用GA求解动态联盟中伙伴选择的多目标优化模型

描述动态联盟中的伙伴选择问题,针对以活动网络形式组织的新产品开发项目,建立 伙伴选择的多目标优化模型,实现项目失败风险最小化和项目完工时间最小化,并利用带自 适应移动线技术的遗传算法,求得问题的整个非劣解集合,仿真结果证明了算法的有效性.     

分布式信息检索的集合选择研究

集合选择是分布式信息检索中的重要问题，将集合选择问题转化为文档检索问题，尝试了多种文档检索方法来解决集合选择问题，并将各种方法的文档检索结果与集合选择结果进行了对比，通过与经典的集合选择算法CORI相比较，实验发现语言模型的集合选择方法能够取得令人满意的结果。     

针对解码转发协作分集的分布式伙伴节点选择算法

该文研究了在解码转发协作分集系统中的伙伴节点选择问题.文中首先建立了伙伴节点选择问题的数学模型,其能够在伙伴节点和目的节点分别满足一定误比特率性能的前提下,使源节点和伙伴节点的发射功率之和最小化.因此,该模型不仅能够开发多用户无线系统所固有的空间分集能力,显著地提高无线传输性能,而且能够增加无线网络的通信容量和降低移动节点的能耗.文中还推导了在瑞利衰减信道中使用M-QAM(M-ary Quadra-ture Amplitude Modulation)时伙伴节点和目的节点的平均误比特率公式,从而揭示了系统性能与伙伴节点之间的依赖关系.根据数学模型和误比特率公式,文中提出了一种伙伴节点选择算法,能够在分布式的无线网络环境中以极小的控制协议开销,逐个消息地选择最优的伙伴节点参与转发消息.不仅如此,该算法还能够随着信道状态的变化动态地调整所选择的伙伴节点,所以对信道状态具有自适应能力.最后,文中通过仿真验证了所提出的算法.     

虚拟企业伙伴选择的优化算法

虚拟企业伙伴选择及优化是组建虚拟企业的一个关键问题。根据虚拟企业任务之间的关系，该文将虚拟企业伙伴选择问题分解为串行问题、并行问题和混合问题3类，并建立了问题的多目标决策模型，通过候选企业筛选和确定优化组合方案，优化了模型求解过程，并验证了该算法的有效性。     

配送中心多区并行拣货系统投产顺序优化研究

随着电商行业的快速发展，配送中心拣选作业系统与工艺越来越复杂，当批次订单下达后，如何在减少排队的前提下尽量缩短完工时间，已经成为企业提高拣选效率、降低物流成本的重点问题。考虑到当前大型配送中心拣货系统多数采取多区并行拣选策略，且不同结构的订单工艺流程各不相同，以最小化完工时间及最小化集合单排队等待时间为双层优化目标，针对拣货系统从拣选到打包出库的全工艺流程，建立了基于多区并行拣选的拣货系统集合单投产顺序优化模型；围绕求解多目标问题，设计了基于快速非支配排序遗传算法的多目标求解方法，并引入数字仿真方法进行适应度值的计算；最后通过实证分析，证明了算法的有效性。结果表明，该集合单投产顺序优化方法对于提高配送中心拣货系统作业效率具备良好的实用价值。     

基于改进遗传算法的第三方物流联盟伙伴选择策略研究

在描述第三方物流供应链联盟伙伴选择模型的基础上,提出了一种求解联盟伙伴选择优化问题的自适应遗传算法.     

求解加热炉调度的改进修复式约束满足算法

针对钢铁生产中加热炉调度问题,考虑炉容受限的情况,以最小化板坯的Makespan和最小化总在炉加工时间为目标建立问题的多目标优化模型,将其归结为多旅行商问题。针对问题的NP-难特性,提出一种改进的修复式约束满足算法求解。松弛炉容约束得到初始调度,在检测冲突变量并构造冲突板坯的可替换加热炉集合的基础上,以开工时间偏移最小规则为冲突板坯重新指派加热炉,得到可行的调度方案。数据实验验证了模型和算法的可行性和有效性。     

基于模型的虚拟企业伙伴选择研究与应用

伙伴选择是虚拟企业建模过程中的一个重要环节。本文从虚拟企业建模的角度出发，提出了虚拟企业伙伴选择的三阶段模型．给出了评价指标体系．并用AHP算法给出了伙伴选择系统的实现方式。     

