我国社区居家养老服务人员队伍结构优化研究——以564名社区居家养老月艮务人员为例

基于社区居家养老服务事业在我国逐渐受到越来越多人关注的背景．从社区养老服务人员队伍结构优化的视角,用访谈和问卷调查等统计分析方法,从年龄、学历、专业知识、职称等方面对社区居家养老服务人员队伍的结构进行研究。本文认为我国社区居家养老服务队伍结构存在较大的问题。要发展养老服务事业必须对其进行优化,在对其结构不够合理的成因进行深入分析的基础上提出了优化措施和策略。     

考虑专家判断信息的灰色关联极大熵权重模型

本文提出了一种从关联角度出发将主观先验信息与客观信息纳入约束条件从而求解综合权重的方法。在利用灰关联深度系数对实际决策问题进行客观权重判断研究的基础之上,构建了源于专家判断信息的权重势比的主观约束条件,将主客观因素同时反映在优化模型的约束条件中,并将权重的极大熵作为目标函数,保证权重判断的可信度,从而构建了确定评价指标综合权重的极大熵优化模型。该方法克服了将主客观条件直接通过线性组合作为目标函数时,主客观参数选取导致的权重大小的不确定性,并同其他赋权方法进行案例结果比较,表明了该方法的有效性。     

Ambiguity in the Small and in the Large

This paper considers local and global multiple‐prior representations of ambiguity for preferences that are (i) monotonic, (ii) Bernoullian, that is, admit an affine utility representation when restricted to constant acts, and (iii) locally Lipschitz continuous. We do not require either certainty independence or uncertainty aversion. We show that the set of priors identified by Ghirardato, Maccheroni, and Marinacci's (2004) “unambiguous preference” relation can be characterized as a union of Clarke differentials. We then introduce a behavioral notion of “locally better deviation” at an act and show that it characterizes the Clarke differential of the preference representation at that act. These results suggest that the priors identified by these preference statements are directly related to (local) optimizing behavior.     

Improving the Numerical Performance of Static and Dynamic Aggregate Discrete Choice Random Coefficients Demand Estimation

The widely used estimator of Berry, Levinsohn, and Pakes (1995) produces estimates of consumer preferences from a discrete‐choice demand model with random coefficients, market‐level demand shocks, and endogenous prices. We derive numerical theory results characterizing the properties of the nested fixed point algorithm used to evaluate the objective function of BLP's estimator. We discuss problems with typical implementations, including cases that can lead to incorrect parameter estimates. As a solution, we recast estimation as a mathematical program with equilibrium constraints, which can be faster and which avoids the numerical issues associated with nested inner loops. The advantages are even more pronounced for forward‐looking demand models where the Bellman equation must also be solved repeatedly. Several Monte Carlo and real‐data experiments support our numerical concerns about the nested fixed point approach and the advantages of constrained optimization. For static BLP, the constrained optimization approach can be as much as ten to forty times faster for large‐dimensional problems with many markets.     

Dynamic Valuation Decomposition Within Stochastic Economies

I explore the equilibrium value implications of economic models that incorporate responses to a stochastic environment with growth. I propose dynamic valuation decompositions (DVD's) designed to distinguish components of an underlying economic model that influence values over long investment horizons from components that impact only the short run. A DVD represents the values of stochastically growing claims to consumption payoffs or cash flows using a stochastic discount process that both discounts the future and adjusts for risk. It is enabled by constructing operators indexed by the elapsed time between the trading date and the date of the future realization of the payoff. Thus formulated, methods from applied mathematics permit me to characterize valuation behavior and the term structure of risk prices in a revealing manner. I apply this approach to investigate how investor beliefs and the associated uncertainty are reflected in current‐period values and risk‐price elasticities.     

应急储备库选址与资源配置随机规划模型研究

研究在突发事件多发区域进行应急储备库的选址与资源配置规划问题.假定了若干随机的突发事件情景,每种情景下物资需求量与运输时间不确定,结合情景分析法,建立了一定应急限制期条件下的两阶段随机规划模型,并设计了混合遗传算法对模型进行求解.研究结果不仅找到了最优的选址与资源配置方案,而且发现牺牲需求满足率与应急限制期带来的成本降低是不经济的.     

