两阶段多供应商选择采购模型

针对如何从众多供应商中选择出适合需求的供货商进行准时采购这一问题,提出两阶段多供应商选择采购模型。利用层次分析方法对各个供应商按照定性准则进行分析评价,利用定量准则所建立的多供应商选择采购的成本优化模型对初步选择的供应商做进一步选择,从而确定最终的供货商及供货数量。实际应用结果表明,该模型不但能使企业选择出符合要求的供货商,而且能使企业降低采购成本和产品成本。     

低成本单伺服注塑机械手控制系统设计

在注塑行业,注塑机械手已经逐渐替代人工操作并发挥着重要作用,而注塑机械手的精度又直接影响到注塑机的工艺和注塑件的质量.针对目前注塑机械手存在部分问题的现状,设计了一种基于STM32单片机的高精度低成本的注塑机械手控制系统,并对其硬件系统和软件系统做了详细的介绍.经过实验测试,注塑机械手达到了预期的功能和控制精度,但是其价格相比工业上的注塑机机械手降低了30％.     

不确定数据聚类的U-PAM算法和UM-PAM算法的研究

UK-means算法在处理不确定数据时对孤立点非常敏感,而且事先必须已知不确定数据的分布函数或概率密度,然而这在实际中往往很难获得。因此,针对UK-means在处理不确定测量数据时的不足,首先提出了基于区间数的PAM不确定聚类算法——U-PAM,该算法用区间数和标准差合理地描述了不确定测量数据的不确定性,进而完成有效的聚类;其次,针对海量不确定测量数据难以聚类的问题,基于U-PAM聚类算法,采用抽样技术提出了处理海量不确定测量数据的算法——UM-PAM算法,该算法先抽样,对样本数据聚类,然后再总体聚类;最后,基于U-PAM算法和CH聚类的有效性指标函数对聚类结果进行分析,以确定最佳聚类数。实验理论表明,所提算法聚类效果明显。     

基于联系数的位置不确定性数据UCNK-Means聚类算法

摘要位置不确定性数据的聚类是一个新的不确定性数据聚类问题。其聚类方法主要包括获取对象的概率密度函数,通过积分计算对象间的期望距离来进行聚类分析和以区间数表示对象,通过区间数的系列运算来进行聚类分析这两大类。前者存在概率密度函数获取困难、计算复杂、实用性不强的缺陷;后者在区间数转化为实数过程中,忽略了区间数变化范围对聚类效果的影响,其聚类质量不佳。鉴于此,提出一种基于联系数的不确定对象聚类新算法UCNK-Means。该算法用联系数巧妙地表示不确定性对象,并专门定义了对象间的联系距离,运用联系数态势值比较联系距离大小,克服了现有算法的不足。仿真实验表明,UCNK-Means具有聚类精度高、计算复杂度低、实用性强的特点。     

Optimal replacement policy for a two-dissimilar-component cold standby system with different repair actions

In this paper, a cold standby repairable system consisting of two dissimilar components and one repairman is studied. When failures occur, the repair of both component 1 and component 2 are not ‘as good as new’. The consecutive operating times of component 1 after repair constitute a decreasing geometric process, while the repair times of component 1 are independent and identically distributed. For component 2, its failure is rectified by minimal repair, and the repair time is negligible. Component 1 has priority in use when both components are good. The replacement policy N is based on the failure number of component 1. Under policy N, we derive the explicit expression of the long-run average cost rate C(N) as well as the average number of repairs of component 2 before the system replaced. The optimal replacement policy N*, which minimises the long-run average cost rate C(N), is obtained theoretically. If the failure rate r(t) of component 2 is increasing, the existence and uniqueness of the optimal policy N* is also proved. Finally, a numerical example is given to validate the developed theoretical model. Some sensitivity analyses are provided to show the influence of some parameters, such as the costs for replacement and repair, and the parameters of the lifetime and repair time distributions of both components, to the optimal replacement policy N* and corresponding average cost rate C(N*).     

