模糊层次分析法在企业设计战略优选中的运用研究

目的对企业设计战略进行优选评价。方法以苏州某企业的设计战略优选为例,首先对影响设计战略评价的要素进行分析,然后利用1～9的比例标度法,将影响该企业评价目标重要性的要素两两进行比较,量化评价的权重,最后通过模糊层次分析法对各项影响因素进行定量计算,以最大隶属度为原则确定最优方案。结论通过模糊层次分析法,可以对影响企业设计战略优选的各项因素进行定量计算,并对设计战略进行全面、客观的评价。模糊综合评价在设计战略优选中提供了量化的决策依据。     

基于模糊规划的电子废弃物逆向物流网络设计

为了解决回收量不确定情况下的回收网络设计问题,构建了一个基于模糊规划的逆向物流网络模型.采用三角模糊数来描述回收量,利用模糊统计方法计算模糊参数的隶属度,把模糊机会约束清晰化,从而将模糊规划模型转化为确定性规划模型,采用传统求解算法和LING○9.0软件求解.以广西逆向物流网络设计为例,选择电视机、电冰箱、洗衣机、空调...     

模糊设计的思想和应用

从生活中的模糊经验相出发,对数学的模糊理论以及其应用进行分析和引用,提出了模糊设计的思想,指出模糊设计就是在形态和使用方式上有一定宽容度的设计,以及有多重角色的设计.并强调模糊设计建立的是一种良性的人一物的关系,它既是设计的思考方法也是设计手段,更是一种健康的生活观念的体现.探讨了模糊设计的东方传统文化背景,并列举了各个领域的应用实例,说明模糊设计在目前的时代背景下,将成为推动人一物一自然和谐发展的设计趋势之一.     

工业产品造型设计评价的探讨

介绍了产品设计评价的方法,并详细描述了模糊评价法的数学模型和评价步骤.用MP4的造型设计评价作为评价实例来阐述模糊评价法,模糊评价法是工业产品造型设计评价的有效方法.     

轴类零件设计中的模糊集理论

近年来,模糊集合理论在机械设计领域也获得了广泛的应用。从机械强度的可靠性优化设计到各种零件参数的模糊设计系统研究,以及设计系统采用模糊推理策略、模糊聚类分析,模糊理论的优势已逐渐地显示出来随着计算机的普及与发展,优化设计已广泛应用于机械设计的各个领域,特别是对参数的设计,不仅可以提高设计效率和设计精度,而且可设计出用传统设计方法所无法得到的最优方案。     

基于AHP-FCE的康复训练仪设计

目的 为了提高中风和脑损伤患者在康复训练过程中的效率,对康复训练仪的设计做了一系列目标优化.方法 使用层次分析法(AHP)以及模糊综合评价(FCE)的方法,建立针对于康复训练仪的设计评价模型,围绕康复训练仪设计评价和方案决策两方面进行了研究.在设计评价方面,将康复训练仪的设计拆解成为不同的层次、不同的要素,采用定性和定量相结合的分析方法,计算并排序每个设计要素的权重.在方案决策方面,依据上阶段计算出的各要素权重值排序,运用模糊综合评价法对康复训练仪的设计方案进行评分,得到最完善、最优型的设计方案.结论 通过引入层次分析法和模糊综合评价在前期设计过程中的应用,能够有效地避免产品设计中的主观因素,为设计决策以及设计评价提供客观依据.     

基于模糊层次分析法的地铁站名图形符号设计

目的 为了进一步完善城市地铁站名标识系统,提升和彰显城市地铁站点的地域特色,提出了一种基于模糊层次分析法的地铁站名图形符号设计方法.方法 首先对国内外地铁站名图形符号的设计现状进行分析和总结,发掘其地铁站名图形符号的设计要素;然后运用层次分析法从装饰性、功能性和文化性3个方面构建地铁站名图形符号设计的评价指标体系,通过专家调查确定模糊判断矩阵并计算各评价要素权重值;最后以成都地铁站名图形符号设计为例,给出设计方案并结合用户调查进行模糊综合评价,评价结果表明该设计方案实现了装饰性、功能性和文化性的有效融合.结论 提出了一种基于模糊层次分析法的地铁站名图形符号设计与评估方法,并通过具体的案例设计验证了该方法的可行性,从而提高了地铁站名的视觉识别力,并彰显了城市地域文化特色.     

