A hybrid vendor managed inventory and redundancy allocation optimization problem in supply chain management: An NSGA-II with tuned parameters

In this research, a bi-objective vendor managed inventory model in a supply chain with one vendor (producer) and several retailers is developed, in which determination of the optimal numbers of different machines that work in series to produce a single item is considered. While the demand rates of the retailers are deterministic and known, the constraints are the total budget, required storage space, vendor's total replenishment frequencies, and average inventory. In addition to production and holding costs of the vendor along with the ordering and holding costs of the retailers, the transportation cost of delivering the item to the retailers is also considered in the total chain cost. The aim is to find the order size, the replenishment frequency of the retailers, the optimal traveling tour from the vendor to retailers, and the number of machines so as the total chain cost is minimized while the system reliability of producing the item is maximized. Since the developed model of the problem is NP-hard, the multi-objective meta-heuristic optimization algorithm of non-dominated sorting genetic algorithm-II (NSGA-II) is proposed to solve the problem. Besides, since no benchmark is available in the literature to verify and validate the results obtained, a non-dominated ranking genetic algorithm (NRGA) is suggested to solve the problem as well. The parameters of both algorithms are first calibrated using the Taguchi approach. Then, the performances of the two algorithms are compared in terms of some multi-objective performance measures. Moreover, a local searcher, named simulated annealing (SA), is used to improve NSGA-II. For further validation, the Pareto fronts are compared to lower and upper bounds obtained using a genetic algorithm employed to solve two single-objective problems separately.     

基于非负矩阵分解的水量分配方案后评价

提出了水量分配方案后评价指标体系,建立了基于非负矩阵分解的水量分配方案后评价模型,并以1983年国务院批复的引滦水量分配方案为研究对象进行了水量分配方案诊断式后评价。该方法利用非负矩阵分解算法将指标矩阵最大程度分解为基向量和权向量,根据权向量元素的数值对水量分配方案进行评级,根据基向量元素的数值判断评价指标的实施状况。通过对引滦水量分配方案质量、实施过程、实施效果和可持续性的调查分析,得出方案综合后评价结果为优,但在来用水可持续性、对水环境影响及水质目标保证率等方面需加以改进。评价结果表明,基于非负矩阵分解原理的后评价方法评价效果较好,可为水量分配方案等项目的后评价提供科学依据。     

Modeling and global trajectory tracking control for an over-actuated MAV

This paper deals with an original micro aerial vehicle (MAV) design, the Omnicopter MAV. It has two central coaxial rotors with fixed-pitch propellers and three perimeter mounted ducted fans with servo motors performing thrust vectoring. Compared with traditional rotary wing MAVs that have inherent underactuation, the Omnicopter possesses some advantages in mobility, for example, lateral translation with zero attitude and hover with nonzero attitude. The trajectory tracking control design, global stability analysis, and control allocation are demonstrated through numerical simulation. The advantage of zero attitude translation is illustrated through experimental results.     

Rescheduling policies for large-scale task allocation of autonomous straddle carriers under uncertainty at automated container terminals

This paper investigates replanning strategies for container-transportation task allocation of autonomous Straddle Carriers (SC) at automated container terminals. The strategies address the problem of large-scale scheduling in the context of uncertainty (especially uncertainty associated with unexpected events such as the arrival of a new task). Two rescheduling policies–Rescheduling New arrival Jobs (RNJ) policy and Rescheduling Combination of new and unexecuted Jobs (RCJ) policy–are presented and compared for long-term Autonomous SC Scheduling (ASCS) under the uncertainty of new job arrival. The long-term performance of the two rescheduling policies is evaluated using a multi-objective cost function (i.e., the sum of the costs of SC travelling, SC waiting, and delay of finishing high-priority jobs). This evaluation is conducted based on two different ASCS solving algorithms–an exact algorithm (i.e., branch-and-bound with column generation (BBCG) algorithm) and an approximate algorithm (i.e., auction algorithm)–to get the schedule of each short-term planning for the policy. Based on the map of an actual fully-automated container terminal, simulation and comparative results demonstrate the quality advantage of the RCJ policy compared with the RNJ policy for task allocation of autonomous straddle carriers under uncertainty. Long-term testing results also show that although the auction algorithm is much more efficient than the BBCG algorithm for practical applications, it is not effective enough, even when employed by the superior RCJ policy, to achieve high-quality scheduling of autonomous SCs at the container terminals.     