GPRs搭接网络分解优化定理在流水作业中的应用

本文发现在GPRs搭接网络传统算法中，针对某些可分解的关键工序，通过工序的分解会产生分解悖论和咖啡时间悖论。通过对这些悖论现象的分析研究，发现其存在帕累托改进。对此，提出了两个分解优化定理及网络的分解优化方法，使网络的总工期和总时差的分布都得到了优化，为项目WBS和资源优化提供了更科学的，更充足的条件。并将该分解优化定理同流水作业原理相结合，用实例证明了该方法的可操作性，为流水作业中施工段的划分提供了科学的优化方法。     

Network Reconfiguration for Increasing Transportation System Resilience Under Extreme Events

Evacuating residents out of affected areas is an important strategy for mitigating the impact of natural disasters. However, the resulting abrupt increase in the travel demand during evacuation causes severe congestions across the transportation system, which thereby interrupts other commuters' regular activities. In this article, a bilevel mathematical optimization model is formulated to address this issue, and our research objective is to maximize the transportation system resilience and restore its performance through two network reconfiguration schemes: contraflow (also referred to as lane reversal) and crossing elimination at intersections. Mathematical models are developed to represent the two reconfiguration schemes and characterize the interactions between traffic operators and passengers. Specifically, traffic operators act as leaders to determine the optimal system reconfiguration to minimize the total travel time for all the users (both evacuees and regular commuters), while passengers act as followers by freely choosing the path with the minimum travel time, which eventually converges to a user equilibrium state. For each given network reconfiguration, the lower‐level problem is formulated as a traffic assignment problem (TAP) where each user tries to minimize his/her own travel time. To tackle the lower‐level optimization problem, a gradient projection method is leveraged to shift the flow from other nonshortest paths to the shortest path between each origin–destination pair, eventually converging to the user equilibrium traffic assignment. The upper‐level problem is formulated as a constrained discrete optimization problem, and a probabilistic solution discovery algorithm is used to obtain the near‐optimal solution. Two numerical examples are used to demonstrate the effectiveness of the proposed method in restoring the traffic system performance.     

Robustness analysis methodology for multi-objective combinatorial optimization problems and application to project selection

Multi-objective combinatorial optimization (MOCO) problems, apart from being notoriously difficult and complex to solve in reasonable computational time, they also exhibit high levels of instability in their results in case of uncertainty, which often deviate far from optimality. In this work we propose an integrated methodology to measure and analyze the robustness of MOCO problems, and more specifically multi-objective integer programming ones, given the imperfect knowledge of their parameters. We propose measures to assess the robustness of each specific Pareto optimal solution (POS), as well as the robustness of the entire Pareto set (PS) as a whole. The approach builds upon a synergy of Monte Carlo simulation and multi-objective optimization, using the augmented ε-constraint method to generate the exact PS for the MOCO problems under examination. The usability of the proposed framework is justified through the identification of the most robust areas of the Pareto front, and the characterization of every POS with a robustness index. This index indicates a degree of certainty that a specific POS sustains its efficiency. The proposed methodology communicates in an illustrative way the robustness information to managers/decision makers and provides them with an additional supplement/tool to guide and support their final decision. Numerical examples focusing on a multi-objective knapsack problem and an application to academic capital budgeting problem for project selection, are provided to verify the efficacy and added value of the methodology.     

A practical guide to robust optimization

Robust optimization is a young and active research field that has been mainly developed in the last 15 years. Robust optimization is very useful for practice, since it is tailored to the information at hand, and it leads to computationally tractable formulations. It is therefore remarkable that real-life applications of robust optimization are still lagging behind; there is much more potential for real-life applications than has been exploited hitherto. The aim of this paper is to help practitioners to understand robust optimization and to successfully apply it in practice. We provide a brief introduction to robust optimization, and also describe important do׳s and don׳ts for using it in practice. We use many small examples to illustrate our discussions.