Modified genetic algorithm-based clustering for probability density functions

This article modifies two internal validity measures and applies them to evaluate the quality of clustering for probability density functions (pdfs). Based on these measures, we propose a new modified genetic algorithm called GA-CDF to establish the suitable clusters for pdfs. The proposed algorithm is tested by four numerical examples including two synthetic data sets and two real data sets. These examples illustrate the superiority of proposed algorithm over some existing algorithms in evaluating the internal or external validity measures. It demonstrates the feasibility and applicability of the GA-CDF for practical problems in data mining.     

On algorithmic construction of maximin distance designs

Maximin distance designs are useful for conducting expensive computer experiments. In this article, we compare some global optimization algorithms for constructing such designs. We also introduce several related space-filling designs, including nested maximin distance designs, sliced maximin distance designs, and general maximin distance designs with better projection properties. These designs possess more flexible structures than their analogs in the literature. Examples of these designs constructed by the algorithms are presented.     

VIF-based adaptive matrix perturbation method for heteroskedasticity-robust covariance estimators in the presence of multicollinearity

In this study, we investigate linear regression having both heteroskedasticity and collinearity problems. We discuss the properties related to the perturbation method. Important observations are summarized as theorems. We then prove the main result that states the heteroskedasticity-robust variances can be improved and that the resulting bias is minimized by using the matrix perturbation method. We analyze a practical example for validation of the method.     

Evaluation of multi-objective optimization approaches for solving green supply chain design problems

This paper evaluates the applicability of different multi-objective optimization methods for environmentally conscious supply chain design. We analyze a case study with three objectives: costs, CO₂ and fine dust (also known as PM – Particulate Matters) emissions. We approximate the Pareto front using the weighted sum and epsilon constraint scalarization methods with pre-defined or adaptively selected parameters, two popular evolutionary algorithms, SPEA2 and NSGA-II, with different selection strategies, and their interactive counterparts that incorporate Decision Maker׳s (DM׳s) indirect preferences into the search process. Within this case study, the CO₂ emissions could be lowered significantly by accepting a marginal increase of costs over their global minimum. NSGA-II and SPEA2 enabled faster estimation of the Pareto front, but produced significantly worse solutions than the exact optimization methods. The interactive methods outperformed their a posteriori counterparts, and could discover solutions corresponding better to the DM preferences. In addition, by adjusting appropriately the elicitation interval and starting generation of the elicitation, the number of pairwise comparisons needed by the interactive evolutionary methods to construct a satisfactory solution could be decreased.     

LC滤波器设计的优化解析公式

给出了改进的Chebyshev和Butterworth低通原型LC滤波器的解析设计公式.此公式充分利用阶数取整带来的余量,在不改变滤波器阶数的前提下减小了通带波动,极大地方便了集成滤波器的实现.给出了集成有源高通滤波器设计实例,验证了改进的Chebyshev和Butterworth低通原型LC滤波器解析设计公式的有效性.     

民族院校人才培养理念的重构与教学管理制度的优化

长期以来,民族院校为民族地区的民主改革、经济建设、改革开放、社会稳定,为维护我国的民族团结和国家统一,为繁荣和弘扬各民族优秀文化做出了重要贡献.民族院校已经完成了自身的第一次跨越,即"正规化"--从非正规化的高等教育模式向普通高等教育模式转变,实现了由以干部培训为主向正规大学的跨越.目前,民族院校在新的形势下,如何实现第二次跨越,建设新型的现代化民族大学,显得尤为紧迫.本文重点论述了民族院校人才培养理念的重构与教学管理制度的优化两个关键问题.     

上海城市人口总量控制与结构优化研究

上海的人口总量和结构一直与其独特的工业化、城镇化进程密不可分。工业化带动人口发展,人口发展推动城市发展。改革开放以来,上海正处于第四波工业化时期,人口增长较快。在人口结构上,户籍人口老龄化,而外来人口具有就近流动、城城流动、定居化特点,且教育水平逐年提高。从承载力上看,上海人口总量合理,但人口分布的不合理影响了人口结构的发展、城市社会的融合,带来了核心区人口过密、产城融合不畅的问题,以及偏好"人脑"、排斥"人手"的问题。面对人口老龄化和可能的人口逆流,上海应稳定人口总量,建设整体推进的多中心发展人口分布格局,完善户籍制度改革,破解"新二元结构",推动社会保障和公共服务均等化,同时结合政府改革,完善相关配套改革措施。     

An oracle-based algorithm for robust planning of production routing problems in closed-loop supply chains of beverage glass bottles

The demand for glass bottles is exhibiting an upward trend over time. The manufacturing of glass bottles is costlier in terms of time and resources and is associated with a higher level of heat generation and environmental pollution compared to recycling processes. In response to the aforementioned challenges, companies that use glass bottles need to implement strategies to manage their reverse supply chains in conjunction with their traditional supply chains, as the economic and environmental benefits of returned products are unquestionable. Closed-loop supply chains (CLSCs) integrate forward and reverse flows of products and information. This integration helps companies to have a broader view of the whole chain. Despite these advantages, managing CLSCs can be challenging as they are exposed to many uncertainties regarding supply and demand processes, travel times, and quantity/quality of returned products.In this study, we consider the production planning, inventory management, and vehicle routing decisions of a CLSC of beverage glass bottles. We propose an MILP model and rely on a multi-stage adjustable robust optimization (ARO) formulation to deal with the randomness in both the demand for filled bottles and the requests for pickups of empty bottles. We develop an exact oracle-based algorithm to solve the ARO problem and propose a heuristic search algorithm to reduce the solution time. Our numerical experiments not only show the incompetency of the customary method, namely the affine decision rule approach, but also illustrate how our algorithms can solve the small-size problems and significantly improve the quality of the obtained solution for large problems. Furthermore, our numerical results show that robust plans tend to be sparse, meaning the routes are chosen so that empty bottles are transported to production sites in such a way that fewer new bottles need to be ordered. Thus, robust planning makes the CLSCs more environmentally friendly.     

Reconstruction of populations by stochastic optimization: Sensitivity analysis

The reconstruction of populations by stochastic optimization solves the nontrivial problem of finding demographic flows from population registers or vital statistics and censuses, if available. These flows allow the reconstruction of stocks (age pyramids and vital statistics). After a review of reconstruction methods, the sensitivity analysis shows the robustness of the method by stochastic optimization to flawed or missing values, to the length of the reconstruction period, and to variations in the actual demographic flows.     

A note on the non-negativity of continuous-time ARMA and GARCH processes

A general approach for modeling the volatility process in continuous-time is based on the convolution of a kernel with a non-decreasing Lévy process, which is non-negative if the kernel is non-negative. Within the framework of Continuous-time Auto-Regressive Moving-Average (CARMA) processes, we derive a necessary condition for the kernel to be non-negative, and propose a numerical method for checking the non-negativity of a kernel function. These results can be lifted to solving a similar problem with another approach to modeling volatility via the COntinuous-time Generalized Auto-Regressive Conditional Heteroscedastic (COGARCH) processes.