基于模糊逻辑的产品意象造型设计研究

为设计出符合消费者感性需求的产品,采用基于模糊逻辑的产品意象造型设计方法。首先确定感性词汇与造型设计要素,利用模糊逻辑建立二者之间的关系。通过模糊化、模糊规则的构建、模糊推理以及反模糊化等过程进行模糊逻辑控制器的设计,用Matlab建立仿真模型,最后通过测试验证了模型的有效性。结合折叠自行车造型设计进行研究,结果表明该方法是正确可行的。     

通用电机产品族协同设计的模糊优化

产品族设计是大规模定制中的核心内容,许多文献在假定产品特征确定情况下,采用平台参数和非平台参数,提出了大规模定制下的产品族设计优化方法。基于产品族设计理念,针对含有不确定特征的产品族设计中的一种基本类型——产品参数与产品功能之间的映射含有模糊系数的情形,建立一种对平台参数和非平台参数进行协同优化的模糊双层线性规划模型,利用有关模糊算法对模糊条件下的通用电机产品族协同设计进行优化。     

基于FAHP的亲子健身器材可用性评价方法

目的 研究影响亲子健身器材可用性的设计因素,提高亲子健身器材的可用性。方法 运用层次分析法和模糊评价法相结合的模糊层次分析法,针对亲子健身器材的可用性进行综合评价。首先针对亲子健身器材初始方案运用层次分析法进行分析,建立可用性评价模型。其次构造判断矩阵,确定各个指标因素的权重值。然后结合模糊综合评价法,计算亲子健身器材可用性的分值,通过该分值评判出最终等级。结论 通过运用层次分析法,为亲子健身器材的改进提供指导,进一步提高了产品的美观性与可靠性。     

Resource-constrained scheduling with hard due windows and rejection penalties

This work studies a scheduling problem where each job must be either accepted and scheduled to complete within its specified due window, or rejected altogether. Each job has a certain processing time and contributes a certain profit if accepted or penalty cost if rejected. There is a set of renewable resources, and no resource limit can be exceeded at any time. Each job requires a certain amount of each resource when processed, and the objective is to maximize total profit. A mixed-integer programming formulation and three approximation algorithms are presented: a priority rule heuristic, an algorithm based on the metaheuristic for randomized priority search and an evolutionary algorithm. Computational experiments comparing these four solution methods were performed on a set of generated benchmark problems covering a wide range of problem characteristics. The evolutionary algorithm outperformed the other methods in most cases, often significantly, and never significantly underperformed any method.     

An improved multi-objective evolutionary algorithm based on decomposition for energy-efficient permutation flow shop scheduling problem with sequence-dependent setup time

With the increasing attention on environment issues, green scheduling in manufacturing industry has been a hot research topic. As a typical scheduling problem, permutation flow shop scheduling has gained deep research, but the practical case that considers both setup and transportation times still has rare research. This paper addresses the energy-efficient permutation flow shop scheduling problem with sequence-dependent setup time to minimise both makespan as economic objective and energy consumption as green objective. The mathematical model of the problem is formulated. To solve such a bi-objective problem effectively, an improved multi-objective evolutionary algorithm based on decomposition is proposed. With decomposition strategy, the problem is decomposed into several sub-problems. In each generation, a dynamic strategy is designed to mate the solutions corresponding to the sub-problems. After analysing the properties of the problem, two heuristics to generate new solutions with smaller total setup times are proposed for designing local intensification to improve exploitation ability. Computational tests are carried out by using the instances both from a real-world manufacturing enterprise and generated randomly with larger sizes. The comparisons show that dynamic mating strategy and local intensification are effective in improving performances and the proposed algorithm is more effective than the existing algorithms.     

联合投资下创业机构间道德风险的博弈分析

联合投资是创业投资最流行的方式之一,针对创业投资机构在采取联合投资模式时可能出现的道德风险——"搭便车"现象,通过构造演化博弈模型,分析两类创业投资机构动态演化的过程,寻找出在不同条件下创业投资机构的演化稳定策略,结果表明创业投资机构的"搭便车"行为是由于即期博弈中收益与成本的对比关系引致的,管理成本以及在投资中所占的比例即股权份额直接影响了联合投资参与者是否会出现道德风险。最后通过控制参数和优化条件提出解决道德风险问题的建议,使联合投资朝着良性循环方向发展。     

基于博弈理论的供应链信任稳定演化策略

考虑供应链信任演化3个不同阶段,研究供应链信任稳定演化策略。针对传统博弈的局限性,从约束机制不健全和约束机制健全两个层面进行分析,将企业选择信任的额外收益、单方面信任成本、单方面不信任额外收益、单方面不信任代价和单方面信任额外补偿引入到演化博弈矩阵中,利用演化博弈理论分析各阶段的稳定状态。为验证模型的有效性和可行性,通过数值分析,研究模型的演化动力变化,得出相应的系统相图。研究结果表明,供应链信任策略值的概率大于0.75时,合作达到稳态,供应链整体收益最大;约束机制不健全下的稳定演化策略明显优于传统博弈下的稳定演化策略,但是约束机制不健全,企业的稳定演化策略未必都是信任;供应链企业间需要健全约束机制,当约束机制健全时,信任是企业最终策略选择。     

微粒群算法的置换Flow-Shop调度问题

介绍了一种新颖的全局优化搜索新方法--微粒群优化算法,讨论了其中的参数设置问题,通过引入交换子和交换序的概念,运用实数构造了一种特殊的微粒编码,并应用于Flowshop调度问题中,得到了较满意的结果.     

