度模拟不同鲁棒保守度下的光电不确定性;相对于常规确定性优化,考虑源-荷协同日前优化调度方法能够充分发挥水电与水库调节的灵活性,具有更强的风险承担能力,且输电形式满足源-荷协同要求,可以有效规避弃电风险。

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。

星地融合边云协同网络下的资源调度研究

通过边缘计算和云计算的优势互补,面向6G的星地融合网络能够实现资源的弹性分配和协同利用,满足数据处理的实时性和智能化要求。为了提升资源服务能力和用户体验质量,提出了星地融合边云协同网络架构。首先,阐述了三种不同边云协同模式下的资源调度方案,从时延、能耗和多目标优化的角度分析了不同场景下的资源调度策略。然后,对比了现有资源调度求解模型和算法的优势和局限性。最后,对基于边云协同的星地融合网络中的资源调度研究进行了总结与展望。

面向任务协同的异构多核嵌入式系统实时调度方法

以往的异构多核嵌入式系统实时调度方法由于仅设置了系统实时调度模型的单项参数,导致系统调度时间过长。本文设计了面向任务协同的异构多核嵌入式系统实时调度方法。面向任务协同构建嵌入式系统实时调度模型,计算系统任务节点的传输情况,构建系统实时调度的数学模型,对构建的实时调度模型中的任务数据信息进行汇集,并进行自适应分析处理,以此为基础,设置实时调度模型的时间均衡控制参数和安全性系数,从而实现嵌入式系统的实时调度。通过上述设计,完成对异构多核嵌入式系统实时调度方法的设计。在仿真实验中,与以往的异构多核嵌入式系统实时调度方法相比,本文设计的面向任务协同的异构多核嵌入式系统实时调度方法的调度时间最长仅为5 s,调度时间更短。

考虑阶梯式碳交易机制的电化学储能与抽水蓄能协同调度优化

为了验证电化学储能与抽水蓄能协同调度下参与电力市场与碳市场的可行性,构建了包含火力发电、风力发电、光伏发电、抽水蓄能以及电化学储能的多能互补系统。考虑平准化度电成本(LCOE)和碳排放成本,以购能成本、LCOE、碳排放成本及弃风弃光成本之和最小为目标函数,调用MATLAB的Cplex求解器,进行调度优化求解。以西北某区域电网夏季典型日的数据为例,引入分时上网电价及阶梯碳交易机制,设置不同储能系统参与、不同碳交易机制等6种场景。结果表明:抽水蓄能与电化学储能协同调度下的电力市场收益和碳市场收益最高,系统碳排放量最低,验证了所提出的储能协同配置方案及调度优化方案的可行性。

自动分拣仓库中多载量AGV调度与路径规划算法

在自动分拣仓库中,多载量自动导引小车(AGV)具有强运输能力,但其多载量特征也增加了调度与路径规划的复杂性。针对多载量AGV调度与路径规划的协同优化问题,以最小化最大搬运完成时间为目标,建立了该问题的混合整数线性规划模型,并提出一种聚类协同优化算法。算法首先定义了包裹相似度,设计聚类算法划分包裹组,使每个包裹组可由多载量AGV在一次作业中完成分拣;进而针对问题的多决策特征,设计协同进化遗传算法对包裹组进行指派和排序,并将无冲突路径规划算法引入到协同进化遗传算法的解码方案中,用以搜索最优路径并解决多AGV路径冲突,从而实现了多载量AGV调度与路径规划的协同优化。通过不同问题规模的仿真实验验证了所提算法的高效性和稳定性。