基于蚁群算法的遥感影像传输资源调度方法

针对遥感影像数据量大,多用户并发请求造成服务器负载加重,使遥感影像传输效率逐渐降低的问题,提出一种在多线服务器环境下分块调度遥感影像资源的策略。该策略采用改进的蚁群优化(IACO)算法,通过引入一个线路等待因子γ动态选择当前最优的线路进行传输,从而提高传输效率。对IACO、ACO、Max-min、Min-min和Random算法进行了对比实验,IACO算法在客户端的任务完成时间和服务器端的执行时间与其他算法相比均是最少的,且随着任务数目的增加,效果更明显;同时IACO算法的线路资源的利用率也更高。仿真结果表明:多线服务器分块调度策略与改进蚁群算法相结合,使遥感影像传输速度和线路资源利用率均有一定提高。     

基于安全性能接口的调度和管理模型研究

以云计算效能提高为目的, 以研究Job调度和资源管理两大决定云计算环境能效的最重要因素为基础, 使用调度和管理对象对作为系统统计信息的参考指标进行采集的方式展开研究。性能代理和服务接口模式(PASI)已经从云环境中采集了原始数据, 通常情况下应用于云的安全技术属于被动防御模式, 如实时动态监控或者深度包检测模式(DPI), 其主要目的是实现动态检测和防御。提出了一个有效的框架以提高云计算中心的综合性能指标, 该架构的建立是基于PASI与DPI相结合的方式, 并且提出了一个基于安全PASI的Job调度和资源管理模型(JDRMSP)。实验结果表明, 该模型有效地提升了云环境的整体运作效能。     

空间资源索引与top-k查询研究

以空间资源索引结构R-tree为基础, 考虑人们对空间资源能力指标的要求, 利用道路网络模型进行空间距离的计算, 提出了一种包含能力维度信息的空间资源索引结构和top-k查询算法, 并与传统遍历算法进行对比实验, 验证了查询算法的有效性。     

一种多接口多信道VANET动态信道分配算法研究

为了合理地实现多接口多信道车载自组织网络（VANET）车辆节点间通信信道的动态分配,提出了一种基于通信双方车辆节点信道切换队列的动态信道分配算法。定义了车辆节点的射频接口状态,给出了信道的性能因子以及信道切换队列的生成方法,通过综合考虑通信双方车辆节点的射频接口状态和信道切换队列,快速自主地建立通信链路,从而解决了信道公平接入和分配不合理的问题。通过软件仿真比较可以看出,该算法实现了信道的动态分配,减小了平均端到端时延,增加了网络平均吞吐量,显著提高了多接口多信道VANET的网络性能。     

随机环境下带有私人信息的单机能力分配策略

本文研究随机环境下考虑私人信息的单机能力分配问题.该问题中各部门的单位产品收益、需求信息及加工设备各时段的能力为私人信息,且各时段中产品的加工时间为随机变量.本文采用设备方与各部门协商的方法对问题进行求解,首先利用随机规划理论将能力分配问题清晰化,然后利用拉格朗日松弛和泰勒级数展开方法进行协商机制设计,接着给出基于偏转次梯度方法的协商参数更新法则,最后综合上述过程给出最终的问题求解算法.数值算例验证了算法的有效性并分析了关键参数对能力分配结果的影响.     

智能电动车辆横纵向协调与重构控制

针对具有高度非线性、强耦合和冗余特性的智能电动车辆运动控制问题,提出了一种由协调控制律和控制分配律组成的横纵向综合控制新方法.首先,建立准确表征智能电动车辆行为机理的动力学模型;其次,采用非奇异滑模控制技术,引入非线性滑动模态切换面,设计有效克服非线性及不确定特性的协调控制律,保证系统状态在有限时间内收敛至平衡点;在此基础上,考虑到轮胎存在冗余和耦合特性,提出基于内点法的控制分配算法来完成期望广义力/力矩的优化分配,实现轮胎横纵向力的协调与重构.仿真结果表明了该方法的有效性.