Makespan minimisation for a parallel machine scheduling problem with preemption and job incompatibility

In this paper, an extension of the graph colouring problem is introduced to model a parallel machine scheduling problem with job incompatibility. To get closer to real-world applications, where the number of machines is limited and jobs have different processing times, each vertex of the graph requires multiple colours and the number of vertices with the same colour is bounded. In addition, several objectives related to scheduling are considered: makespan, number of pre-emptions and summation over the jobs’ throughput times. Different solution methods are proposed, namely, two greedy heuristics, two tabu search methods and an adaptive memory algorithm. The latter uses multiple recombination operators, each one being designed for optimising a subset of objectives. The most appropriate operator is selected dynamically at each iteration, depending on its past performance. Experiments show that the proposed algorithm is effective and robust, while providing high-quality solutions on benchmark instances for the graph multi-colouring problem, a simplification of the considered problem.     

Handling constraints with an evolutionary tool for scheduling oil wells maintenance visits

Oil exploitation and transportation are very important activities for the development of industrial society. However, these activities are generating risks that are translated into accidental or chronic contaminations that directly affect the ecosystem. It is important that oil companies carry out correct maintenance of their oil fields. For example, the scheduling of visits to oil wells is a critical problem that has to be addressed in this scenario. To do that, an efficient tool called PAE was formerly proposed that is based on an evolutionary algorithm which obtains better schedules than those obtained by experts. In this article, an extension of PAE is presented to solve a constrained version of the maintenance problem. This new version of PAE is studied under the application of a set of constraint-handling techniques based on penalty functions and repair algorithms. The results and comparisons demonstrated that PAE, using a repair algorithm, required less computational effort (evaluations) with the same quality of solutions for the set of instances used. Details of implementation, results and benefits are presented.     

动态激励下科技资源平台与小微企业创新行为演化博弈分析

针对政府激励、科技资源平台创新、小微企业参与平台创新的博弈问题,构建小微企业、平台与政府三方演化博弈模型,考虑政府激励的动态性,分析影响各参与主体行为策略演化路径的因素。研究发现,增加政府监管制度供给、实施平台动态考核、适度控制奖励支付总额及设计合理的奖励衰减机制;督促平台提供创新性服务、打造良好平台生态;实施有利于降低小微企业参与平台创新的成本,以及增加其不参与时的潜在损失的措施等等,能增加三方收益,是促进科技资源平台与小微企业创新行为的有效方法。     

进化论自适应滤波算法及降噪特性研究

基于生物进化论策略的自适应滤波算法，利用其有性繁殖和无性繁殖的随机搜索能力实现全局最优搜索，可克服最小均方法（Least Mean Square，简称：LMS）对于多峰特性问题有可能收敛于某一局部最小值，无法实现全局最优的缺点。提出峰值系数指标PR（Peak Ratio）概念解决进化论自适应滤波器的性能评估问题，提出收敛速度CS（Converge Speed）指标和峰值系数指标PR高度的方法解决最优滤波参数的选取问题，并列进化论自适应滤波器的降噪进行模拟仿真，讨论了滤波长度和进化系数对进化论自适应算法消噪效果的影响及最优滤波参数。     

Evolutionary Algorithm Integrating Stress Heuristics for Truss Optimization

The optimal truss design using problem-oriented evolutionary algorithm is presented in the paper. The minimum weight structures subjected to stress and displacement constraints are searched. The discrete design variables are areas of members, selected from catalogues of available sections. The integration of the problem specific knowledge into the optimization procedure is proposed. The heuristic rules based on the concept of fully stressed design are introduced through special genetic operators, which use the information concerning the stress distribution of structural members. Moreover, approximated solutions obtained by deterministic, sequential discrete optimization methods are inserted into the initial population. The obtained hybrid evolutionary algorithm is specialized for truss design. Benchmark problems are calculated in numerical examples. The knowledge about the problem integrated into the evolutionary algorithm can enhance considerably the effectiveness of the approach and improve significantly the convergence rate and the quality of the results. The advantages and drawbacks of the proposed method are discussed.