岸桥移动约束的连续泊位和岸桥集成调度

针对泊位和岸桥集成调度优化未考虑岸桥移动约束限制,造成岸桥频繁移动导致岸桥作业相互干扰、岸桥资源利用率低问题,首先通过拓展现有泊位和岸桥集成调度模型,建立混合整数规划模型实现岸桥分配数量上的合理化,然后以最小化船舶延误成本和岸桥移动成本为目标函数,建立考虑岸桥移动约束限制的泊位和岸桥集成调度的混合整数规划模型.算例表明该模型可以:(1)在靠泊船舶数大于5艘时提高岸桥实际使用效率40%以上;(2)防止因岸桥频繁调动而造成的岸桥作业相互干扰;(3)防止出现在船舶未装卸完之前没有岸桥为其服务的情况;(4)减少由于未限制岸桥频繁移动而带来的额外成本;(5)提高港口码头对岸桥和船舶的管理效率.     

第四方物流企业调度管理优化的模拟退火粒子群算法研究

结合第四方物流的内涵与发展现状,从第四方物流企业调度管理优化决策问题出发,通过引入惯性权重因子和收缩因子对标准粒子群算法进行改进,然后与模拟退火算法相结合,形成改进的模拟退火粒子群(MSA-PSO)算法,求解第四方物流企业调度管理优化问题。在阐述算法原理和运算流程后,分别运用标准粒子群算法、模拟退火算法和本算法对6个基准测试函数进行仿真试验来检验比较算法的性能。结果显示:改进后的模拟退火粒子群算法计算精度和寻优结果优于标准粒子群算法和模拟退火算法。     

车辆网络中基于网络编码机会的调度算法

证明在车辆网络中应用网络编码,其广播容量最大化问题是NP难的;提出一种基于最大编码机会的优化调度算法. 该算法结合网络编码与车辆缓存提供的编码机会,有效提高了广播容量. 结果表明,与贪婪算法相比,该算法获得更大网络编码增益,保证了一定的传输公平性.     

Best Practice Irrigation Management and Extension in Peri-Urban Landscapes – Experiences and Insights from the Hawkesbury–Nepean Catchment,Australia

Purpose: The aim of this article to examine key irrigation management issues and their implications for future research and extension developments.

Design/Methodology/Approach: Peri-urban landscapes are important as they supply fresh fruit, vegetables, turf, ornamental plants and other farm products to the cities. In this study, the Hawkesbury–Nepean Catchment (HNC) was used as a case study and a number of farmers were selected to examine how they use the available technologies for irrigation scheduling to make better decisions for developing water management strategies on their farms. The farmers who participated in the study were mainly growing fruit, turf, vegetables, pasture, olive trees and cut flowers.

Findings: It was observed that a range of factors influenced correct irrigation scheduling decisions of farmers, including their limited understanding of the soil-water-plant relationship, costs of implementing soil-moisture monitoring technology and finding a date for irrigation activity that is compatible with their other farm tasks. It was also found that the farmers’ experiential knowledge played an important role in their irrigation scheduling decisions. The study indicated that irrigation scheduling approaches, which have a low labour requirement and are cost effective and easy to use would be helpful to farmers.

Practical Implications: The study highlighted that there are some irrigation management issues for research and extension that are specific to peri-urban farming and as such they need to be addressed to improve irrigation management and to cope with future water scarcity in urban and peri-urban landscapes.

Originality/Value: This is one of few studies that examine irrigation scheduling issues in a peri-urban context. The study has revealed that irrigation scheduling in reality cannot be an exact science, but it needs to accommodate various farmer constraints in field situations and may have implication for extension activities in peri-urban and other regions.     

Fuzzy flexible resource constrained project scheduling based on genetic algorithm

Both fuzzy temporal constraint and flexible resource constraint are considered in project scheduling. Inorder to obtain an optimal schedule, we propose a genetic algorithm integrated with concepts on fuzzy set theory aswell as specialized coding and decoding mechanism. An example demonstrates that the proposed approach can assistthe project managers to obtain the optimal schedule effectively and make the correct decision on skill training before aproject begins.     

基于改进蚁群算法的QoS约束云任务调度

在研究现有云环境下独立任务和工作流任务调度模型的基础上,提出一种满足QoS约束的部分相关任务调度模型,并改进蚁群算法为每个子群选择信息素更新方法,通过小范围局部优化从而获得整体最优解。CloudSim仿真结果表明,该调度模型具有较高的收敛性和寻优能力,适用于云环境下任务调度。     

基于云计算资源调度的多Agent遗传算法

针对标准遗传算法在云计算资源调度的应用中往往会出现负载不均的问题,本文提出了一种基于云计算资源调度的多Agent遗传算法,首先引入蚁群算法,利用蚂蚁变换方向的影响权重对标准遗传算法的信息素关联进行优化,然后优化了原算法的信息素更新方式,最后利用小生境方法限制种群个体的繁衍,以达到种族多样化的优化。仿真试验结果表明,本文提出的基于云计算资源调度的多Agent遗传算法在CPU利用率和内存利用率上比标准遗传算法具有更优秀的性能。     

引入任务安全性函数分形维的资源均衡算法

资源负载均衡是提高云计算资源调度效率的重要因素,为提高云计算中任务调度的效率,减少执行时间,针对传统的资源均衡算法在没有考虑到云计算环境下的资源节点安全性因素,复杂度高,效果不好的问题,提出一种引入安全性函数分形维特征重组的资源均衡算法。建立任务调度策略及资源均衡模型,构造任务调度所需要时间、费用、可靠性等方面的因素进行特征描述,对负载资源信息流的分形维信息特征提取,通过分层化状态重组,实现对资源负载的均衡处理,得到负载目标函数,提高任务处理效率。实验结果表明,该算法进行Cloud Sim平台下的负载云计算任务调度和负载资源均衡,能有效节省任务执行时间,降低计算开销,执行效率优越于其它算法。     

基于用户使用频率的Linux系统智能化任务调度机制

如今,智能手机的普及率越来越高。在数字化的生活里,人们不仅仅满足于手机的智能化操作,更追求系统运行的速度与效率,追求手机的节能与省电。而在大部分使用智能手机的人群中,以安卓系统为代表的群体占据了相当一部分的比例。本文基于安卓手机的操作系统,对Linux系统的任务调度机制增加了一种全新的功能,实现了系统对特定用户使用进程频率的识别、统计以及高效率的调度。     

分布式产品数据采集动态任务调度模型研究

网络数据采集是大数据时代进行数据挖掘和分析的基础性工作。本文尝试着以任务在不同节点上采集过程中产生的信息作为调度指标制定动态任务调度策略,分别从任务调度策略、任务修改策略和任务回收策略3个角度构建任务调度模型,最后通过实验分析其可行性。实验结果表明,动态任务调度模型能够提高数据采集的效率。