Pursuing your leader development: Lessons from 101 executives

To lead effectively in a VUCA (i.e., volatile, uncertain, complex, and ambiguous) environment, leaders must quickly and continuously acquire new skills. However, formal development opportunities aren’t nearly enough to support the ongoing skill development leaders need to manage the level of complexity and change they are facing. Instead, leaders must take ownership of their own development. In this paper, we describe a straightforward, four-step process that leaders can implement to pursue their individualized development through leader development planning. Specifically, we walk leaders through the evidence-based steps of creating their unique leader development plan (LDP), including (1) identifying their leadership strengths and weaknesses, (2) setting effective leader development goals, (3) designing SMART strategies, and (4) reflecting, refining, and realigning their plan. Throughout the article, we detail positive examples as well as common pitfalls that we observed from our work supporting 101 mid- to senior-level leaders in designing their LDPs. Our purpose in doing so is to provide tools and examples to enable leaders at all levels to drive their development at their own pace.     

基于效应的随机灰规划模型和应用

为了解决同时含有随机因素和灰色因素的不确定规划问题,通过结合区间灰数所属区间两个端点的随机性,给出随机区间灰数和随机区间灰函数的定义,提出了随机灰规划模型。通过综合效应函数理论用随机变量期望值和方差综合量化表示灰数所属区间的两个端点值。应用该理论对综合量化后的两个端点值继续进行综合量化,从而将随机灰规划转化为确定型规划问题。应用遗传算法进行求解。通过综合效应函数的理念,综合随机变量的期望和方差,同时综合区间灰数的区间因素,将随机灰规划数学模型转化为确定型规划模型即基于效应的随机灰规划模型。通过选取不同的综合效应函数,得到了关于不同决策意识下的随机灰规划的最优解。这个方法可为决策者进行不确定决策提供参考。     

Economic interpretations of DEA

Research on productive efficiency at the firm level has developed as an important and active strand of research the last decades, both within operations research, management science and economics. Two apparently different definitions of efficiency are examined, but it is shown that when both estimation methods are based on solving linear programming problems the definitions of efficiency are identical. The purpose of the paper is to give the basic ideas of efficiency analyses using DEA as a tool for researchers not so familiar with efficiency analysis and DEA. The concept of shadow prices is given special attention.     

浅议现代企业的财务管理

论述现代企业财务管理的一般方法、企业目标与财务管理的关系。     

面向对象程序设计教学改革与实践

指出了面向对象程序设计教学中存在的问题,针对问题提出了改革措施：采用案例式教学,加强实践教学管理,结合BB教学平台辅助教学。不断提高学生学习的自觉性和主动性,成为教学改革的重要研究内容。     

小学体育新课标目标统领下单元教学计划的研究

新的全日制义务教育《体育(1至6年级)课程标准,》对体育课提出了运动参与、运动技能、身体健康、心理健康和社会适应等五大领域的教学目标。文章通过实验,在对比中阐述了以新课程标准为教学目标,进行有针对性的教学设计和课堂实施,可使学生各方面的发展更符合新时期的要求的观点,并提出了单元教学计划设计的方法。     

Lower Bound Restrictions on Intensities in Data Envelopment Analysis

We propose an extension to the basic DEA models that guarantees that if an intensity is positive then it must be at least as large as a pre-defined lower bound. This requirement adds an integer programming constraint known within Operations Research as a Fixed-Charge (FC) type of constraint. Accordingly, we term the new model DEA_FC. The proposed model lies between the DEA models that allow units to be scaled arbitrarily low, and the Free Disposal Hull model that allows no scaling. We analyze 18 datasets from the literature to demonstrate that sufficiently low intensities—those for which the scaled Decision-Making Unit (DMU) has inputs and outputs that lie below the minimum values observed—are pervasive, and that the new model ensures fairer comparisons without sacrificing the required discriminating power. We explain why the low-intensity phenomenon exists. In sharp contrast to standard DEA models we demonstrate via examples that an inefficient DMU may play a pivotal role in determining the technology. We also propose a goal programming model that determines how deviations from the lower bounds affect efficiency, which we term the trade-off between the deviation gap and the efficiency gap.     

供应链管理中的两层规划算法

文章概述两层规划问题在交通领域、资源分配、委托代理以及供应链管理中的应用并讨论了线性和非线性两种两层规划问题,总结了极点算法、下降迭代、惩罚函数和分支定界算法,并进行了评价,最后进一步探讨了两层规划问题的应用前景和算法的发展方向。     

On the Use of Value Efficiency Analysis and Some Further Developments

The purpose of this paper is to discuss the use of Value Efficiency Analysis (VEA) in efficiency evaluation when preference information is taken into account. Value efficiency analysis is an approach, which applies the ideas developed for Multiple Objective Linear Programming (MOLP) to Data Envelopment Analysis (DEA). Preference information is given through the desirable structure of input- and output-values. The same values can be used for all units under evaluation or the values can be specific for each unit. A decision-maker can specify the input- and output-values subjectively without any support or (s)he can use a multiple criteria support system to assist him/her to find those values on the efficient frontier. The underlying assumption is that the most preferred values maximize the decision-maker's implicitly known value function in a production possibility set or a subset. The purpose of value efficiency analysis is to estimate a need to increase outputs and/or decrease inputs for reaching the indifference contour of the value function at the optimum. In this paper, we briefly review the main ideas in value efficiency analysis and discuss practical aspects related to the use of value efficiency analysis. We also consider some extensions.