Asynchronous action-reward learning for nonstationary serial supply chain inventory control

Action-reward learning is a reinforcement learning method. In this machine learning approach, an agent interacts with non-deterministic control domain. The agent selects actions at decision epochs and the control domain gives rise to rewards with which the performance measures of the actions are updated. The objective of the agent is to select the future best actions based on the updated performance measures. In this paper, we develop an asynchronous action-reward learning model which updates the performance measures of actions faster than conventional action-reward learning. This learning model is suitable to apply to nonstationary control domain where the rewards for actions vary over time. Based on the asynchronous action-reward learning, two situation reactive inventory control models (centralized and decentralized models) are proposed for a two-stage serial supply chain with nonstationary customer demand. A simulation based experiment was performed to evaluate the performance of the proposed two models. Chang Ouk Kim received his Ph.D. in industrial engineering from Purdue University in 1996 and his B.S. and M.S. degrees from Korea University, Republic of Korea in 1988 and 1990, respectively. From 1998--2001, he was an assistant professor in the Department of Industrial Systems Engineering at Myongji University, Republic of Korea. In 2002, he joined the Department of Information and Industrial Engineering at Yonsei University, Republic of Korea and is now an associate professor. He has published more than 30 articles at international journals. He is currently working on applications of artificial intelligence and adaptive control theory in supply chain management, RFID based logistics information system design, and advanced process control in semiconductor manufacturing. Ick-Hyun Kwon is a postdoctoral researcher in the Department of Civil and Environmental Engineering at University of Illinois at Urbana-Champaign. Previous to this position, Dr. Kwon was a research assistant professor in the Research Institute for Information and Communication Technology at Korea University, Seoul, Republic of Korea. He received his B.S., M.S., and Ph.D. degrees in Industrial Engineering from Korea University, in 1998, 2000, and 2006, respectively. His current research interests are supply chain management, inventory control, production planning and scheduling. Jun-Geol Baek is an assistant professor in the Department of Business Administration at Kwangwoon University, Seoul, Korea. He received his B.S., M.S., and Ph.D. degrees in Industrial Engineering from Korea University, Seoul, Korea, in 1993, 1995, and 2001 respectively. From March 2002 to February 2007, he was an assistant professor in the Department of Industrial Systems Engineering at Induk Institute of Technology, Seoul, Korea. His research interests include machine learning, data mining, intelligent machine diagnosis, and ubiquitous logistics information systems. An erratum to this article can be found at     

基于数据仓库技术的高校招生决策支持系统的构建

该文首先介绍了决策支持系统的概念,然后通过对高校招生进行需求分析,利用微软的产品MSSQLServer2005构建了招生决策支持系统平台。     

嵌套分割算法在随机需求车辆路径问题中的应用研究

对需求量满足二项分布的随机需求车辆路径问题进行了研究,在服务失败时采取允许部分服务的策略,并将嵌套分割算法与扫描算法相结合,给出了一种新的求解随机需求车辆路径问题的两阶段算法,数值试验验证了该算法的有效性。同时,该算法也拓展了车辆路径问题的算法空间。     

基于设备级备份系统的按需恢复方法

提出了一种在设备级备份系统基础上的数据按需恢复方法,这种方法突破了设备级备份只能实现设备级恢复的限制,使得设备级备份系统能够提供数据的设备级、数据块级和文件级的按需恢复.按需恢复方法通过虚拟设备技术实现.这种技术具有一定的通用性,并详细介绍了虚拟设备技术的设计与实现.     

带有两次订购机会且两阶段需求相关的Newsboy模型

首先建立需求形式为一般随机变量情形下零售商的两次订购决策模型,并从理论上证明了在两个阶段需求相互独立情形下,零售商期望利润函数的下凹性及最优订购策略的存在唯一性,从而弥补了已有模型在寻求最优订购策略时采用数值方法所存在的缺憾;然后,建立两阶段需求相关情形下带有两次订购的Newsboy模型,给出了模型的分析解.数值实例验证了模型的求解过程,并得到了相关管理启示.     

Modelling sustainable international tourism demand to the Brazilian Amazon

The Amazon rainforest is one of the world's greatest natural wonders and holds great importance and significance for the world's environmental balance. Around 60% of the Amazon rainforest is located in the Brazilian territory. The two biggest states of the Amazon region are Amazonas (the upper Amazon) and Pará (the lower Amazon), which together account for around 73% of the Brazilian Legal Amazon, and are the only states that are serviced by international airports in Brazil's north region. The purpose of this paper is to model and forecast sustainable international tourism demand for the states of Amazonas, Pará, and the aggregate of the two states. By sustainable tourism is meant a distinctive type of tourism that has relatively low environmental and cultural impacts. Economic progress brought about by illegal wood extraction and commercial agriculture has destroyed large areas of the Amazon rainforest. The sustainable tourism industry has the potential to contribute to the economic development of the Amazon region without destroying the rainforest. The paper presents unit root tests for monthly and annual data, estimates alternative time series models and conditional volatility models of the shocks to international tourist arrivals, and provides forecasts for 2006 and 2007.     

以培养复合型人才为目标的计算机专业英语教学体系的研究

本文探讨如何建立以培养具有较高计算机专业知识和较强的英语听、说、读、写等应用能力的复合型人才为目标的计算机专业英语教学体系,尝试改进教学方法,修正课程内容与结构。实践表明,新的教学体系不仅明确了计算机专业英语的教学目的和人才培养目标,更能够使学生在英语中学习信息技术、从计算机专业知识的应用中体会和使用英语。     

广告创意之我见

广告创意犹如万花筒,可以千变万化任意发挥,但不论广告创意如何新颖,媒体运用如何灵活,如果没有正确的广告创意方法,就很难从时下广告信息泛滥的洪流中,脱颖而出,赢得消费者的青睐。本文就广告创意的一些基本方法进行了阐述,为广大初入广告业的从业人员提供一点广告创意的基本方法。     

试论高校动画本科教育改革

近年来,我国的动漫产业正在迅速成长,对动漫人才的需求猛增,出现了动漫人才紧缺的现象。而作为动漫产业人才培养的主要基地——高校动画本科教育却由于各种原因无法培养合格的动漫人才。针对这一问题,本论文旨在通过研究当前我国动漫产业对人才的需求与高校动画本科教育之间的主要矛盾,寻找一条适合高校动画本科教育的人才培养模式,解决我国动漫人才紧缺的情况。     

基于Elastos操作系统的图形引擎的设计与实现

Elastos图形系统Vinci是Elastos操作系统的一个子系统,负责在屏幕上绘制像素、文本和用户界面（包括窗口、菜单、对话框）等。图形引擎的体系结构决定了此系统的简易性、灵活性、效率和稳定性,所以它的设计又显得至关重要。分别介绍了基于Elastso操作系统的图形系统Vinci、此图形引擎的设计需求,在此基础上根据图形系统的引擎功能设计需求结合nitros图形系统Vinci自有的特点,实现了Vinci引擎的设计,使得基于Vinci系统的Elastos操作系统可以完成各种各样的操作。