Solving fuzzy capacitated location routing problem using hybrid variable neighborhood search and evolutionary local search

A fuzzy capacitated location routing problem (FCLRP) is solved by using a heuristic method that combines variable neighborhood search (VNS) and evolutionary local search (ELS). Demands of the customer and travel times between customers and depots are considered as fuzzy and deterministic variables, respectively in FCLRP. Heterogeneous and homogeneous fleet sizes are performed together to reach the least multi-objective cost in a case study. The multi-objective cost consists of transportation cost, additional cost, vehicle waiting cost and delay cost. A fuzzy chance constrained programming model is added by using credibility theory. The proposed method reaches the solution by performing four stages. In the first stage, initial solutions are obtained by using a greedy heuristic method, and then VNS heuristic, which consists of seven different neighborhood structures, is performed to improve the solution quality in the second stage. In the third stage, a perturbation procedure is applied to the improved solution using ELS algorithm, and then VNS heuristic is applied again in the last stage. The combination of VNS and ELS is called VNSxELS algorithm and applied to a case study, which has fifty-seven customers and five distributing points, effectively in a reasonable time.     

‘Problem families’ in public housing: discourse,commentary and (dis)order

This article contributes to a chain of literature extending back to the late nineteenth century on the ‘problem family’, particularly when encountered by housing providers as the ‘problem’ tenant. Using archival evidence of the techniques employed by one social housing provider in the mid-1970s to identify a definitive solution to the challenges posed by ‘problem’ households, I trace the patterns and practices in ‘problem family’ discourse more broadly, and their intersection with those of other discursive fields, particularly eugenics and social work. I show how attempts to define, identify and design models of rehabilitation for ‘problem families’ can be understood as forms of a discursive strategy which Foucault identified as ‘commentary’, and argue that such commentary remains intrinsic to welfare state efforts to tackle entrenched disadvantage.     

基于伪布尔模型和启发式算法求解无容量设施选址问题

采用伪布尔模型和启发式算法来求解无容量设施选址问题。首先给出了问题的伪布尔(pseudo-Boolean)表示，然后基于Khumawala规则对问题进行预处理，最后提出两种启发式分支准则来求解问题。实验结果表明所提算法简单有效。     

Polynomial filtering for nonlinear stochastic systems with state‐ and disturbance‐dependent noises

This article is concerned with the polynomial filtering problem for a class of nonlinear stochastic systems governed by the Itô differential equation. The system under investigation involves polynomial nonlinearities, unknown‐but‐bounded disturbances, and state‐ and disturbance‐dependent noises ((x,d)‐dependent noises for short). By expanding the polynomial nonlinear functions in Taylor series around the state estimate, a new polynomial filter design method is developed with hope to reduce the conservatism of the existing results. In virtue of stochastic analysis and inequality technique, sufficient conditions in terms of parameter‐dependent linear matrix inequalities (PDLMIs) are derived to guarantee that the estimation error system is input‐to‐state stable in probability. Moreover, the desired polynomial matrix can be obtained by solving the PDLMIs via the sum‐of‐squares approach. The effectiveness and applicability of the proposed method are illustrated by two numerical examples with one concerning the permanent magnet synchronous motor.     

基于模糊故障树的数控龙门铣床加工误差分析与试验

针对数控龙门铣床加工中出现的平面度误差问题进行研究与改进,分析工艺系统因素产生的误差,建立加工平面误差的模糊故障树,通过下行法对其进行简化分析,定性分析影响加工平面度误差的最小割集,运用模糊隶属函数定量分析顶事件的发生概率及底事件的发生概率重要度,确定主要因素为工作台形位误差、定位元件误差、热变形等。使用激光测量技术对安装的工作台平面进行矩形布点测量,根据采集数据建立基于最小二乘法的数学模型,运用Matlab软件定性分析平面度的评定,并对超差区域进行改进,最终实现了机床装配与使用要求。     

双层过道布置问题的混合整数非线性规划模型及两阶段改进模拟退火算法

结合实际布局活动中，设施在多层空间布置的实际情况，提出了双层过道布置问题，并构建了该问题的混合整数非线性规划（MINLP）模型。针对问题特征，提出了一种改进模拟退火算法，通过采用两阶段改进策略，对退火过程及抽样过程进行改进。该算法以自适应搜索策略替代马氏链搜索长度，并引入记忆功能、回火操作以提高求解质量，通过设置双阈值来提高求解效率。应用所提算法对24个基准算例进行测试，并将其与原模拟退火算法和GUROBI精确方法作对比，验证了所提算法的有效性。     

