基于混沌集群智能优化算法的多目标粗糙集属性约减

针对管理实践及大数据处理过程中具有多决策属性的粗糙集属性约减问题,将条件属性依赖度与知识分辨度进行结合构建属性权重,分别建立针对不同决策属性的约减目标函数,引入帕累托最优思想,将基于多决策属性的粗糙集属性约减问题转化为离散多目标优化问题。针对该问题的结构设计了具有集群智能优化思想的元胞自动机求解算法,在算法中引入基于个体的非支配解集平衡局部最优与全局最优的关系,引入混沌遗传算子增加种群多样性。以某铁路局设备安全风险处理数据为案例构建多决策属性粗糙集决策表进行优化计算并进行管理决策分析。研究发现:(1)相对于传统的NSGA-II与MO-cell算法,本文提出的算法具有更强的多目标属性挖掘性能;(2)帕累托最优思想可以较好地解释多决策属性粗糙集在管理实践中的意义。     

基于Agent与分解协调的综合生产计划研究

以作业单元为局部决策Agent,车间管理者为全局协调Agent,引入生产节点间的内部结算价格,基于多Agent系统,建立了综合生产计划的分布式决策模型.通过将局部Agent决策目标的总和与全局Agent决策目标进行对比,证明了所引入的内部结算价格就是全局Agent目标函数关于物流平衡约束的Lagrange乘子.基于Lagrange分解协调原理,设计了局部作业单元Agent和全局协调Agent的迭代协调算法.该迭代算法以上次计算的中间结果作为对其它作业单元生产需求的估计,从而能将各个生产单元Agent的决策模型分离,实现了分布建模与求解.在算例研究中使用启发式规则来确定Lagrange乘子迭代的步长系数,保证了较好的收敛性,证明模型和算法是有效的.     

基于多目标进化算法的供水系统优化运行研究

将多目标进化算法与启发式算法相接合,对供水管网微观模型进行优化调度研究.目标函数为供水系统的运行费用和维护费用最小化,以及水压服务水平的最大化(保证安全供水),以各泵站各型号水泵的开启和调速泵的转数比为决策变量,进行二进制-实数混合编码,并采用新型的交叉算子.运用NSGA-Ⅱ、epsilon-MOEA、SPEA2三种多目标进化方法求解优化运行模型,并通过工程算例进行比较.应用表明,多目标进化算法能为供水系统的优化决策提供支持.     

大型网络基于连通可靠度的最优可靠度分配

由于网络连通可靠度计算属于NP-hard问题,当系统可靠度无法显式表达时,基于连通可靠度的大型复杂网络优化通常只能采用启发式优化算法解决．通过对复杂网络连通可靠度算法结构的分析,给出了系统连通可靠度的Taylor方程．采用遗传算法,由系统连通可靠度的Taylor方程确定种群适应值,得到一个系统最优可靠度分配方案;将最优解带入改进Minty算法或递推分解算法中,计算该最优解的连通可靠度精确值和对应的连通可靠度的Taylor展开方程;再次采用遗传算法求最优解．当最优解对应的可靠度精确值和Taylor方程算得得近似值误差小于指定精度时,则此最优解为最终的系统最优可靠度分配方案A·D2将此优化过程称为迭代遗传算法．算例显示迭代遗传算法不仅可用于大型网络的连通可靠度最优分配,而且优化迭代过程中可以得到多组阶段最优解,这些解均落在最优解附近,构成了近似最优解群,在实际工程优化中拓展了选择面．     

考虑资源约束和数量折扣的联合补货-选址库存协同优化研究

本文构建了一种基于联合补货策略的配送中心选址-库存协同优化新模型,该模型允许缺货,有资源约束且考虑数量折扣;同时设计了一种融合模拟退火思想的双种群独立进化的自适应差分算法(Adaptive Simulated Annealing Differential Algorithm,ASADE)对该模型进行求解,并通过算例与自适应差分算法、改进的蛙跳算法进行对比,证实了ASADE算法的有效性。最后进行了敏感性分析,讨论相关参数变动对总成本的影响,可为管理者更好决策提供有益的依据。     

考虑订单合并和货物转运的多式联运路径优化研究

为了获得运输的规模经济效应,本文研究了一种考虑订单合并和货物转运的零担多式联运路径优化问题。首先,以总运输成本为目标函数,以网络中的运输工具容量、可以提供的运输工具最大数量、运输工具服务的关闭时间以及订单时间窗为约束,构建混合整数规划模型,在模型中允许多个订单进行合并运输并考虑运输过程中的转运成本。其次,由于多式联运路径优化问题是典型的NP-hard问题,为了快速求解该模型,开发了一种可以快速为该问题提供近似最优解和下界的列生成启发式算法。最后,生成并测试了大量算例,结果表明所开发的列生成启发式算法可以在较短的时间内提供高质量的近似最优解。文章所构建的模型和开发的列生成启发式算法可以为零担自营多式联运物流企业提供高效的决策支持。     