基于任务—资源分配图的虚拟企业伙伴优选算法

针对时间成本均衡的虚拟企业伙伴选择问题, 建立了统一描述虚拟企业过程和资源的项目配置图, 并以任务—资源分配图作为调度模型, 采用基于相对费效比的启发式算法迭代求解。算法考虑了虚拟伙伴的制造和转运时间及费用, 在保证截止期约束的同时能有效降低总成本。实际算例和大量模拟实验证明了该方法能有效降低获取优化方案的运算时间。     

基于实例推理的企业动态联盟伙伴选择与优化模型

将基于案例的推理方法运用于动态联盟伙伴企业选择与优化系统中，建立了伙伴企业选择系统的模型。具体讨论了方案库和评价结果库的建立，提出了基于灰色关联理论和模糊集理论相结合的相似度计算方法，从而可以准确地检索到相近案例，提高了伙伴企业选择的效率和准确性。     

Pareto process optimization of product development project using bi-objective hybrid genetic algorithm

Overlapping and iteration between development activities are the main reasons to cause complexity in product development (PD) process. Overlapping may not only reduce duration of a project but also create rework risk, while iteration increases the project duration and cost. In order to balance the duration and cost, this article presents four types of time models from the angle of time overlapping and activities dependent relationships based on Collaboration Degree Design Structure Matrix (CD-DSM) and builds the cost model considering the negation cost. On basis of the formulated model, a hybridization of the Pareto genetic algorithm (PGA) and variable neighborhood search (VNS) algorithm is proposed to solve the bi-objective process optimization problem of PD project for reducing the project duration and cost. The VNS strategy is implemented after the genetic operation of crossover and mutation to improve the exploitation ability of the algorithm. And then, an industrial example, a LED module PD project in an optoelectronic enterprise, is provided to illustrate the utility of the proposed approach. The optimization model minimizes the project duration and cost associated with overlapping and iteration and yields a Pareto optimal solution of project activity sequence for project managers to make decision following different business purposes. The simulation results of two different problems show that the proposed approach has a good convergence and robustness.     

A hybrid project scheduling and material ordering problem: Modeling and solution algorithms

A novel combination of a multimode project scheduling problem with material ordering, in which material procurements are exposed to the total quantity discount policy is investigated in this paper. The study aims at finding an optimal Pareto frontier for a triple objective model derived for the problem. While the first objective minimizes the makespan of the project, the second objective maximizes the robustness of the project schedule and finally the third objective minimizes the total costs pertaining to renewable and nonrenewable resources involved in a project. Four well-known multi-objective evolutionary algorithms including non-dominated sorting genetic algorithm II (NSGAII), strength Pareto evolutionary algorithm II (SPEAII), multi objective particle swarm optimization (MOPSO), and multi objective evolutionary algorithm based on decomposition (MOEAD) solve the developed triple-objective problem. The parameters of algorithms are tuned by the response surface methodology. The algorithms are carried out on a set of benchmarks and are compared based on five performance metrics evaluating their efficiencies in terms of closeness to the optimal frontier, diversity, and variance of results. Finally, a statistical assessment is conducted to analyze the results obtained by the algorithms. Results show that the NSGAII considerably outperforms others in 4 out of 5 metrics and the MOPSO performs better in terms of the remaining metric.     

Multi-mode resource constrained project scheduling and contractor selection: Mathematical formulation and metaheuristic algorithms

Contractor selection is a matter of particular attraction for project managers whose aim is to complete projects considering time, cost and quality issues. Traditionally, project scheduling and contractor selection decisions are made separately and sequentially. However, it is usually necessary to satisfy some principles and obligations that impose hard constraints to the problem under consideration. Ignoring this important issue and making project scheduling and contractor selection decisions consecutively may be suboptimal to a holistic view that makes all interrelated decisions in an integrated manner. In this paper, an integrated bi-objective optimization model is proposed to deal with Multi-mode Resource Constrained Project Scheduling Problem (MRCPSP) and Contractor Selection (CS) problem, simultaneously. The objective of the proposed model is to minimize the total costs of the project, and minimize the makespan of the project, simultaneously. To solve the integrated MRCPSP-CS, two multi-objective meta-heuristic algorithms, Non-Dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization algorithm (MOPSO), are adopted, and 30 test problems of different sizes are solved. The parameter tuning is performed using the Taguchi method. Then, diversification metric (DM), mean ideal distance (MID), quality metric (QM) and number of Pareto solutions (NPS) are used to quantify the performance of meta-heuristic algorithms. Analytic Hierarchy Process (AHP), as a prominent multi-attribute decision-making method, is used to determine the relative importance of performance metrics. Computational results show the superior performance of MOPSO compared to NSGA-II for small-, medium- and large-sized test problems. Moreover, a sensitivity analysis shows that by increasing the number of available contractors, not only the makespan of the project is shortened, but also, the value of NPS in the Pareto front increases, which means that the decision maker(s) can make a wider variety of decisions in a more flexible manner.     