Probabilistic characterization of the effect of transient stochastic loads on the fatigue-crack nucleation time

The rainflow counting algorithm for material fatigue is both simple to implement and extraordinarily successful for predicting material failure times. However, it neglects memory effects and time-ordering dependence, and therefore runs into difficulties dealing with highly intermittent or transient stochastic loads with heavy tailed distributions. Such loads appear frequently in a wide range of applications in ocean and mechanical engineering, such as wind turbines and offshore structures. In this work we employ the Serebrinsky–Ortiz cohesive envelope model for material fatigue to characterize the effects of load intermittency on the fatigue-crack nucleation time. We first formulate efficient numerical integration schemes, which allow for the direct characterization of the fatigue life in terms of any given load time-series. Subsequently, we consider the case of stochastic intermittent loads with given statistical characteristics. To overcome the need for expensive Monte-Carlo simulations, we formulate the fatigue life as an up-crossing problem of the coherent envelope. Assuming statistical independence for the large intermittent spikes and using probabilistic arguments we derive closed expressions for the up-crossing properties of the coherent envelope and obtain analytical approximations for the probability mass function of the failure time. The analytical expressions are derived directly in terms of the probability density function of the load, as well as the coherent envelope. We examine the accuracy of the analytical approximations and compare the predicted failure time with the standard rainflow algorithm for various loads. Finally, we use the analytical expressions to examine the robustness of the derived probability distribution for the failure time with respect to the coherent envelope geometrical properties.     

Thermal and rheological effects in a classical Graetz problem using a nonlinear Robertson-Stiff fluid model

The present article elaborates the Graetz problem for the Robertson-Stiff fluid model with imposed iso-thermal conditions. The closed-form expression of Robertson-Stiff fluid velocity is obtained. Employing the classical separation of variables approach, the energy equation of the said problem is reduced into an eigenvalue problem. The solution of the eigenvalue problem is developed numerically via the MATLAB built-in algorithm BVP4C. The constants appearing in series solutions are computed by Simpson's rule. The special case of this analysis with appropriate scaling is also applicable for the Bingham, power-law, and Newtonian fluid models. The impact of the dissipation function on Nusselt numbers and mean temperature is also considered. The pictorial representation of average temp7erature and Nusselt number are discussed in the presence of the plug radius, power-law index, and Brinkman number. It is observed that the presence of the plug radius and power-law index delay the prevalence of fully developed conditions for the Nusselt number. Moreover, the local Nusselt number for channel confinement attains higher values as compared with tube confinement. The present investigation has numerous applications in the field of engineering, nanotechnology, biomedical sciences, and development of several thermal types of equipment or microfluidic devices.     

复杂系统的设计认知和创新

目的研究复杂系统设计过程中设计师的设计认知和设计创新行为。方法通过国内外相关文献的研究和分析,总结归纳复杂系统设计问题界定和解决方案构思的认知过程、复杂系统创新设计构思的生成机制、复杂系统的创新设计策略,分析复杂系统设计认知和创新研究领域将发生的变革和未来发展的趋势。结论系统探讨了复杂系统设计认知与创新过程中的设计问题界定和解决方案构思的联合演化机制、给定和自发性解决方案示例,给设计师带来的认知固化和类比推理创新启发作用、结构化和机会主义的创新设计策略,发现当下的复杂系统创新设计理论和实践研究,还需要进一步开展跨学科知识融合激励的复杂系统创新设计研究,与此同时,加强关于设计师主观认知不确定性的定量建模研究,从而更好、更有针对性地探究复杂系统创新设计中认知过程的自然本质。     

The provably good parallel seeding algorithms for the k-means problem with penalties

As a classic NP-hard problem in machine learning and computational geometry, the k-means problem aims to partition the given points into k sets to minimize the within-cluster sum of squares. The k-means problem with penalties (k-MPWP), as a generalizing problem of the k-means problem, allows a point that can be either clustered or penalized with some positive cost. In this paper, we mainly apply the parallel seeding algorithm to the k-MPWP, and show sufficient analysis to bound the expected solution quality in the case where both the number of iterations and the number of points sampled in each iteration can be given arbitrarily. The approximate guarantee can be obtained as , where is a polynomial function involving the maximal ratio M between the penalties. On one hand, this result can be viewed as a further improvement on the parallel algorithm for k-MPWP given by Li et al., where the number of iterations depends on other factors. On the other hand, our result also generalizes the one solving the k-means problem presented by Bachem et al., because k-MPWP is a variant of the k-means problem. Moreover, we present a numerical experiment to show the effectiveness of the parallel algorithm for k-means with penalties.