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.     

Consistent hierarchical economic NMPC for a class of hybrid systems using neighboring-extremal updates

A hierarchical two-layer control algorithm is developed for a class of hybrid (discrete-continuous dynamic) systems to support economically optimal operation of batch or continuous processes with a predefined production schedule. For this class of hybrid systems, the optimal control moves as well as the controlled switching times between two adjacent modes are determined online. In contrast to closely related schemes for integrated scheduling and control, the sequence of modes is not optimized. On the upper layer, the economic optimal control problem is solved rigorously by a slow hybrid economic model predictive controller at a low sampling rate. On the lower layer, a fast hybrid neighboring-extremal controller is based on the same economic optimal control problem as the slow controller to ensure consistency between both layers. The fast neighboring-extremal controller updates rather than tracks the optimal trajectories from the upper layer to account for disturbances. Consequently, the fast controller steers the process to its operational bounds under disturbances and the economic potential of the process is exploited anytime. The suggested two-layer control algorithm provides fully consistent control action on the fast and slow time-scale and thus avoids performance degradation and even infeasibilities which are commonly encountered if inconsistent optimal control problems are formulated and solved.     

基于不确定决策树分类算法在滑坡危险性预测的应用

滑坡灾害预测受多种因素影响,其中降雨等不确定因素存在难以获取数据及有效处理等难题,为提高滑坡危险性预测的准确率,根据滑坡灾害发生相关理论及决策树分类原理,提出了基于不确定决策树算法在滑坡危险性预测的方法.该方法引入不确定因子降雨,并将不确定因子和其余评价因子一起,根据不确定决策树算法理论构建出不确定决策树,建立滑坡危险性等级预测模型,并用延安市宝塔区的实例进行验证.实验结果表明,该预测方法取得了较高的总体精度和有效精度,达到了滑坡预测的精度标准,且两项预测精度均高于传统C4.5决策树方法.     

基于改进离散粒子群禁忌算法的并行测试任务调度

针对并行测试任务调度问题特点,提出了一种解决并行测试任务调度的离散粒子群禁忌算法,设计了符合并行测试任务调度特点的粒子初始化编码方式、粒子位置转移公式以及禁忌搜索邻域。通过与禁忌搜索相结合,避免了离散粒子群算法早熟问题。通过实验以及算法对比,表明提出的算法是快速有效的,能够很好地解决并行测试任务调度问题,求得测试总时间最短的任务调度序列。     

一种新型可变径管道机器人的结构设计与控制实现

基于ATmega128单片机的管道探测机器人系统,包括硬件系统设计以及软件程序实现。为满足不同工业环境的需要,设计了无线与RS485总线两套通信方案进行人机交互控制系统与单片机的通信。独特的变径结构可以让机器人适应不同管径的管道,配合高效的电池切换算法,可以使其更好的适应工业环境。为了观察管道内部信息,安装了一个可360°旋转的摄像头,将管道内部画面实时传送给上位机。     

基于改进遗传算法的星地任务优化调度研究

星地任务优化调度是利用特定的星地资源合理地安排星地任务。由于星地任务众多而资源有限,而且星地任务受星地可见性以及多方面约束,星地任务调度问题十分复杂。针对星地任务的特点,建立了星地任务调度问题模型,提出了基于改进遗传算法的星地任务优化调度算法。算法采用按适应度排名轮盘赌选择、顺序交叉、随机对换变异的算法要素。针对遗传算法局部搜索能力弱的特点,提出了利用爬山算法优化新一代个体的方法,以增强遗传算法的局部搜索能力,给出了基于改进遗传算法的星地任务调度算法。     

一种集中式水下无线传感器网跨层调度协议

分析了水下无线传感器网络（WSNs）中单链路发射功率和距离与频率的关系,提出一种跨层的集中式调度方法,AP（access point）在网络中通过广播信标的形式收集估计各节点的时延和距离信息,在MAC层考虑水下链路延迟特点,对各链路进行调度减少冲突;节点在物理层通过自适应地改变节点发射功率和发射频率,减少能耗.仿真结果表明：所提方案在节省系统能量消耗的同时,能够减少碰撞,提高系统吞吐量.     

鲁棒性项目调度中缓冲设置方法

研究应对不确定因素的鲁棒性项目调度, 针对其所用时间缓冲区技术中的两种重要方法——集中缓冲和STC (starting time criticality) 分散缓冲, 采用仿真模拟实验, 以项目管理库中的帕特森例1 为对象, 进行详细的比较研究. 研究结果表明, 分散缓冲法具有更好的解鲁棒性; 当活动时间的不确定性程度较大时, 集中缓冲法的质量鲁棒性较好; 当项目工期较紧时, 分散缓冲法具有更好的鲁棒性. 这些研究结论为管理者在特定的项目环境下选择合适的缓冲方法提供了决策依据.     

超声波传感器在沙子料位检测中的应用

为提高混凝土的质量,配比很重要,其中沙子精确计量是关键因素之一。提出并设计了超声波系统测量沙子料位;为保证测量精度,采取自动增益控制电路和温度补偿。经实验证明:该方法简单、可行,为沙子质量的检测提供了一种新的参考方法。