生鲜农产品多配送中心连续选址FCM-ISA算法及应用

本文对生鲜农产品多配送中心连续选址问题进行了研究,在建立考虑最小距离约束下连续选址模型的基础上,针对以往连续选址模型求解过程中采用随机方式生成初始解会造成算法搜索范围过大且易陷入局部最优的局限,创新性地提出了连续选址模型的模糊C均值聚类-改进模拟退火(FCM-ISA)算法,并以杭州市为例验证了所建模型及设计算法的有效性。计算结果表明,本文所建立的生鲜农产品多配送中心连续选址模型更符合实际选址情景,设计的FCM-ISA算法收敛速度快且全局寻优效果好,对科学地进行生鲜农产品多配送中心选址决策具有重要的指导意义。     

改进的蝗虫优化算法在双目标应急物资中心选址问题中的应用

近年来世界各地频发灾情疫情等紧急事件,严重影响人民的生活物资保障。在这种情况下,急需建立应急物资中心来缓解燃眉之急。该类问题通常面临资源稀缺并且时间相对紧迫的处境,因此需要在短时间内获得合理的应急设施选址方案来提升服务的质量和效率。本文对应急物资中心选址问题展开研究,提出一种考虑后续运输成本以及有概率发生紧急事件而导致无法正常运送物资的双目标离散选址模型,并为此设计一种二进制多目标蝗虫优化算法。该算法采用模糊关联熵系数来引导迭代更新,同时为其添加外部档案,最优解选择机制和竞争决策机制来提升算法性能。多次数值实验表明该算法的计算效率和求解质量较高,可作为应急物资中心选址问题的一种可行且有效的算法。     

考虑需求不确定的多级应急物流设施选址研究

为了对急物流设施选址问题进行合理的研究,建立了包含配送中心、配送点和需求点的多级应急物流网络。基于应急物资需求特点,使用三角模糊数表示应急物资需求的不确定性,同时考虑应急救援成本和应急救援时间两个目标,建立了应急物流设施选址模型。采用去模糊化方法将三角模糊数转化为确定数,利用成本和时间的单目标的最优结果将多目标转化为相对值,再对时间和成本目标进行加权处理,既消除了不同目标之间的单位及数量级差异,还可以进行动态调整。设计了遗传算法对模型进行求解,通过实际算例表明了模型和算法可以有效地解决应急物流设施选址问题。     

基于微分进化算法的FCM图像分割算法

为提高模糊C均值(FCM)算法的自动化程度,提出基于微分进化算法的FCM图像分割算法(DEFCM),利用微分进化算法全局性和鲁棒性的特点自动确定分类数和初始聚类中心,再将其作为模糊c均值聚类的初始聚类中心,弥补FCM算法的不足.实验表明该算法不仅能够正确地对图像分类,而且能获得较好的图像分割效果和质量.     

An new initialization method for fuzzy c-means algorithm

In this paper an initialization method for fuzzy c-means (FCM) algorithm is proposed in order to solve the two problems of clustering performance affected by initial cluster centers and lower computation speed for FCM. Grid and density are needed to extract approximate clustering center from sample space. Then, an initialization method for fuzzy c-means algorithm is proposed by using amount of approximate clustering centers to initialize classification number, and using approximate clustering centers to initialize initial clustering centers. Experiment shows that this method can improve clustering result and shorten clustering time validly.     

Maximum cut in fuzzy nature: Models and algorithms

The maximum cut (Max-Cut) problem has extensive applications in various real-world fields, such as network design and statistical physics. In this paper, a more practical version, the Max-Cut problem with fuzzy coefficients, is discussed. Specifically, based on credibility theory, the Max-Cut problem with fuzzy coefficients is formulated as an expected value model, a chance-constrained programming model and a dependent-chance programming model respectively according to different decision criteria. When these fuzzy coefficients are represented by special fuzzy variables like triangular fuzzy numbers and trapezoidal fuzzy numbers, the crisp equivalents of the fuzzy Max-Cut problem can be obtained. Finally, a genetic algorithm combined with fuzzy simulation techniques is designed for the general fuzzy Max-Cut problem under these models and numerical experiment confirms the effectiveness of the designed genetic algorithm.     