评估项目风险中协调偏好的DEA方法

针对于综合考虑项目风险评估问题中影响风险因素的正向指标和逆向指标,在分析DEA有效解和多目标规划Pareto解等价性的基础上,建立了基于DEA的协调偏好的风险评估模型,优化协调了具有相反评价指标的项目风险评估问题。根据模型构造的生产可能集,结合两种相反评价指标对风险评估的影响,给出了风险包络面的概念,通过选取两组移动因子和具有熵权的模糊综合评价方法,逐层缩小风险包络面对决策单元进行评估排序,改进了移动风险曲面排序风险的方法,实现了风险决策单元的完全排序,使得评估结果在一定程度上兼具主观性与客观性。最后通过一个对软件项目进行风险评估的实际算例,表明了该方法的合理性和有效性。     

An epsilon-constraint method for fully fuzzy multiobjective linear programming

Linear ranking functions are often used to transform fuzzy multiobjective linear programming (MOLP) problems into crisp ones. The crisp MOLP problems are then solved by using classical methods (eg, weighted sum, epsilon-constraint, etc), or fuzzy ones based on Bellman and Zadeh's decision-making model. In this paper, we show that this transformation does not guarantee Pareto optimal fuzzy solutions for the original fuzzy problems. By using lexicographic ranking criteria, we propose a fuzzy epsilon-constraint method that yields Pareto optimal fuzzy solutions of fuzzy variable and fully fuzzy MOLP problems, in which all parameters and decision variables take on LR fuzzy numbers. The proposed method is illustrated by means of three numerical examples, including a fully fuzzy multiobjective project crashing problem.     

多模式多资源均衡及基于动态种群的多目标微粒群算法

研究了多模式多资源均衡问题,该问题需要动态选取每项任务的执行模式,并综合考虑项目截止日期和资源限额等约束.将种群竞争模型嵌入到基于 Pareto 的向量评价微粒群算法(VEPSO-BP)中,提出了一种新的基于动态种群的多目标微粒群算法(MOPSO-DP).通过实例测试了 MOPSO-DP 的性能,并与 VEPSO-BP 进行了对比.实验结果表明, MOPSO-DP 能取得更为丰富且优化效果更好的 Pareto 非支配解.     

Endogenous Movement and Equilibrium Selection in Spatial Coordination Games

We study the effects of agent movement on equilibrium selection in network based spatial coordination games with Pareto dominant and risk dominant Nash equilibria. Our primary interest is in understanding how endogenous partner selection on networks influences equilibrium selection in games with multiple equilibria. We use agent based models and best response behaviors of agents to study our questions of interest. In general, we find that allowing agents to move and choose new game play partners greatly increases the probability of attaining the Pareto dominant Nash equilibrium in coordination games. We also find that agent diversity increases the ability of agents to attain larger payoffs on average.     

混合多目标粒子群优化算法在热精轧负荷分配优化中的应用

通过对热精轧负荷分配过程的分析,选取负荷均衡、板形良好和轧制功率最低为目标,建立了热精轧负荷分配多目标优化模型.为了提高多目标优化算法解集的分布性和收敛性,提出了一种混合多目标粒子群优化算法(HMOPSO),该算法根据Pareto支配关系得到Pareto前沿进而保证种群收敛;采用分解策略维护外部存档,该策略首先根据Pareto前沿求出上界点对目标空间进行归一化处理,然后对种群进行分区处理进而保证种群的分布性能.仿真结果表明,HMOPSO的收敛性和分布性都好于MOPSO和d MOPSO;采用模糊多属性决策的方法从Pareto最优解集中选择一个Pareto最优解,通过与经验负荷分配方法相比,表明该Pareto最优解可以使轧制方案更加合理.     

软件开发团队的多目标优化构建研究

分析了客户需求与候选成员能力的关系,使用模糊排序聚类算法得到专业领域分工的集群;同时依据迈尔斯—布里格斯性格类型指标得到候选成员协作关系的量化评估。建立了以成员综合能力和性格匹配度最大化为目标的团队构建模型。最后,结合一个具体案例,采用带有判断与修复算子的微粒群算法对模型进行求解,得到表示团队构建候选方案集合的帕累托解,从而验证了该优化模型及算法的有效性和实用性。