Approximation algorithms for general one‐warehouse multi‐retailer systems

Logistical planning problems are complicated in practice because planners have to deal with the challenges of demand planning and supply replenishment, while taking into account the issues of (i) inventory perishability and storage charges, (ii) management of backlog and/or lost sales, and (iii) cost saving opportunities due to economies of scale in order replenishment and transportation. It is therefore not surprising that many logistical planning problems are computationally difficult, and finding a good solution to these problems necessitates the development of many ad hoc algorithmic procedures to address various features of the planning problems. In this article, we identify simple conditions and structural properties associated with these logistical planning problems in which the warehouse is managed as a cross‐docking facility. Despite the nonlinear cost structures in the problems, we show that a solution that is within ε‐optimality can be obtained by solving a related piece‐wise linear concave cost multi‐commodity network flow problem. An immediate consequence of this result is that certain classes of logistical planning problems can be approximated by a factor of (1 + ε) in polynomial time. This significantly improves upon the results found in literature for these classes of problems. We also show that the piece‐wise linear concave cost network flow problem can be approximated to within a logarithmic factor via a large scale linear programming relaxation. We use polymatroidal constraints to capture the piece‐wise concavity feature of the cost functions. This gives rise to a unified and generic LP‐based approach for a large class of complicated logistical planning problems. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

基于RBF网络的滑模变结构控制在无刷直流电机伺服系统中的应用

无刷直流电动机的参数强耦合、高度非线性特性增加了对其速度控制的难度,针对这一特点,设计了一种神经网络滑模变结构速度控制器。将无刷直流电机速度伺服系统分成名义模型和不确定模型,采用状态反馈方法对名义模型进行控制,以RBF神经网络为滑模动态补偿器对不确定系统进行控制。该方法不仪具有变结构控制的抗参数摄动、抗干扰以及速度快等优点,神经网络控制的加入还有效地减弱了单纯滑模变结构控制所带来的“抖振”现象。Matlab仿真结果表明,该控制器具有良好的控制性能和鲁棒性能。     

对“民参军”企业质量管理现状的分析与思考

本文简要分析了"民参军"企业质量管理现状与存在的不足,并针对存在的问题提出了如何保持"民参军"企业质量管理体系持续有效运行的措施与思考。     

基于DSPACE的高功率密度柴油机智能化冷却系统控制策略验证

为了验证高功率密度柴油机智能化控制冷却系统控制策略是否正确,以DSPACE为基础搭建了半实物仿真试验平台;通过仿真试验平台验证,控制策略符合柴油机冷却系统工作要求,电控阀、传感器、驱动电路等部件工作可靠,节省了研制时间、经费,降低了研制风险。     

烷烃磨削液应用于Si_3N_4陶瓷磨削时的砂轮堵塞机理及改性研究

通过一系列试验,考察了采用不同烷烃乳液及极性添加剂作为磨削液时金刚石砂轮磨削S i3N4陶瓷时的堵塞效果。试验结果表明:由于烷烃油膜表面的非极性性质对非极性的S i3N4磨屑有很强的吸引力,采用烷烃乳液作为磨削液时,金刚石砂轮较易发生堵塞。通过在烷烃乳液中添加极性有机物,可以很大程度上缓解堵塞的发生,最佳的极性添加剂分子结构需要同时具备一定的憎水基团和亲水基团。     

船用柴油机PID神经网络控制系统的设计

给出了PID NN控制器的结构形式、计算公式,并将其应用于船舶柴油机的转速控制系统中.从仿真结果可知,该算法的控制性能和鲁棒稳定性良好,具有较好的自学习和自适应性.     

氧气鱼雷供氧系统中扰动波的建模与仿真

鱼雷发射时,对高压氧气舱中的氧气起开关作用的启动阀快速开启,从而在启动阀的两端产生激波和膨胀波等两种扰动波.考虑了高压氧气的分子间相互作用力及分子所占体积的影响,采用范德瓦耳斯公式来代替完全气体的状态方程,同时建立了扰动波的数学模型,并推导了气体在接触间断面上的速度计算模型,仿真计算得出激波波阵面上的温度、由膨胀波所引起的低温及其它物理量的数值,为分析氧气鱼雷使用高压氧气的安全性提供了参考数据.     

非晶碳类金刚石薄膜摩擦学特性及其应用

综合地介绍了类金刚石膜研究现状,着重说明了非晶碳类金刚石薄膜的摩擦学特性,分析了沉积方法、掺杂元素、摩擦环境、基体及对偶材料对其摩擦学特性的影响。最后,简要说明了非晶碳类金刚石膜应用情况。     

火灾探测技术研究

火灾探测技术是传感技术和火灾探测算法的结合产物。从火灾探测的基本原理和火灾探测算法这两个方面详细分析了火灾探测技术,为消防工作中火灾探测器选择提供指导,并对火灾探测技术的现状和新兴技术进行了归纳分析,为进一步研究火灾探测技术指明了方向。     

Coping with shortages caused by disruptive events in automobile supply chains

Unpredictable disruptive events significantly increase the difficulty of the management of automobile supply chains. In this paper, we propose an automobile production planning problem with component chips substitution in a finite planning horizon. The shortage of one chip can be compensated by another chip of the same type with a higher-end feature at an additional cost. Therefore, the automobile manufacturer can divert the on-hand inventory of chips to product lines that are more profitable in the event of shortages caused by supply chain disruptions. To cope with this, we propose a max-min robust optimization model that captures the uncertain supplies of chips. We show that the robust model has a mixed-integer programming equivalence that can be solved by a commercial IP solver directly. We compare the max-min robust model with the corresponding deterministic and two-stage stochastic models for the same problem through extensive numerical experiments. The computational results show that the max-min robust model outperforms the other two models in terms of the average and worst-case profits.