多点配送中心选址及求解蚁群算法

将多点配送中心选址问题(multi-distribution center location problem,MDLP)映射为扩展K-TSP过程并设计了改进的蚁群算法.为提高算法的求解性能,在蚂蚁的选择规则里加入了代价引导函数,使用2-opt策略优化可行解并优化了信息素的更新方式.仿真算例及算法对比表明,模型和算法可以有效地表达和求解MDLP.     

蚁群算法在物流配送路径优化问题上的应用

物流配送路径优化是现代物流配送服务的关键环节之一,需要一个快速而有效的求解算法,构建了物流配送路径优化问题的数学模型,并应用蚁群算法加以求解。在模拟试验基础上,得出了蚁群算法对物流配送路径优化问题十分有效的结论,探索出了一条高效求解物流配送路径优化问题的新途径。     

基于遗传算法的高速公路网络规划模型优化

结合选址及路径优化模型对高速公路物流网络的优化问题展开研究,构建了高速公路物流网络规划模型并应用遗传算法进行求解分析,通过算法的改进设计提高了算法求解的效率和最优解的精度,以期对以高速公路路网为载体的社会物流服务改进提供参考。     

电力系统无功优化算法综述

总结了无功优化算法的研究现状,介绍了求解无功优化问题的常规方法和人工智能方法,并综合评述了现有优化方法的优缺点。同时还对无功优化算法进一步发展做了一些探讨。     

物流配送车辆调度问题算法综述

配送车辆调度优化问题旨在解决配送中路径和车辆调度问题的一类组合优化问题,是近年来物流控制优化领域的研究热点。文章对运输调度问题进行了分类总结,给出总体模型的概括描述,分析遗传算法、模拟退火算法、禁忌搜索算法、蚁群算法和微粒群算法的特点及其在求解配送车辆调度优化问题中的求解思路,并讨论了其求解现状,对未来研究方向进行展望,指出改进混合现有算法,开拓新算法将是更有效解决配送车辆调度问题的好方法。     

单亲遗传算法在TSP问题中的应用

基于单亲遗传算法的思想,编写了求解TSP问题的程序,给出了数值算例,并与遗传算法及蚁群算法在TSP问题的求解效率上进行了比较。结果表明,单亲遗传算法是求解TSP等组合优化问题的非常有效的算法。     

蚁群算法在卷烟配送路径优化中的应用研究

针对目前烟草商业企业的物流配送中存在的问题,提出用仿生学的蚁群优化算法对卷烟配送路径的选择进行优化,以基本的蚁群优化算法模型和原理为理论基础,建立了相应的数学模型,并结合某烟草公司的案例对本算法的应用进行了计算机仿真,实验结果表明该算法在求解卷烟配送的路径优化问题上是行之有效的。     

基于ACA的军事物流车辆配送路径优化研究

将蚂蚁算法应用到车辆配送的路径优化问题中,设计了求解车辆配送路径优化问题的模型,并应用实例加以仿真计算,最后的实例结果求解表明了该算法的有效和实用,将提高我军在现代战争中的精确保障能力。     

蚁群算法在卷烟配送路径优化中的应用研究

针对目前烟草商业企业的物流配送中存在的问题,提出用仿生学的蚁群优化算法对卷烟配送路径的选择进行优化,以基本的蚁群优化算法模型和原理为理论基础,建立了相应的数学模型,并结合某烟草公司的案例对本算法的应用进行了计算机仿真,实验结果表明该算法在求解卷烟配送的路径优化问题上是行之有效的.     

基于安全性目标的机位分配模型及算法

以分配在同一机位的相邻两架飞机产生的冲突概率最小为优化目标，并建立了整数规划模型，利用现代优化算法-遗传算法对该问题进行求解。该算法在计算时问、并行性和结果优化程度上都有一定的优势     

An integrated fuzzy mathematical model and principal component analysis algorithm for forecasting uncertain trends of electricity consumption

This paper introduces an integrated algorithm for forecasting electricity consumption (EL) based on fuzzy regression, time series and principal component analysis (PCA) in uncertain markets such as Iran. The algorithm is examined by mean absolute percentage error, analysis of variance (ANOVA) and Duncan Multiple Range Test. PCA is used to identify the input variables for the fuzzy regression and time series models. Monthly EL in Iran is used to show the superiority of the algorithm. Moreover, it is shown that the selected fuzzy regression model has better estimated values for total EL than time series. The algorithm provides as good results as intelligent methods. However, it is shown that the algorithm does not require utilization of preprocessing methods but genetic algorithm, artificial neural network and fuzzy inference system require preprocessing which could be a cumbersome task to deal with ambiguous data. The unique features of the proposed algorithm are three fold. First, two type of fuzzy regressions with and without preprocessed data are prescribed by the algorithm in order to minimize the bias. Second, it uses PCA approach instead of trial and error method for selecting the most important input variables. Third, ANOVA is used to statistically compare fuzzy regression and time series with actual data.     

