Strategic capacity decision‐making in a stochastic manufacturing environment using real‐time approximate dynamic programming

In this study, we illustrate a real‐time approximate dynamic programming (RTADP) method for solving multistage capacity decision problems in a stochastic manufacturing environment, by using an exemplary three‐stage manufacturing system with recycle. The system is a moderate size queuing network, which experiences stochastic variations in demand and product yield. The dynamic capacity decision problem is formulated as a Markov decision process (MDP). The proposed RTADP method starts with a set of heuristics and learns a superior quality solution by interacting with the stochastic system via simulation. The curse‐of‐dimensionality associated with DP methods is alleviated by the adoption of several notions including “evolving set of relevant states,” for which the value function table is built and updated, “adaptive action set” for keeping track of attractive action candidates, and “nonparametric k nearest neighbor averager” for value function approximation. The performance of the learned solution is evaluated against (1) an “ideal” solution derived using a mixed integer programming (MIP) formulation, which assumes full knowledge of future realized values of the stochastic variables (2) a myopic heuristic solution, and (3) a sample path based rolling horizon MIP solution. The policy learned through the RTADP method turned out to be superior to polices of 2 and 3. © 2010 Wiley Periodicals, Inc. Naval Research Logistics 2010     

电子侦察卫星联合侦察的混合调度算法

针对电子侦察卫星的使用约束,及不同任务的调度需求,建立了电子侦察卫星联合侦察的多目标混合整数规划模型.利用进化算法的全局搜索能力和变邻域搜索的局部优化能力,提出了一种多目标进化算法和变邻域搜索相结合两阶段混合调度算法MOEA VNS.针对问题多时间窗组合优化特点,设计了进化算子与邻域移动算子,在确保解多样性的同时使算法...     

A branch‐and‐bound‐based solution approach for dynamic rerouting of airborne platforms

This article is a sequel to a recent article that appeared in this journal, “An extensible modeling framework for dynamic reassignment and rerouting in cooperative airborne operations” [ 17 ], in which an integer programming formulation to the problem of rescheduling in‐flight assets due to changes in battlespace conditions was presented. The purpose of this article is to present an improved branch‐and‐bound procedure to solve the dynamic resource management problem in a timely fashion, as in‐flight assets must be quickly re‐tasked to respond to the changing environment. To facilitate the rapid generation of attractive updated mission plans, this procedure uses a technique for reducing the solution space, supports branching on multiple decision variables simultaneously, incorporates additional valid cuts to strengthen the minimal network constraints of the original mathematical model, and includes improved objective function bounds. An extensive numerical analysis indicates that the proposed approach significantly outperforms traditional branch‐and‐bound methodologies and is capable of providing improved feasible solutions in a limited time. Although inspired by the dynamic resource management problem in particular, this approach promises to be an effective tool for solving other general types of vehicle routing problems. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013     

Bounded flexibility in days‐on and days‐off scheduling

This article presents a flexible days‐on and days‐off scheduling problem and develops an exact branch and price (B&P) algorithm to find solutions. The main objective is to minimize the size of the total workforce required to cover time‐varying demand over a planning horizon that may extend up to 12 weeks. A new aspect of the problem is the general restriction that the number of consecutive days on and the number of consecutive days off must each fall within a predefined range. Moreover, the total assignment of working days in the planning horizon cannot exceed some maximum value. In the B&P framework, the master problem is stated as a set covering‐type problem whose columns are generated iteratively by solving one of three different subproblems. The first is an implicit model, the second is a resource constrained shortest path problem, and the third is a dynamic program. Computational experiments using both real‐word and randomly generated data show that workforce reductions up to 66% are possible with highly flexible days‐on and days‐off patterns. When evaluating the performance of the three subproblems, it was found that each yielded equivalent solutions but the dynamic program proved to be significantly more efficient. © 2013 Wiley Periodicals, Inc. Naval Research Logistics 60: 678–701, 2013     

高校“双语教学课程”的定位

高校"双语教学课程"面临的许多问题都指向一个共同的根源,即课程定位不清。从本质上看,"双语教学课程"是一种语言课程,就目前的现实情况来看,"双语教学课程"在教学目的上,应以提高外语能力为主。它既不同于"双语教育",也不同于"外语教学",应属于"双语教学"的范畴。     

改进的笛卡尔遗传编程组合电路演化设计

利用笛卡尔遗传编程进行电路演化时,存在收敛速度慢、收敛时间波动较大等问题．对笛卡尔遗传编程中的可编程单元模型进行改进,增加与目标函数相关的逻辑运算,去除无关的逻辑运算,从而提高演化算法命中目标的概率．利用改进的笛卡尔遗传编程方法分别对电机换相电路和乘法器等组合电路进行演化设计．结果表明,改进后的方法明显缩短了电路演化生成的时间,且收敛时间波动较小．     

基于贪婪算法的影响网络行动方案优选

针对影响网络中行动方案的优选问题,分析了现有方法存在的不足,提出了一种基于贪婪算法的行动方案优选方法。该方法采用自顶向下的搜索方式,通过分析行动组合对期望效果的整体影响来选择较优的行动方案。并进行实例验证。结果表明,与灵敏度分析法和穷举搜索法相比,该方法能够在较短时间内找到较优的可行行动方案集合,可有效支持行动方案的优选。     

复杂性视域下高校思想政治理论课教学评价

以复杂系统作为研究对象的复杂性科学,带来了一场思维方式的变革。复杂性理论体现出的方法论特征,超越了传统的还原论的思维定势,为推动思想政治理论课教学评价研究提供新的视角。在复杂性视域下,思政课教学评价需要一种范式转换,弃用将教学目标解析分化、并与预定指标机械对比的简单化操作,树立整体的评价观念,采用相对的价值标准,注重评价的过程属性。     

基于单变量多目标规划的装甲车辆冷却系统智能化控制

根据新一代装甲车辆推进系统热部件散热特点及其冷却系统调控原理,提出了一种单变量多目标数学规划与模糊智能控制相结合的控制算法。该算法将冷却系统抽象为单变量多目标数学规划问题,采用加权的方法将多个目标函数转化为综合目标函数,输入到模糊智能控制器中进行冷却控制。仿真结果表明:该控制算法具有良好的控制效果,能够有效地解决新型推进系统热源多、目标温度范围广而可调参数单一的问题。     

屋面太阳能电池布设优化模型

以光伏电池接受太阳能辐射强度最大化为目标,建立了太阳能电池板倾角和朝向优化模型,以确定太阳能电池板的最佳布设方式。根据发电量最大化、单位发电费用最小化原则,以净经济效益为目标函数,建立了光伏电池优选模型,对光伏电池进行优选排序从而选出性价比最高的光伏电池,并对模型进行了检验。结果表明：在山西省大同市地区铺设太阳能光伏电池,架空布设电池板的最佳倾角为36°,朝向为南偏西26.5°,光伏电池板最佳布设方式及电池优选模型与实际情况基本相符。