Capacitated location allocation problem with stochastic location and fuzzy demand: A hybrid algorithm

In this article, a capacitated location allocation problem is considered in which the demands and the locations of the customers are uncertain. The demands are assumed fuzzy, the locations follow a normal probability distribution, and the distances between the locations and the customers are taken Euclidean and squared Euclidean. The fuzzy expected cost programming, the fuzzy β-cost minimization model, and the credibility maximization model are three types of fuzzy programming that are developed to model the problem. Moreover, two closed-form Euclidean and squared Euclidean expressions are used to evaluate the expected distance between customers and facilities. In order to solve the problem at hand, a hybrid intelligent algorithm is applied in which the simplex algorithm, fuzzy simulation, and a modified genetic algorithm are integrated. Finally, in order to illustrate the efficiency of the proposed hybrid algorithm, some numerical examples are presented.     

Project scheduling problem with mixed uncertainty of randomness and fuzziness

Project scheduling problem is to determine the schedule of allocating resources so as to balance the total cost and the completion time. This paper considers project scheduling problem with mixed uncertainty of randomness and fuzziness, where activity duration times are assumed to be random fuzzy variables. Three types of random fuzzy models as expected cost minimization model, (α, β)-cost minimization model and chance maximization model are built to meet different management requirements. Random fuzzy simulations for some uncertain functions are given and embedded into genetic algorithm to design a hybrid intelligent algorithm. Finally, some numerical experiments are given for the sake of illustration of the effectiveness of the algorithm.     

Fuzzy facility location-allocation problem under the Hurwicz criterion

Facility location-allocation (FLA) problem has been widely studied by operational researchers due to its many practical applications. Many researchers have studied the FLA problem in a deterministic environment. However, the models they proposed cannot accommodate satisfactorily various customer demands in the real world. Thus, we consider the FLA problem with uncertainties. In this paper, a new model named α-cost model under the Hurwicz criterion is presented with fuzzy demands. In order to solve this model, the simplex algorithm, fuzzy simulations and a genetic algorithm are integrated to produce a hybrid intelligent algorithm. Finally, some numerical examples are presented to illustrate the effectiveness of the proposed algorithm.     

Fuzzy minimum weight edge covering problem

Minimum weight edge covering problem, known as a classic problem in graph theory, is employed in many scientific and engineering applications. In the applications, the weight may denote cost, time, or opponent’s payoff, which can be vague in practice. This paper considers the edge covering problem under fuzzy environment, and formulates three models which are expected minimum weight edge cover model, α-minimum weight edge cover model, and the most minimum weight edge cover model. As an extension for the models, we respectively introduce the crisp equivalent of each model in the case that the weights are independent trapezoidal fuzzy variables. Due to the complexity of the problem, a hybrid intelligent algorithm is employed to solve the models, which can deal with the problem with any type of fuzzy weights. At last, some numerical experiments are given to show the application of the models and the robustness of the algorithm.     

ETSK模糊模型辨识及模糊控制算法

本文提出一种基于扩张原理的ＥＴＳＫ（ＥｘｔｅｎｄｅｄＴＳＫ）模型,导出了该模型的输入输出解析式,给出了辨识这种模型的方法。本文还导出了ＥＴＳＫ模型的一种等价形式——变权ＴＳＫ模型,从而将ＥＴＳＫ模型规则后件中的模糊数及其扩展运算转化为普通数的运算,使基于ＥＴＳＫ模型的模糊控制算法ＭＢＦＣ（Ｍｏｄｅｌ－ＢａｓｅｄＦｕｚｚｙＣｏｎｔｒｏｌ）易于实现。仿真辨识结果表明,ＥＴＳＫ模型的辨识效果和预报精度优于ＴＳＫ和ＬＭ模型;ＭＢＦＣ算法的控制效果优于通常模型ＰＩ控制算法     

Dynamic pricing model and algorithm for perishable products with fuzzy demand

This paper studies the dynamic pricing problem of selling fixed stock of perishable items over a finite horizon, where the decision maker does not have the necessary historic data to estimate the distribution of uncertain demand, but has imprecise information about the quantity demand. We model this uncertainty using fuzzy variables. The dynamic pricing problem based on credibility theory is formulated using three fuzzy programming models, viz.: the fuzzy expected revenue maximization model, α‐optimistic revenue maximization model, and credibility maximization model. Fuzzy simulations for functions with fuzzy parameters are given and embedded into a genetic algorithm to design a hybrid intelligent algorithm to solve these three models. Finally, a real‐world example is presented to highlight the effectiveness of the developed model and algorithm. Copyright © 2009 John Wiley & Sons, Ltd.     