自适应粒子群优化算法在混沌系统参数估计中的应用

惯性权重w的变化会影响粒子群优化算法的搜索能力,本文针对基本粒子群算法不能适应复杂的非线性优化搜索过程的问题,在其基础上提出了一种动态改变惯性权的自适应粒子群算法。该自适应算法引入了h来衡量算法的进化速度,引入s来衡量算法的粒子聚集度,并将其作为函数w的变量,使w与算法的运行状态相关,从而使算法具有动态自适应性。最后,本文引入了两个经典的测试函数对该PSO算法进行测试,结果表明该算法明显优于基本PSO算法。     

Urban housing with discrete structures

Competitive equilibrium in an urban housing model with structures that cannot be readily aggregated or subdivided is analyzed in this paper. The price and quantity of services forthcoming from each dwelling occupied by a household is determined by the equality of competitive supply and demand. A stack algorithm is then developed to ensure households are assigned to dwellings so that they would not prefer to live in any other dwelling with an equilibrium price less than the one they would pay if they lived there. Using the Urban Institute Housing Model as an example, the results of this algorithm are compared with those of the algorithm developed by de Leeuw and Struyk (D-S). The stack algorithm yields greater price discounting of existing dwellings below the price of a new dwelling than does the D-S algorithm and, thus, a greater potential for housing prices to be increased by a demand subsidy such as a housing allowance.     

Estimating latent class model parameters for filter questions with skip patterns

Filter questions with skip patterns have been widely used in survey research, and latent class models (LCM) are often used to analyze this type of categorical data. The LCM parameters are usually estimated by means of an EM (expectation maximization) algorithm. When the pattern is present, the non-response of the skip pattern cannot be treated as random missingness. We thus propose a modified algorithm to estimate the latent class parameters when non-response is present, and the approach is attractive for two reasons. First, the latent class model with the algorithm is very flexible in the sense that it can model the association of variables with the skip patterns under study. Secondly, the algorithm can be easily implemented using any computer language. An empirical example is used to demonstrate the usefulness of the algorithm. The algorithm may also be flexibly generalized to more complex surveys, for example, polytomous responses.     

A minimum chi-squared method for indirect parameter estimation from Poisson data

In this paper, we introduce a new algorithm for estimating non-negative parameters from Poisson observations of a linear transformation of the parameters. The proposed objective function fits both a weighted least squares (WLS) and a minimum χ² estimation framework, and results in a convex optimization problem. Unlike conventional WLS methods, the weights do not need to be estimated from the datas, but are incorporated in the objective function. The iterative algorithm is derived from an alternating projection procedure in which "distance" is determined by the chi-squared test statistic, which is interpreted as a measure of the discrepancy between two distributions. This may be viewed as an alternative to the Kullback-Leibler divergence which corresponds to the maximum likelihood (ML) estimation. The algorithm is similar in form to, and shares many properties with, the expectation maximization algorithm for ML estimation. In particular, we show that every limit point of the algorithm is an estimator, and the sequence of projected (by the linear transformation into the data space) means converge. Despite the similarities, we show that the new estimators are quite distinct from ML estimators, and obtain conditions under which they are identical.     

车辆数不确定的软时间窗车辆路径问题的改进遗传算法

综合考虑车辆数和行驶距离两种优化目标,提出了VRPSTW的多目标优化模型,同时提出了解决VRPSTW问题的一种改进遗传算法。在算法中,通过适应度函浸透的变化,较好地解决了多目标优化的问题;通过对交叉算子改进,增加了算法的寻优能力,同时又克服了算法对群体多样性的要求;针对遗传算法局部搜索能力弱的问题。加入了2-opt局部搜索方法,很好地弥补了遗传算法的不足。经过实验,本方法能较好地解决VRPSTW问题,从而对运榆决策提供有力支持。     

Robust multivariate analysis for mixed-type data: Novel algorithm and its practical application in socio-economic research

We propose a novel method and algorithm for the analysis and clustering of mixed-type data using a hierarchical approach based on Forward Search. In our procedure, the identification of groups is based on the identification of similar trajectories and then linked to very intuitive two-dimensional maps. The proposed algorithm can use different measures for the calculation of distance in the case of mixed-type data, such as Gower’s metric and Related metric scaling. A key feature of our algorithm is its ability to discard redundant information from a given set of variables. The practical usefulness of the algorithm is illustrated through two applications of high relevance for empirical economic research. The first one focuses on comparing different indicators of environmental policy stringency in different countries. The second one applies our procedure to identify clusters of countries based on information regarding their institutional characteristics.     

An integrated approach to the development of continuous simulations

An integrated approach to the development of Forrester-style simulation models is described. The approach incorporates the concept of an interaction matrix to assist in the development of causal loop diagrams and Dynamo flow diagrams. The interaction matrix is derived from the fundamental notions of system dynamics. Premised upon the presumption that a computer-aided procedure for model formulation can expedite, systematize, and operationalize the model formulation process, the integrated approach utilizes the interaction matrix as a data structure within the computer. An algorithm designed to interface with a remote terminal (such as a CRT display) determines the interaction matrix by interrogating a user until sufficient information about the problem of interest has been obtained. This algorithm is also described in the paper. The interrogations both motivate and facilitate the determination of quantities to be included as well as couplings between the quantities. When a quantity or coupling is designated by a user, the algorithm “knows” its identity at the time of user origination. Both algorithm and matrix are illustrated through recourse to a text-book example and the paper concludes with a summarizing discussion of the possible contribution of such an approach.     

卷烟厂制丝车间自动排产问题研究

以卷烟厂为研究背景,对制丝车间生产特点和排产需求进行分析,提出了基于规则的启发式算法,以解决卷烟厂制丝车间排产问题。以生产均衡为目标,构建以工段为整体的倒序排产的制丝车间排产算法,并将该算法应用于某卷烟厂。经实际数据验证,该算法可很好地满足制丝排产的需求。