模糊车辆路径问题的一种混合遗传算法

在对模糊车辆路径问题进行简单描述的基础上,通过引入决策者主观偏好值的概念,给出了解决该问题的基本思路,建立了具有模糊特征的车辆路径问题的模糊机会规划模型,提出了求解该问题的一种基于模糊模拟的混合遗传算法。同时,在最小化总行驶距离的目标下,通过随机模拟方法研究了决策者主观偏好值的选择对最终决策目标的影响作用,并给出了其最佳取值范围。     

基于顾客退货的闭环供应链运作绩效研究

为了探讨顾客退货情境下的供应链运作绩效问题,采用动态建模方法对产品正向物流和退货逆向物流为一体的闭环供应链系统进行研究,构建生产-分销-市场-退货-再处理-再销售等多个环节的系统运作模型.通过计算机仿真方法模拟不同情境下系统的动态行为规律,着重从产品生命周期视角探讨顾客退货对供应链系统运作绩效的影响;以系统总利润为指标,评估退货率、退货处理时间、产品价格衰减等因素的影响效应.研究结果表明,无论是对长生命周期产品还是短生命周期产品,退货率和产品价格衰减均是影响系统绩效的关键因素,高退货率和高价格衰减会显著削弱供应链整体的盈利能力,导致系统总利润大幅下滑.对于陷入高退货、高价格衰减困境的企业,设定相对较短的退货期限可以在一定程度上改善盈利情况;此外,当加快退货处理速度不会引起额外的成本投资时,快速的退货处理方式对企业更为有益,特别是对短生命周期产品而言,缩短退货环节的处理时间具有非常显著的经济价值.     

基于元数据的数据仓库在智能决策支持系统中的应用

将人工智能理论引入决策支持系统,构成智能决策支持系统。数据库是智能决策支持系统的重要组成部分,本文介绍了元数据的基本概念,将基于元数据的数据仓库技术运用到智能决策支持系统中,提出一个新的智能决策支持系统框架。     

非正式创新网络结构仿真研究

非正式创新网络对于组织创新能力的提高具有重要意义,特别是我国社会具有充满"人情"味的特征,研究非正式创新网络更具有重要的现实意义.在对非正式创新网络的概念和研究现状进行分析梳理的基础之上,对非正式创新网络建立计算机仿真模型.初步研究了非正式创新网络的结构特征.发现非正式创新网络同时具有小世界以及无标度特性.     

基于Agent和进化博弈的服务商动态联盟协同管理策略研究

基于进化博弈理论,实现了用于服务商动态联盟协同管理工作的多智能体模拟系统,并进行模拟数据分析。扩展传统对称静态博弈为含惩罚参数和协同效率参数的离散进化博弈情形,设计一种含历史信息和近邻特征的群体进化学习规则;在Repast基础上,用java编程实现了多加盟企业进化博弈Agent模拟系统并进行分析。结果表明:agent规模数对平均收益影响小,具有不同决策特征的agent的比例分布能改变企业的平均收益,不同协同率、沟通方式和惩罚参数对期望收益有影响。为电子商务和移动商务环境下的动态联盟管理决策提供参考。     

控制性股东侵占行为及其负外部性研究

如何保护投资者利益免受侵害和提高资本市场效率是在转型经济背景下中国公司治理、产权制度以及金融市场改革过程中亟待解决的关键问题之一.然而,控制性股东主导的公司治理中,相对于控制性股东监督效应而言,控制性股东侵占效应是占优均衡.控制权私人收益的排他性使得侵占行为同时具有很强的负外部性.解决问题的关键在于通过动态配置决策权来建立有效的制衡机制优化公司治理机制以实现利益相关者间的互惠相容.     

面向多样化需求的M RO 服务资源配置模型与算法

针对大型复杂企业维修任务集的多服务需求点和多样化需求特征,基于服务需求视角,定义一类多服务需求点下的MRO服务资源配置问题,建立以MRO服务提供商整体服务成本为优化目标,同时考虑资源紧张度、信任度和多服务需求点不同工期要求的问题模型;针对模型中的关键参数（即服务成本和资源紧张度）的特征进行分析,并给出相应的度量方法;在此基础上,设计改进的模拟植物生长算法实现求解,算法通过启发式获得初始可行解,并进行敏感性分析。研究结果表明, MRO服务资源配置模型和算法是可行和有效的,服务成本的波动、信任度和资源紧张度的变化对于资源配置结果均有影响,模型能够有效提高维修服务成本的估算能力,同时能够保持MRO服务提供商资源配置的稳定性。     

Effects of Hourly,Low-Incentive,and High-Incentive Pay on Simulated Work Productivity: Initial Findings With a New Laboratory Method

The failures of previous studies to demonstrate productivity differences across different percentages of incentive pay may be partially due to insufficient simulation fidelity. The present study compared the effects of different percentages of incentive pay using a more advanced simulation method. Three payment methods were tested: hourly, low-incentive, and high-incentive (0%, 10%, and 100%) pay. Four participants performed a simulated work task for 30 6-hr sessions. Productivity under the 100% incentive condition was consistently higher than under the 10% condition for all participants. Productivity under the 10% condition was higher than under the 0% condition for two participants. Results suggest that different percentages of incentive pay may in fact produce productivity differences under more realistic simulated work conditions.     

先买权基础理论之探究

先买权可以分为法定先买权与意定先买权、物权性先买权与债法上的先买权。债法上的先买权是一种形成权,不具有对抗第三人的效力;物权性先买权则是一种物权取得权,具有对抗第三人的效力。我国民事立法中规定的先买权应解释为债法上的先买权,不具有对抗第三人的效力。     

Program Evaluation and Causal Inference With High‐Dimensional Data

In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data‐rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function‐valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced‐form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post‐regularization and post‐selection inference that are uniformly valid (honest) across a wide range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced‐form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets. The results on program evaluation are obtained as a consequence of more general results on honest inference in a general moment‐condition framework, which arises from structural equation models in econometrics. Here, too, the crucial ingredient is the use of orthogonal moment conditions, which can be constructed from the initial moment conditions. We provide results on honest inference for (function‐valued) parameters within this general framework where any high‐quality, machine learning methods (e.g., boosted trees, deep neural networks, random forest, and their aggregated and hybrid versions) can be used to learn the nonparametric/high‐dimensional components of the model. These include a number of supporting auxiliary results that are of major independent interest: namely, we (1) prove uniform validity of a multiplier bootstrap, (2) offer a uniformly valid functional delta method, and (3) provide results for sparsity‐based estimation of regression functions for function‐valued outcomes.