基于可行空间并行的安全约束最优潮流

为了提高电网控制决策的实时性,文中将计及预想故障安全约束的经济性优化问题分解为以发电成本最小为目标的基态最优潮流主问题和预想故障安全校核子问题,并提出一种基于可调空间切片并行的分解协调算法进行求解,以避免交替迭代。首先,按不同比例对可调空间进行切片形成多个切片方案,并基于并行计算平台采用原始-对偶内点法对各切片方案进行基态最优潮流主问题求解;然后,对各优化后方式进行预想故障并行安全校核;接着从通过安全校核的切片方案中挑选出发电成本最小的方案;最后,以贵州省某市电网为算例分析验证了所提方法的有效性。     

微电网跟踪调度计划双层双时间尺度实时控制策略

为了提升微电网跟踪调度计划的能力,并降低分布式电源和负荷的功率波动对跟踪效果的影响,提出一种利用由超级电容和电池储能组成的混合储能系统跟踪调度计划的双层双时间尺度实时控制策略。上层基于模型预测控制方法建立日内滚动优化模型,结合分布式电源和负荷的超短期功率预测结果,综合考虑一段时域内的跟踪偏差及混合储能系统的功率和荷电状态对跟踪调度计划进行滚动优化;下层在上层优化结果的基础上,采用基于荷电状态的混合储能系统实时控制策略对超级电容和电池储能的实时功率进行协调分配,进一步降低分布式电源和负荷的实时功率波动对跟踪效果的影响。通过仿真算例对所提控制策略进行验证,结果表明:所提策略不但实现了良好的实时跟踪调度计划的控制效果,而且优化了超级电容和电池储能的荷电状态。     

计及风电置信风险的源网协调多目标优化调度

为了更为合理灵活地评估风电高估/低估给电力系统优化调度带来的风险性以及降低调度决策的保守性,基于风电机会约束提出风电高估/低估置信风险功率偏差量化计算方法,并在决策变量中引入变压器变比调节和无功补偿容量优化,构建计及风电置信风险和源网协调运行的经济/风险多目标优化调度模型。提出一种基于可行性和非劣性综合排序回溯搜索算法,从而实现对调度模型的高效准确求解。IEEE 30节点系统算例结果验证了所提方法的有效性和优越性。     

基于模型的子空间聚类与时间段蚁群算法的合同生产批量调度方法

针对目前冷轧薄板厂生产流程复杂、大量的多品种小批量合同并线生产,导致难以制定生产计划的问题,本文提出了混合模型子空间聚类（Subspace clustering mixed model,SCMM）方法,以合同中待加工钢卷的宽度、冷轧机组的入口厚度、 出口厚度以及合同的交货期为约束,对待生产合同进行组批. 依据冷轧厂实际生产过程,将冷轧机组视为核心节点,考虑准时交货、 在制品库存和生产流向产能分配的要求,对组批后的生产合同建立全流程合同计划模型,并且利用提出的时间段蚁群算法（Time-section ant colony optimization,TSA）,制定合同计划.利用生产过程的实际数据测试,本文的方法优于人工排产,可以满足制定冷轧薄板全流程生产计划的要求.     

Single-machine parallel-batching scheduling with family jobs to minimize weighted number of tardy jobs

We consider the problem of scheduling n jobs in batches on a single parallel-batching machine, where the jobs are partitioned into jobs families and the jobs in each family have the same due date. The objective is to minimize the weighted number of tardy jobs. We first devise an efficient pseudo-polynomial time and a fully polynomial time approximation scheme for the weighted problem. Then we present $O(n^2)$-time and $O(n\log n)$-time algorithms for the case where the jobs have the same weight and for the case where the jobs have the same processing time, respectively.     

