基于前摄性调度的电动汽车路径问题研究

针对车辆调度过程中资源不均衡的问题,利用需求的不确定性,将配送周期划分为初始配送阶段和补货阶段,建立多阶段电动汽车的两级车辆路径优化模型.根据需求的动态程度对配送区域进行划分,结合前摄性调度和反应性调度策略,提出了一种混合禁忌搜索算法(HTSA)来求解该模型.在真实的案例和多个基准评估算例上的实验结果表明:模型和算法的性能优于传统的启发式算法,具有一定的实用价值.     

面向多检查的门诊患者调度研究

及时的检查对于患者病情诊断和治疗非常重要。然而,患者不同的紧急程度、检查项目的多样性、以及患者的行为因素如失约等,使门诊患者调度问题难以求解。为解决该问题,本文考虑患者对两种检查项目的不同需求,两类不同的紧急程度,以及患者失约和医生加班,建立了有限时域马尔可夫决策过程(MDP)模型,目标是使得患者检查所得的期望收益最大化以及期望加班时间惩罚成本最小化。由于MDP模型复杂,难以用解析方法来分析最优控制策略,因此本文基于MDP模型进行数值实验,观察最优解的结构特征,进一步构造了两种参数化启发式调度策略,并采用遗传算法对调度策略的参数进行优化。数值实验比较了最优控制策略、两种启发式调度策略以及先到先服务规则,实验结果表明本文所提的调度策略性能偏离最优解不超过10%;当工作负荷非常大时,启发式调度策略远远优于先到先服务规则。     

预制构件流水车间订单接受与调度的集成优化

针对预制构件生产管理过程中订单工期紧和生产能力不足的问题,在充分考虑中断和不可中断工序,串行和并行工序等复杂工况特点的基础上,以最大化净利润为目标,建立了一种订单接受与调度集成优化模型。鉴于问题的NP难性和模型的高度非线性,通过集成问题性质、构造启发式、邻域搜索和破坏-构造机制,提出了一种混合加速迭代贪婪搜索框架。其中,在调度构造阶段,为提高算法求解质量和搜索效率,设计了两种融合订单插入操作性质的加速构造策略。计算结果显示,与混合遗传禁忌搜索算法,遗传算法以及禁忌搜索算法相比,本文所提算法具有更好的求解质量和搜索效率。同时验证了所提出的加速构造策略能够有效减少算法运行时间。该研究有望显著提高预制生产企业净利润和客户满意度。     

项目鲁棒调度的资源分配启发式算法研究

合理的资源配置是提高项目调度鲁棒性一种有效的方法。本文针对项目鲁棒调度问题,提出了Max-PRUA资源分配启发式算法,以期通过生成鲁棒性高的资源分配方案来提高调度计划的鲁棒性。本算法设计了最大化利用优先关系和不可避免弧传递资源的资源分配两项策略来传递最大资源量,以减少由额外约束传递的资源量,降低对项目调度鲁棒性的影响。为寻优最优资源分配方案,配合局部搜索算法,本算法构建了动态活动组GRA,通过对组内活动顺序重排以生成多种资源分配方案,以利于从解空间中寻优出最佳的鲁棒性方案。最后通过大量的仿真实验验证和与其它算法进行比较,结果表明本算法对于不同规模和不同因素影响的项目均有较好的适应性,生成的资源分配方案对调度计划鲁棒性影响较小,是一种有效的算法。     

求解带组换装时间单机调度问题的禁忌搜索算法

以包头某钢铁线材企业生产实际调度问题为背景,研究了一类带组换装时间的单机调度问题.由于该问题是NP难的,本文提出了一类适合该问题的禁忌搜索算法.此外,本文将问题性质引入了禁忌搜索算法以进一步提高算法寻优性能,降低算法运行时间.本文提出的算法在随机问题和实际问题上均进行了测试,实验结果表明,本文提出的算法能在不到10秒的时间内获得实际问题的一个近似最优解.     

基于不同信息素更新策略的卫星数传调度蚁群优化算法

针对具有时间窗口和数传资源限制卫星数传调度问题,提出了基于解构造图模型的蚁群优化算法.借鉴精英机制,设计了绝对精英策略、相对精英策略、收益精英策略和对等精英策略等四种信息素更新策略.通过对不同规模场景的仿真试验,验证了基于不同信息素更新策略的蚁群算法是求解卫星数传调度问题的有效途径.基于信息素平衡思想的相对精英策略、收益精英策略和对等精英策略相对于绝对精英策略而言,能够避免算法过早陷入局部最优或出现退化行为,在规模较大的场景中能够收敛到比绝对精英策略更优的解.在小规模场景中,相对精英策略和收益精英策略所得解最好,而在大规模场景中对等精英策略所得解最好.     