Optimal number of minimal repairs with cumulative repair cost limit for a two-unit system with failure rate interactions

A discrete replacement model is presented that includes a cumulative repair cost limit for a two-unit system with failure rate interactions between the units. We assume a failure in unit 1 causes the failure rate in unit 2 to increase, whereas a failure in unit 2 causes a failure in unit 1, resulting in a total system failure. If unit 1 fails and the cumulative repair cost till to this failure is less than a limit L, then unit 1 is repaired. If there is a failure in unit 1 and the cumulative repair cost exceeds L or the number of failures equals n, the entire system is preventively replaced. The system is also replaced at a total failure, and such replacement cost is higher than the preventive replacement cost. The long-term expected cost per unit time is derived using the expected costs as the optimality criterion. The minimum-cost policy is derived, and existence and uniqueness are proved.     

A review on cost allocation methods in collaborative transportation

Collaboration in transportation between two or more agents is becoming an important approach to find efficient solutions or plans. Efficiency can be measured in, for example, lower cost or more flexibility. An important aspect of the collaboration is to decide on how to share the benefits—for example, cost, profit, or resources. There are many sharing mechanisms or cost allocations proposed in the literature. Some are based on simple proportional rules and others are based on theoretical concepts found in game theory. We provide a survey on cost allocation methods found in the literature on collaborative transportation, including problems on planning, vehicle routing, traveling salesman, distribution, and inventory. A total of 55 scientific articles compose the main part of the survey, most of them published between 2010 and 2015. We identify more than 40 cost allocation methods used in this stream of literature. We describe the theoretical basis for the main methods as well as the cases where they are used. We also report savings from the collaborations when they are based on industrial data. Some directions for future research are discussed.     

基于移动—能量代价函数的无线自组织网络路由策略研究

能量均衡技术一直是无线自组织网络的热点研究领域.在深入研究网络信息传输特性的基础上,提出了一种基于移动-能量代价函数的无线自组织网络路由策略,并用于网络信息传输.首先,本文考虑节点连通性、能量均衡性,提出了一种节点移动策略;然后,以传输路径节点集合中的瓶颈节点剩余能量、传输链路数量作为准则,建立以网络节点为对象的能量代价函数.基于移动-能量代价函数的路由策略从链路层的决策转移到节点层的决策.最后,采用MATLAB数值仿真该路由策略的性能,结果显示:本文提出的移动-能量代价函数的路由策略既保持了原有路由优化的精度,延迟网络瓶颈节点能量下降速度,提高网络生存时间.     

An adaptive learning regulator for uncertain minimum phase systems with undermodeled unknown exosystems

The design of an adaptive learning regulator is addressed for uncertain minimum phase linear systems (with known bounds, known upper bound on system order, known relative degree, known high frequency gain sign) and for unknown exosystems (with unknown order, uncertain frequencies). On the basis of a known bound on system uncertainties and a known bound on the modeled exosystem frequencies, a new adaptive output error feedback control algorithm is proposed which guarantees exponential convergence of both the output and the control input errors into residual bounds which decrease as the exosystem modeling error decreases. Exponential convergence of both errors to zero is obtained when the regulator exactly models all exosystem excited frequencies, while asymptotic convergence of both errors to zero is achieved when the actual exosystem is overmodeled by the regulator. The new algorithm generalizes existing learning controllers since, in the case of periodic references and/or disturbances, the knowledge of the period is not required.     

Stabilization for T-S model based uncertain stochastic systems

This paper considers the stabilization problem of a class of uncertain Itô stochastic fuzzy systems driven by a multidimensional Wiener process. The uncertainty modeled in the systems is of the linear fractional type which includes the norm-bounded uncertainty as a special case. The objective is to design a state-feedback fuzzy controller such that the closed-loop system is robustly asymptotically stable under a stochastic setting. By using a stochastic Lyapunov approach, sufficiency conditions for the stability and stabilization of this class of systems are established based on a novel matrix decomposition technique. The derived stability conditions are then employed to design controllers which stabilize the uncertain Itô stochastic fuzzy systems. Two simulation examples are given to illustrate the effectiveness of the approaches proposed.