A produce‐to‐stock system with advance demand information and secondary customers

We consider a manufacturer (i.e., a capacitated supplier) that produces to stock and has two classes of customers. The primary customer places orders at regular intervals of time for a random quantity, while the secondary customers request a single item at random times. At a predetermined time the manufacturer receives advance demand information regarding the order size of the primary customer. If the manufacturer is not able to fill the primary customer's demand, there is a penalty. On the other hand, serving the secondary customers results in additional profit; however, the manufacturer can refuse to serve the secondary customers in order to reserve inventory for the primary customer. We characterize the manufacturer's optimal production and stock reservation policies that maximize the manufacturer's discounted profit and the average profit per unit time. We show that these policies are threshold‐type policies, and these thresholds are monotone with respect to the primary customer's order size. Using a numerical study we provide insights into how the value of information is affected by the relative demand size of the primary and secondary customers. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

基于信息粒化理论的主体间任务分配方法

有效的任务分配对于多主体系统通过主体间协作完成其使命任务是非常重要的。基于信息粒化理论提出了一种新的解决方法,任务分配过程包括两个阶段:主体和任务粒化阶段和粒内规划阶段。给出了主体和任务粒化阶段的数学表示和基于遗传算法的求解方法。案例分析和对比试验表明本文的方法具有较好的性能。     

演化排列优化拉丁超立方设计方法

拉丁超立方设计是最常用的计算机试验设计方法之一,针对现有拉丁超立方设计方法采样一次性且难以兼顾设计的空间均匀性和计算效率的问题,提出了一种演化排列拉丁超立方试验设计方法。通过对小样本设计的演化、排列信息继承和扩充等操作,以较小的计算量实现了样本的扩充与优化。此外,所提方法可以兼顾现有样本和新增采样点之间的关系,实现样本的序列扩充,这在实际近似建模过程中十分方便。通过多组数值试验,验证了本文方法在空间均匀性和计算效率等方面的优越性。