Portfolio selection based on fuzzy cross-entropy

In this paper, the Kapur cross-entropy minimization model for portfolio selection problem is discussed under fuzzy environment, which minimizes the divergence of the fuzzy investment return from a priori one. First, three mathematical models are proposed by defining divergence as cross-entropy, average return as expected value and risk as variance, semivariance and chance of bad outcome, respectively. In order to solve these models under fuzzy environment, a hybrid intelligent algorithm is designed by integrating numerical integration, fuzzy simulation and genetic algorithm. Finally, several numerical examples are given to illustrate the modeling idea and the effectiveness of the proposed algorithm.     

基于遗传算法的随机模糊立体运输问题

随机模糊立体运输问题的研究是为了解决现实生活中双因素不确定性问题,在遗传算法的基础上,运用可信性理论建立随机模糊运输问题的机会约束规划模型.通过算例进行VC++编程模拟计算,验证了此模型的可行性,最终提出了基于遗传算法解决随机模糊立体运输问题的模型.     

考虑设施中断情景的震后救援初期应急物流网络优化设计

在震后救援初期,构建合理的应急物流网络,对于快速有效供应应急物资、减轻灾情具有重大价值。在传统可靠性选址问题与应急设施选址-分配问题的基础上,考虑震后救援初期的阶段性特征、设施中断情景、多品种模糊需求、设施能力限制等因素,建立了一个适用于震后救援初期的应急设施选址-分配模型。通过三角模糊数的期望值公式将模糊需求去模糊化。在此基础上,考虑模型特点,设计了一种整数编码的混合遗传算法。最后,以5·12汶川地震为背景,构造算例进行数值仿真。验证了所提模型和算法。结果表明:考虑设施中断情景后,即使系统中的部分设施失效,整个网络仍能较好运行,且优化结果更具可靠性和稳健性。     

模糊人工蜂群算法的多选择多维背包问题求解

针对传统人工蜂群算法早熟收敛问题,基于模糊化处理和蜂群寻优的特点,提出一种模糊人工蜂群算法。将模糊输入输出机制引入到算法中来保持蜜源访问概率的动态更新。根据算法计算过程中的不同阶段对蜜源访问概率有效调整,避免算法陷入局部极值。通过对多选择多维背包问题的仿真实验和与其他算法的比较,表明本算法可行有效,有良好的鲁棒性。     

Fuzzy project scheduling problem and its hybrid intelligent algorithm

Project scheduling problem is to determine the schedule of allocating resources so as to balance the total cost and the completion time. This paper considers a type of project scheduling problem with fuzzy activity duration times. According to some management goals, three types of fuzzy models are built to solve the project scheduling problem. Moreover, the technique of fuzzy simulation and genetic algorithm are integrated to design a hybrid intelligent algorithm to solve the fuzzy models. Finally, some numerical examples are given to illustrate the effectiveness of the algorithm.     

Random fuzzy EOQ model with imperfect quality items

This paper investigates an economic order quantity (EOQ) problem with imperfect quality items, where the percentage of imperfect quality items in each lot is characterized as a random fuzzy variable while the setup cost per lot, the holding cost of each unit item per day, and the inspection cost of each unit item are characterized as fuzzy variables, respectively. In order to maximize the expected long-run average profit, a random fuzzy EOQ model is constructed. Since it is almost impossible to find an analytic method to solve the proposed model, a particle swarm optimization (PSO) algorithm based on the random fuzzy simulation is designed. Finally, the effectiveness of the designed algorithm is illustrated by a numerical example.     

基于交通流的多模糊时间窗车辆路径优化

研究了基于交通流的多模糊时间窗车辆路径问题,考虑了实际中不断变化的交通流以及客户具有多个模糊时间窗的情况,以最小化配送总成本和最大化客户满意度为目标,构建基于交通流的多模糊时间窗车辆路径模型。根据伊藤算法的基本原理,设计了求解该模型的改进伊藤算法,结合仿真算例进行了模拟计算,并与蚁群算法的计算结果进行了对比分析,结果表明,利用改进伊藤算法求解基于交通流的多模糊时间窗车辆路径问题,迭代次数小,效率更高,能够在较短的时间内收敛到全局最优解,可以有效的求解多模糊时间窗车辆路径问题。     

A cutting plane algorithm for MV portfolio selection model

This paper deals with a portfolio selection problem with fuzzy return rates. A possibilistic mean variance (FMVC) portfolio selection model was proposed. The possibilistic programming problem can be transformed into a linear optimal problem with an additional quadratic constraint by possibilistic theory. For such problems there are no special standard algorithms. We propose a cutting plane algorithm to solve (FMVC). The nonlinear programming problem can be solved by sequence linear programming problem. A numerical example is given to illustrate the behavior of the proposed model and algorithm.