Cost-time Minimization in a Transportation Problem with Fuzzy Parameters: A Case Study

In real world applications the supply, the demand and the transportation cost per unit of the quantities in a transportation problem are hardly specified precisely because of the changing economic and environmental conditions. It is also important that the time required for transportation should be minimum. In this paper a method has been proposed for the minimization of transportation cost as well as time of transportation when the demand, supply and transportation cost per unit of the quantities are fuzzy. The problem is modeled as multi objective linear programming problem with imprecise parameters. Fuzzy parametric programming has been used to handle impreciseness and the resulting multi objective problem has been solved by prioritized goal programming approach. A case study has been made using the proposed approach.     

Sampling of alternatives for route choice modeling

This paper presents a new paradigm for choice set generation in the context of route choice model estimation. We assume that the choice sets contain all paths connecting each origin–destination pair. Although this is behaviorally questionable, we make this assumption in order to avoid bias in the econometric model. These sets are in general impossible to generate explicitly. Therefore, we propose an importance sampling approach to generate subsets of paths suitable for model estimation. Using only a subset of alternatives requires the path utilities to be corrected according to the sampling protocol in order to obtain unbiased parameter estimates. We derive such a sampling correction for the proposed algorithm.Estimating models based on samples of alternatives is straightforward for some types of models, in particular the multinomial logit (MNL) model. In order to apply MNL for route choice, the utilities should also be corrected to account for the correlation using, for instance, a path size (PS) formulation. We argue that the PS attribute should be computed based on the full choice set. Again, this is not feasible in general, and we propose a new version of the PS attribute derived from the sampling protocol, called Expanded PS.Numerical results based on synthetic data show that models including a sampling correction are remarkably better than the ones that do not. Moreover, the Expanded PS shows good results and outperforms models with the original PS formulation.     

浅谈三角高程测量在麦地河电站中的应用

随着电磁波测距仪、全站仪的普及,EDM三角高程测量越来越受到关注,应用范围也不断扩大。本文仅结合一些工作中的实例作了简略的阐述。     

应用模糊数学优化潭邵高速公路软基处理方案

地基方案的优化选择,就是使所选的地基方案满足多种要求。地基方案的优化受技术、经济、环保等多项指标的影响。聚类分析是模糊数学中研究物以类聚的一种多元分析方法。针对潭邵高速公路软土地基处理的特殊性,应用模糊数学理论对工程设计方案中的模糊性进行了分析,最终确定,潭邵高速公路软土地基,采用砂桩处理方案进行加固。     

基于支持向量机的水下机器人操纵运动参数辨识

根据平面运动机构试验所得到的水动力系数确定水下运载器的运动方程,应用最小二乘支持向量机对水下运载器的试验数据进行分析,辨识得到了水动力系数,并将辨识得到的参数同平面运动机构试验所确定的参数相比,验证了最小二乘支持向量机进行水下运载器操纵运动参数辨识的有效性。根据所辨识的水动力系数进行了模型验证,证明所辨识得到的水动力参数是可靠的。该方法对水下运载器的操纵和控制的研究有重要意义。     

轴向荷载作用下K型管节点焊缝周围应力分布的参数公式

应用分区产生网格的方法产生了K型节点的有限元模型,计算了在轴向荷载作用下K型节点焊缝周围的热点应力分布情况,并与相关试验结果进行对比分析,验证了所提出的有限元模型的可行性和准确性。通过对1152个K节点模型分析,研究了几何参数对K型节点焊缝周围热点应力分布的影响,发现在几何参数取不同值时热点应力的分布随几何参数的变化发生改变,而且热点应力的位置也随着几何参数的变化在冠点和鞍点之间移动。并在此几何参数分析的基础上,提出了K型节点焊缝周围应力集中系数分布的参数公式,并对参数公式进行了误差分析。对于绝大多数K节点模型,拟合得到的参数公式所计算的焊缝周围应力分布结果是精确可靠的,所以提出的参数公式为工程中K节点疲劳设计和分析提供了参考方法。     

基于知识工程的自升式钻井平台方案设计方法

针对目前自升式钻井平台方案设计特点,引入知识工程的理念,建立面向对象的知识表达方法及基于实例和规则的混合推理机制,采用参数化设计方法进行特征驱动,在快速生成设计方案的同时对设计进行检验并提出建议,使设计人员的工作重点从数据处理转移到对设计对象的分析上来.开发了原型系统,证明了该方法的有效性和实用性.     

Experimental Investigation of TARMAX Model for Modeling of Hydrodynamic Forces on Cylinder-like Structures

A new approach that models lift and drag hydrodynamic force signals operating over cylindrical structures was developed and validated. This approach is based on stochastic auto regressive moving average with exogenous (ARMAX) input and its time-varying form, TARMAX. Model structure selection and parameter estimation were discussed while considering the validation stage. In this paper, the cylindrical structure was considered as a dynamic system with an incoming water wave and resulting forces as the input a...     

The multinomial logit model revisited: A semi-parametric approach in discrete choice analysis

The multinomial logit model in discrete choice analysis is widely used in transport research. It has long been known that the Gumbel distribution forms the basis of the multinomial logit model. Although the Gumbel distribution is a good approximation in some applications such as route choice problems, it is chosen mainly for mathematical convenience. This can be restrictive in many other scenarios in practice. In this paper we show that the assumption of the Gumbel distribution can be substantially relaxed to include a large class of distributions that is stable with respect to the minimum operation. The distributions in the class allow heteroscedastic variances. We then seek a transformation that stabilizes the heteroscedastic variances. We show that this leads to a semi-parametric choice model which links the linear combination of travel-related attributes to the choice probabilities via an unknown sensitivity function. This sensitivity function reflects the degree of travelers’ sensitivity to the changes in the combined travel cost. The estimation of the semi-parametric choice model is also investigated and empirical studies are used to illustrate the developed method.     

Experimental design influences on stated choice outputs: An empirical study in air travel choice

Discrete choice experiments are conducted in the transport field to obtain data for investigating travel behaviour and derived measures such as the value of travel time savings. The multinomial logit (MNL) and other more advanced discrete choice models (e.g., the mixed MNL model) have often been estimated on data from stated choice experiments and applied for planning and policy purposes. Determining efficient underlying experimental designs for these studies has become an increasingly important stream of research, in which the objective is to generate stated choice tasks that maximize the collected information, yielding more reliable parameter estimates. These theoretical advances have not been rigorously tested in practice, such that claims on whether the theoretical efficiency gains translate into practice cannot be made. Using an extensive empirical study of air travel choice behaviour, this paper presents for the first time results of different stated choice experimental design approaches, in which respective estimation results are compared. We show that D-efficient designs keep their promise in lowering standard errors in estimating, thereby requiring smaller sample sizes, ceteris paribus, compared to a more traditional orthogonal design. The parameter estimates found using an orthogonal design or an efficient design turn out to be statistically different in several cases, mainly attributed to more or less dominant alternatives existing in the orthogonal design. Furthermore, we found that small designs with a limited number of choice tasks performs just as good (or even better) than a large design. Finally, we show that theoretically predicted sample sizes using the so-called S-estimates provide a good lower bound. This paper will enable practitioners in better understanding the potential benefits of efficient designs, and enables policy makers to make decisions based on more reliable parameter estimates.