Efficient fault-tolerant collision-free data aggregation scheduling for wireless sensor networks

This paper investigates the design of fault-tolerant TDMA-based data aggregation scheduling (DAS) protocols for wireless sensor networks (WSNs). DAS is a fundamental pattern of communication in wireless sensor networks where sensor nodes aggregate and relay data to a sink node. However, any such DAS protocol needs to be cognisant of the fact that crash failures can occur. We make the following contributions: (i) we identify a necessary condition to solve the DAS problem, (ii) we introduce a strong and weak version of the DAS problem, (iii) we show several impossibility results due to the crash failures, (iv) we develop a modular local algorithm that solves stabilising weak DAS and (v) we show, through simulations and an actual deployment on a small testbed, how specific instantiations of parameters can lead to the algorithm achieving very efficient stabilisation.     

Competitive online adaptive scheduling for sets of parallel jobs with fairness and efficiency

We study online adaptive scheduling for multiple sets of parallel jobs, where each set may contain one or more jobs with time-varying parallelism. This two-level scheduling scenario arises naturally when multiple parallel applications are submitted by different users or user groups in large parallel systems, where both user-level fairness and system-wide efficiency are of important concerns. To achieve fairness, we use the well-known equi-partitioning algorithm to distribute the available processors among the active job sets at any time. For efficiency, we apply a feedback-driven adaptive scheduler that periodically adjusts the processor allocations within each set by consciously exploiting the jobs’ execution history. We show that our algorithm achieves asymptotically competitive performance with respect to the set response time, which incorporates two widely used performance metrics, namely, total response time and makespan, as special cases. Both theoretical analysis and simulation results demonstrate that our algorithm improves upon an existing scheduler that provides only fairness but lacks efficiency. Furthermore, we provide a generalized framework for analyzing a family of scheduling algorithms based on feedback-driven policies with provable efficiency. Finally, we consider an extended multi-level hierarchical scheduling model and present a fair and efficient solution that effectively reduces the problem to the two-level model.     

许昌市水资源多模式联合调度与合理配置

水资源短缺是制约城市经济社会发展的重要因素,合理配置和有效利用有限的水资源是解决水资源供需矛盾的有效途径。以许昌市国家水生态文明试点城市建设为例,在分析水资源开发利用现状的基础上,运用定额法对需水量进行预测。以多年平均水资源量为基础,2011年作为研究基准年,根据水资源取用方式、工程方案及各种水量需求等,利用MIKE BASIN建立水资源配置模型,对许昌市水资源供需平衡进行模拟,通过三次供需平衡分析,得到不同规划水平年水资源供需配置方案,最终达到水资源供需平衡,使有限的水资源发挥最大效益。研究结果表明,本地水资源结合南水北调、引黄调蓄、中水回用等多模式联合调度,可以满足许昌市中长期经济社会可持续发展用水需求,对未来许昌市水资源可持续利用具有重要的指导意义。     

Optimisation of partner selection and collaborative transportation scheduling in Virtual Enterprises using GA

Partner selection and transportation scheduling are critical to the success of a Virtual Enterprise. Collaborative transportation is a promising strategy that can help many enterprises survive and thrive in today’s highly competitive market. To help decision makers establish and operate Virtual Enterprises more effectively, an innovative decision support system is proposed in this paper. First, new model for integration of partner selection and collaborative transportation scheduling in Virtual Enterprises is developed. This integrated optimisation problem is very dynamic in nature and it is required to optimise a number of interlinked sub-problems at the same time. Then, a novel Genetic Algorithm with a unique dynamic chromosome representation and genetic operations is developed to find an optimal solution to the integrated problem. The effectiveness of the proposed approach is demonstrated in a representative case study.     

An analytical framework for integrated maritime terminal scheduling problems with time windows

Container transport, an integral part of intercontinental trade, has steadily increased over the past few decades. The productivity of such a system, in part, hinges on the efficient allocation of terminal resources such that the container transit time is minimized. This study provides an analytical framework, which entails efficient scheduling of quay and yard cranes, to minimize the time spent by containers at a terminal. A mixed-integer programming model is developed to capture the two-stage multi-processor characteristic of the problem, where each crane has specific time window availability. A genetic algorithm equipped with a novel decoding procedure is developed. The mixed-integer model is tested on a number of problem instances of varying sizes to gain managerial insights.