Performance measurement with classification information: an enhanced additive DEA model

This paper presents an improved measurement tool for evaluating performance of branches within a major Canadian bank. While there have been numerous previous studies of performance in the banking industry, particularly at the branch level, this study is different in a very significant way: specifically two kinds of data are used to develop the model. The first type of data is that related to standard transactions, available from any bank; such have formed the basis of numerous previous studies. The second type of data, obtained from the site studied, is classification information, based on branch consultant/expert judgment as to good and poor performance of branches. The purpose herein is to present a modified version of an existing benchmarking model, data envelopment analysis (DEA), and to show how this tool is applied in the banking industry. The mechanism used herein to incorporate expert knowledge within the DEA framework is to first apply a discriminant or classification tool, to quantify the functional relation that best captures the expert's mental model for performance. The outcome of this first phase is an orientation of variables to aid in the definition of inputs and outputs. The resulting orientation then defines the DEA model that makes up the second phase of the model.     

Optimization Model and Algorithm for Crew Management During Airline Irregular Operations

Airline irregular operations have long been a realm where human experience and judgement are the most important tools to utilize. Crew management during irregular operations is usually the bottleneck of the whole system-recovering process due to complicated crew schedules and restrictive crew legalities as well as the size and scope of the hub-and-spoke networks adopted by major carriers. A system-wide multi-commodity integer network flow model and a heuristic search algorithm for the above purpose are presented and discussed in this paper. The computational experiences show that the algorithm is efficient enough to solve problems of realistic size and also has the flexibility to accommodate practical business requirements.     

产业集群参与者交互偏好与知识网络演化:模型与仿真

知识网络是产业集群网络演化的重要组成部分和先决条件,本文将产业集群中参与者进行交互的偏好分为任务导向型和知识导向型,建立了一个基于多智能主体的产业集群知识网络模型,利用Blanche软件实现了知识网络演化模型的仿真计算,通过仿真研究了任务导向型和知识导向型知识网络的对网络演化的不同影响.最后,本文提出了研究的局限性和下一步研究的方向.     

基础研究学科发展的宏观知识挖掘

本文提出了一种学科发展分析的宏观知识挖掘思路和方法.从整体上、宏观上对蕴含于基础研究项目建议书(Proposal)中的知识进行挖掘,并在挖掘的基础之上对基础研究的学科发展趋势、特点,学科领域的分化、融合以及交叉等进行分析,并从学科整体、学科领域和项目等三个层面对基础研究的科研管理工作提供基于知识的决策支持.     

对学习历史事件法的一种改进

依据组织学习理论,对学习历史事件法进行了改进。由于企业的经验知识不完全等同于隐性知识,学习历史事件法同样不能直接应用于隐性知识的转化,为此,提出了适合隐性知识转化的改进型学习历史事件法。改进充分考虑了隐性知识的特征,并专门对隐性知识和经验知识进行了区分。同时,改进的学习历史事件法充分考虑了企业应用的可操作性,保留了量表的形式。最后,将该方法应用于MIS项目开发中,对新改进的量表进行了定量解析。     

Inverse Combinatorial Optimization: A Survey on Problems, Methods, and Results

Given a (combinatorial) optimization problem and a feasible solution to it, the corresponding inverse optimization problem is to find a minimal adjustment of the cost function such that the given solution becomes optimum.Several such problems have been studied in the last twelve years. After formalizing the notion of an inverse problem and its variants, we present various methods for solving them. Then we discuss the problems considered in the literature and the results that have been obtained. Finally, we formulate some open problems.     

基于知识图谱的员工创新行为研究分析

以中国知网数据库2007—2017年收录的题名中含有“员工创新行为”的293篇期刊论文作为数据来源,借助CiteSpace5.0软件绘制可视化知识图谱,对该时间段国内员工创新行为研究的年度发文量、发文机构、文献来源机构、代表性学者及研究热点进行可视化分析。研究结果表明：2007—2010年,员工创新行为研究的载文量较少但呈逐步增长趋势,2010—2017年,员工创新行为发文量增长趋势明显;发文量排名前20的研究机构中有19个为高校;载文量在5篇以上的期刊主要为经济管理类期刊;其研究热点主要集中在员工创新行为主体研究,以及个人特征、领导力、组织文化与氛围等员工创新行为前因变量研究。未来的研究方向为：开发适合中国情境的员工创新行为量表,在整体视角下探讨不同创新主体之间的关联及员工创新行为的结果变量等。     

众筹教育与高校美育课改革的契合及实现路径

众筹教育通过打造并完善教育资源共享平台,推动教育资源的虚拟重组、均衡发展,实现跨区域教育的协同发展,有助于实现知识增值的目标。众筹教育所具备的创意性筹资、柔性化生产、互动化交流、区域化共享等特征使其与当下的高校美育选修课教育体制改革的需求高度契合。从实现路径来看,众筹教育介入高校美育选修课改革可以从校内模拟众筹、区域内课程共享、完善教育资源共享平台3个层面逐步铺开,形成高校美育选修课线上与线下互动发展的新模式,为推动我国教育体制改革困局的破解提供思路。     

An Exploration of “Superb Competences” in College Teachers in the Context of U-Learning

Ubiquitous learning (U-learning), a potential shortcut for China to modernize its education, is treated in this discussion as a mentality and means for citizens in high-tech society to seek a lifelong education. Thus situated, this article presents an exploration of “superb competences”—material intelligence, internet thinking, and brain-related neuro knowledge—in college teachers outside their trained professional areas. It is believed that once the teachers are equipped with the material intelligence, internet thinking, and brain-related neuro knowledge, real U-learning practice is ensured and its rationale will be developed. Material intelligence and internet thinking are evolved from and closely related to technological development, and therefore constitute the essential requirements of U-learning in terms of technical and educational cognitive framework. A good knowledge of brain related neuroscience, the third competence, guarantees the scientific rooting of every element in the designing of U-leaning tasks, whose success resides in a serious respect of cognitive nature and rules of the brain. The building of such superb competences, as is argued at the end of the paper, relies on various supports, like policies and technological aids, from all levels of educational administration in the country as well as each individual teacher’s personal understanding and efforts.     

酒店连锁企业员工知识分享对知识资本影响的实证研究

员工知识分享是促进组织知识资本增值,进而提高组织绩效的驱动力之一。本文基于计划行为理论,从行为态度、主观规范以及认知行为控制3个维度来探讨酒店连锁企业员工知识分享对知识资本和组织绩效的影响程度,并运用结构方程模型进行实证分析,旨在为酒店连锁企业的经营管理提供参考。