A hybrid model for selection of an outsourcing provider

Outsourcing is a good strategy for firms that need to reduce operating costs and improve competitiveness and it is important that firms scientifically select appropriate outsourcing providers. Some efforts have been made to find systematic ways to deal with outsourcing problems, but these efforts incorrectly assumed that the criteria used in the decision process are independent, which is not true in the real world. In this study, we propose a new hybrid multiple criteria decision-making (MCDM) model, which addresses the dependent relationships between the various criteria. The relations-structure among the criteria is built with the aid of the decision-making trial and evaluation laboratory (DEMATEL) method. Decision-makers tend to hold diverse opinions about their preferences due to incomplete information and knowledge, or inherent conflict between various departments. We further used the fuzzy preference programming and the analytic network process (ANP) to form a model for the selection of partners for outsourcing providers. The proposed model can help practitioners improve their decision making process, especially when criteria are numerous and inter-related. The method is demonstrated using data from a Taiwanese airline.     

求解线性模型稳健参数估计的整数编码遗传算法

线性模型回归系数的一些稳健估计如LMS、LQS、LTS、LTA的应用越来越广泛,然而它们的精确计算依赖于NP难题,在遇到高维大规模数据集时不可能在较短时间内得到精确解.为尽快得到较高精度的近似解,提出了求解线性模型的稳健参数估计的整数编码遗传算法,通过计算机模拟试验验证了算法可以更快地找出全局最优解.     

Robust airfoil optimization based on improved particle swarm optimization method

A robust airfoil optimization platform is constructed based on the modified particle swarm optimization method (i.e., the second-order oscillating particle swarm method), which consists of an efficient optimization algorithm, a precise aerodynamic analysis program, a high accuracy surrogate model, and a classical airfoil parametric method. There are two improvements for the modified particle swarm method compared with the standard particle swarm method. First, the particle velocity is represented by the combination of the particle position and the variation of position, which makes the particle swarm algorithm a second-order precision method with respect to the particle position. Second, for the sake of adding diversity to the swarm and enlarging the parameter searching domain to improve the global convergence performance of the algorithm, an oscillating term is introduced to the update formula of the particle velocity. At last, taking two airfoils as examples, the aerodynamic shapes are optimized on this optimization platform. It is shown from the optimization results that the aerodynamic characteristic of the airfoils is greatly improved in a broad design range.     

Optimal uncertainty size in distributionally robust inverse covariance estimation

In a recent paper, Nguyen et al. (2018) built a distributionally robust estimator for the precision matrix of the Gaussian distribution. The distributional uncertainty size is a key ingredient in the construction of this estimator. We develop a statistical theory which shows how to optimally choose the uncertainty size to minimize the associated Stein loss. Surprisingly, rather than the expected canonical square-root scaling rate, the optimal uncertainty size scales linearly with the sample size.     

Commutativity of first‐order discrete‐time linear time‐varying systems

After introducing the concept of commutativity for continuous‐time linear time‐varying systems, the related literature and the results obtained so far are presented. For a simple introduction of the commutativity of discrete‐time linear time‐varying systems, the problem is formulated for first‐order systems. Finally, explicit necessary and sufficient conditions for the commutativity of first‐order discrete‐time linear time‐varying systems are derived, and their advantageous use in digital system design is illustrated, which are the main objectives of the paper. The results are verified by examples which include an application in amplitude modulation for digital telecommunication.     

利他偏好下供应链效率和公平度动态演进研究

根据零售商利他偏好信息非对称性和动态演进特征,将零供博弈信息结构分为4个演进过程,采用逆向归纳法求解各个过程中供应链主观效率和客观效率、以"公平熵"计算供应链主观公平度和客观公平度,从主观与客观2个维度分析供应链利他偏好行为对供应链效率和公平度动态演进的影响.通过对比分析得出:信息对称下零售商利他偏好行为不改变供应链利润但会改变各个成员在供应链中的利润分配比例,起到"供应链利润分配机制"的作用;供应链主观效率总是不低于客观效率,供应链主观公平度总是不低于客观公平度;从主观评价供应链能够实现协调且供应链能够兼顾效率和公平.     

Distributionally robust profit opportunities

This paper expands the notion of robust profit opportunities in financial markets to incorporate distributional ambiguity using Wasserstein distance as the ambiguity measure. Financial markets with risky and risk-free assets are considered. The infinite dimensional primal problems are formulated, leading to their simpler finite dimensional dual problems. A principal motivating question is how distributional ambiguity helps or hurts the robustness of the profit opportunity. Towards answering this question, some theory is developed and computational experiments are conducted. Finally some open questions and suggestions for future research are discussed.     

Energy and reserve dispatch with distributionally robust joint chance constraints

We develop a two-stage stochastic program for energy and reserve dispatch of a joint power and gas system with a high penetration of renewables. Data-driven distributionally robust chance constraints ensure that there is no load shedding and renewable spillage with high probability. We solve this problem efficiently using conditional value-at-risk approximations and linear decision rules. Out-of-sample experiments show that this model dominates the corresponding stochastic program without chance constraints that models the effects of load shedding and renewable spillage explicitly.     

基于动态参照点的鲁棒投资组合模型及其拓展

鲁棒投资组合模型是一种适用于收益不确定条件下寻求最优决策的方法。首先考虑投资者对底线的重视,根据当收益触及底线时,激进者和保守者在参照点上的不同变化情况,建立动态参照点模型。接着,一方面将动态参照点作为划分获益和损失的界限值,改进现有的Worst-case Omega(WOmega)模型。另一方面结合投资者对下侧风险更为厌恶的特点,以动态参照点作为下侧风险的基准,改进现有的Relative Robust Portfolio Optimization(RRPO)模型。实证研究中,对于WOmega类模型,结果表明激进行为模型在样本内表现较好,而保守行为模型在样本外表现较好。对于RRPO类模型,结果显示激进行为的收益表现良好,保守行为对标准差及最大损失值的控制较好。随着约束的放松,所有模型的收益都能得到可观提升。     

基于犹豫模糊偏好关系FMEA方法的改进

为有效解决产品在研发过程中存在的一系列质量可靠性问题,本文提出了一种新的基于犹豫模糊偏好关系的改进FMEA方法。考虑到专家小组对不同失效模式评估时主要依据相关标准和自身经验,存在犹豫模糊不确定或自身偏好问题。本文首先对风险因子的评分标准进行犹豫模糊化,并用犹豫模糊偏好关系对失效模式的相对风险矩阵进行处理;其次,将得到的具有犹豫模糊偏好关系的综合偏好值与犹豫模糊评价信息相结合,得到改进的风险优先数,从而得出新的失效模式风险评估顺序对FMEA进行改进;最后,利用改进的FMEA模型对产品研发过程中的质量风险进行分析验证,使得风险结果更接近实际情况,进而提高研发成功率,显示该方法可行、有效。