考虑风电与光伏机组出力下农村微网系统运行优化模型研究

我国农村电网架构薄弱,限制了光伏扶贫等政策的开展,造成了可再生能源中弃风、弃光、弃水等现象的出现,如何将农村丰富的生物质能与风电、光伏构建成具有多能互补特性微网系统是解决上述问题主要途径.因此,本文建立了包含风力机组、光伏发电、蓄能系统和生物质能的农村微网系统,考虑了风电与光伏出力的不确定性,满足机组运行、系统备用、系统负荷平衡多能约束条件,构建了基于不确定性多情景下农村微网多能调度鲁棒优化模型.针对传统启发式算法早熟、容易陷入局部最优的问题,运用GAMS求解农村多能流微能网调度模型.算例结果表明,算法有效降低系统日运行成本,证明了模型的正确性.农村多能流微网系统的建设能够有效消纳富裕的风、光可再生能源及秸秆粪便等生物质能,对改善农村环境污染问题具有重要意义.     

考虑碳排放的需求可拆分车辆路径问题模型及算法研究

考虑低碳环境下的需求可拆分车辆路径问题,建立了以配送成本最小为决策目标的数学模型.随后根据模型特点,设计了基于动态学习因子的改进粒子群算法,并通过两个不同规模算例对模型验证模型和算法的有效性和合理性.通过两个算例中的算法对比发现,所提出的算法较改进前算法,均能够在保证求解质量的前提下,减少计算时间;而当算例规模增大时,这一优势更为明显.     

智能制造环境下的备件生产与运输协同调度问题研究

智能制造和即时配送环境下的备件生产与运输协同调度问题是目前国内研究的一大热点,这是因为备件供应链响应速度已成为当前备件制造企业赢得客户的关键因素。为了提高客户满意度,尽可能缩短从客户下达定制化生产订单到订单配送完成的时间,本文建立了以所有客户总等待时间最短为目标的混合整数规划模型和集合覆盖模型,推导了最优解性质,并设计改进的分支定价算法求得最优解。通过将小规模算例结果与CPLEX进行对比,验证了模型和算法的有效性。多组算例测试结果表明,所提出的模型和算法可以有效提升智能制造环境下的备件供应链运作效率。     

基于模糊规划算法求多层线性规划的折中最优解

本文利用模糊规划算法求解多层线性规划问题。在对问题的第l层进行求解时,确定前l层目标函数和前l-1层决策变量的隶属函数,通过隶属函数表明各层决策者对于目标函数和决策变量的满意水平。建立使得最差满意水平最大化的线性规划模型求得折中最优解。通过数值算例验证算法的可行性。     

基于多目标双层规划的智能电网虚假数据注入攻击研究

相对于传统电网,智能电网引入了先进的信息通信技术能显著提高企业生产效率和电力数据处理效率,但由于传统状态估计的漏洞,智能电网易受到虚假数据注入的攻击。现有的研究仅追求电力系统运营成本的最大化或攻击资源的最小化,没有考虑两者之间的均衡。基于此,本文考虑建立智能电网虚假数据注入攻击双层多目标规划模型,其中多目标表示电力系统运营成本与攻击成本之间的均衡,并结合IEEE 14总线算例进行分析,针对攻击者不同的攻击倾向给出了相应的虚假数据注入攻击策略。     

多工厂环境下考虑个体利益的多目标协同生产计划模型及算法

针对由多个工厂组成的分布式系统,考虑不同工厂的个体利益诉求,构建了多目标协同生产计划模型。在该模型中,以最大化整体收益作为第一个优化目标;同时,基于亚当斯的公平理论,利用偏离系数法,以最小化个体收益平衡偏差作为第二个优化目标。结合模型结构特点,基于快速非支配排序遗传算法,设计了相应的求解过程。最后,通过一个算例验证表明,本文设计的计划模型和求解方法,不仅可以从网络集成角度协调各个工厂的生产、库存和运输活动,而且能够实现整体利益和个体利益非一致性的最小化。     

Solving a new stochastic multi-mode p-hub covering location problem considering risk by a novel multi-objective algorithm

In this paper, we develop a novel stochastic multi-objective multi-mode transportation model for hub covering location problem under uncertainty. The transportation time between each pair of nodes is an uncertain parameter and also is influenced by a risk factor in the network. We extend the traditional comprehensive hub location problem by considering two new objective functions. So, our multi-objective model includes (i) minimization of total current investment costs and (ii) minimization of maximum transportation time between each origin–destination pair in the network. Besides, a novel multi-objective imperialist competitive algorithm (MOICA) is proposed to obtain the Pareto-optimal solutions of the problem. The performance of the proposed solution algorithm is compared with two well-known meta-heuristics, namely, non-dominated sorting genetic algorithm (NSGA-II) and Pareto archive evolution strategy (PAES). Computational results show that MOICA outperforms the other meta-heuristics.     

风险应对视角下不确定需求定位-路径鲁棒优化研究

及时准确地配送应急救援物资是突发公共事件救援过程中的关键环节,综合考虑应急救援过程中出现的需求随机性,路网的连通能力下降和设施点失灵等情况,采用车辆与直升机联合运输方式进行配送,以应急系统中各物资需求点的救援时间满意度之和最大、系统总成本最小及物资到达需求点的救援时间之和最小为目标,对多式联运三级网络应急物流系统定位—路径优化问题进行研究,建立了多目标定位—路径模型,并改进了遗传算法。最后,以汶川地震应急救援相关数据为例,对模型和算法的有效性进行了数据仿真验证,通过实验结果的对比分析,证明了鲁棒优化方法在处理不确定需求和设施失灵风险方面的有效性,进而为抵御突发公共事件发生后出现的风险,为解决突发公共事件发生后救援物资的安全及时准确配送提供了有效的方法。     

Fuzzy multi-objective vendor selection under lean procurement

This paper investigates a fuzzy multi-objective vendor selection program under lean procurement based on cost minimization, delivery schedule violation minimization, and maximizing the quality level of the purchased quantity. Specifically, the paper incorporates the vendor production capacity uncertainty into the model to identify an appropriate selection policy for vendors under practical operating conditions. The use of a soft time-window mechanism for the vendor selection model enables decision makers to further incorporate a time based performance metric for vendor evaluation, based on the degree of urgency or need for a part. A solution algorithm using fuzzy AHP is proposed. The results of a numerical example suggest that decision makers prefer vendors who can promise tighter delivery schedules rather than on cost or quality. A sensitivity analysis of the soft time-window on the achievement of the lean procurement objectives is also conducted.     

一个电力分配的数学模型与算法

用电分配涉及整个社会的各行各业和千家万户,评价分配方案优劣既要考虑经济效益又要考虑社会效益,因此属于多目标决策分析问题。本文运用目标规划原理建立了用电分配数学模型,其中包括利润、产值、电业局收入及各种电量限制等96个目标,按5个优先级进行优化。文中所用算法的基础是单纯形方法,为给出有实用价值的优化分配方案,本文提出以上一年同期实际用电指标为基础研究现行分配方案的基本思想,并采取若干相应措施。     

TOPSIS for bi-level MODM problems

TOPSIS (technique for order preference by similarity to ideal solution) is a multiple criteria method to identify solutions from a finite set of alternatives based upon simultaneous minimization of distance from an ideal point and maximization of distance from a nadir point. This paper proposes a fuzzy TOPSIS algorithm to solve bi-level multi-objective decision-making (BL-MODM) problems, and in which the objective function at each level are non-linear functions which are to be maximized. The proposed model for getting the satisfactory solution of the BL-MODM problems includes the membership functions for the upper level decision variables vector with possible tolerances, the membership function of the distance function from the positive ideal solution (PIS) and the membership function of the distance function from the negative ideal solution (NIS). A numerical illustrative example is given to clarify the proposed TOPSIS approach of this paper.     

A multi-objective mathematical optimization model for process targeting using 100% inspection policy

The selection of the optimal process target is an important problem in production planning and quality control. Such process targeting problems are usually modeled in the literature using a single objective optimization model. In this paper multi-objective optimization is introduced in the process targeting area. The quality characteristic under consideration is normally distributed with unknown mean and known standard deviation, and has two market specification limits. 100% inspection is used as the mean of product quality control. Product satisfies the first specification limit is sold in a primary market at a regular price and products fails the first specification limit and satisfies the second one is sold in a secondary market at a reduced price. The product is reworked if it does not satisfy both specification limits. The developed multi-objective optimization model consists of three objective functions, which are to maximize profit, income and product uniformity using Taguchi quadratic function as a surrogate for product uniformity. An algorithm is proposed to obtain and rank the set of Pareto optimal points. The utility of the model has been demonstrated using a numerical example from the literature with some additional data the new model requires. Sensitivity analysis was conducted and showed that the results of the model are sensitive to changes in process variance. In addition the optimal objectives of the profit function and product uniformity are more sensitive to changes in model parameters than the income function.     

基于直觉模糊规划的多目标投资组合选择模型

将直觉模糊集合的概念引入投资组合模型中,并将多目标投资组合模型中的收益、方差和偏度三个目标模糊化,用隶属函数与非隶属函数作为新的目标函数.针对该模糊多目标投资组合模型,提出了一个动态遗传算法,算例给出了该模型的一个实例的最优解.     

Multisourcing suppliers selection in service outsourcing

Multisourcing suppliers selection in service outsourcing involves selecting a supplier portfolio with a reasonable number of suppliers and better performance to cover aspiration levels of criteria. It is a specific weighted matching problem with new challenges. This paper proposes a decision method for solving this problem. In the proposed method, different formats of preference information, including numerical values, interval numbers and linguistic variables, are used to express alternative ratings. The technique for order preference by similarity to ideal solution is extended to aggregate the three formats of preference information. A bi-objective 0–1 linear programming model using the aggregated information is built to select a desired supplier portfolio, in which the objectives of minimization of suppliers number and maximization of supplier performance are involved. To solve this model, we transform it into an equivalent, and then an exact multi-objective branch-and-bound algorithm is developed to obtain Pareto-optimal solutions. In addition, a real case of an insurance company is used to illustrate the applicability of the proposed method.