一种改进的禁忌搜索算法及其在连续全局优化中的应用

禁忌搜索算法是一种元启发式的全局优化算法,是局部搜索算法的一种推广,已被成功地应用于许多组合优化问题中。本文针对有界闭区域上的连续函数全局优化问题,提出了一种改进的禁忌搜索算法,并进行了理论分析和数值实验。数值实验表明,对于连续函数全局优化问题的求解该算法是可行有效的,并且结构简单,迭代次数较少,是一种较好的全局启发式优化算法。     

考虑恶化和学习效应的多机制造系统智能优化方法

研究了考虑机器恶化和工人学习效应的平行机连续批调度问题,其中,工件具有不同的一般加工时间,机器具有不同的恶化率,工人具有不同的学习能力,批次的容量对于所有机器是相同的.目标是最小化最大完工时间.论文首先针对工件的组批排序问题推导了一系列重要性质,并提出了相应的启发式组批策略.然后,基于给定的工件分配和每个机器上工件的组批和排序,研究设计了工人和机器启发式匹配策略.由于所研究的问题在一般情形下被证明是NP-hard问题,论文设计了改进的变邻域搜索算法(IVNS)求解该问题并用算例验证了所提出算法的有效性.     

带有资源转移时间的RCPSP资源流模型及算法

本文在传统资源受限项目调度问题(resource-constrained project scheduling problem, RCPSP)中引入资源转移时间,为有效获得问题的最优解,采用资源流编码方式表示可行解,建立了带有资源转移时间的RCPSP资源流优化模型,目标为最小化项目工期。根据问题特征设计了改进的资源流重构邻域算子,分别设计了改进的禁忌搜索算法和贪心随机自适应禁忌搜索算法求解模型。数据实验结果表明,相较于现有文献中的方法,所提两种算法均可针对更多的项目实例求得最优解,并且得到最优解的时间更短,求解效率更高。此外,分析了算法在求解具有不同特征的项目实例时的性能,所得结果为项目经理结合项目特征评价算法适用性提供了指导。     

快速补货模式下的动态定价与库存联合策略

在供应有限的情况下,研究常规补货和快速补货下商品动态定价问题.首先,建立了动态规划模型,理论证明了最优库存策略是基于(s,S)策略下改进的基本库存策略.其次,提出了一种启发式策略求复杂系统的最优策略,启发式算法能够求出最优价格和最优库存水平.最后,数值算例研究表明,库存管理中采用快速补货提高了零售商的利润;初始库存水平越高零售商的利润越高.     

A model updating approach based on design points for unknown structural parameters

Finite element analysis has become an essential tool to estimate structural responses under static and dynamic loads. However, there are a lot of uncertainties in structural properties. For this reason, in many cases, the outcomes of the theoretical and experimental modal analyses do not match. Therefore, the analytical models of the structures need to be updated according to the experimental test results. The commonly used method to get parameters for model updating is experimental modal analysis which provides structural dynamic characteristic (natural frequencies, mode shapes and modal damping ratio). There are many methods available for the updating process. This study addresses an updating algorithm to modify the numerical models by using the design points for unknown structural properties. The proposed method aims to minimize the difference between the analytical and experimental natural frequencies by updating uncertain parameters for each mode and combine them to get an optimum solution. The algorithm is tested on a column and a 2D frame models. These models are investigated by taking the connection rigidity and elasticity modulus as unknown parameters. It is observed that the proposed algorithm gives better results for unknown structural properties compared to the initial values.     

云环境下面向复杂资源需求的虚拟机能效部署研究

针对云环境下在线虚拟机部署这一矢量装箱问题进行了研究,提出了多维空间划分模型和在线虚拟机能效部署算法OEEVMP。多维空间划分模型可以引导虚拟机部署,避免多维资源的不均衡利用;基于此模型,提出的OEEVMP算法在物理机运行数量局部最优和全局最优之间取得均衡,从而提高虚拟机部署能效。通过仿真实验,将OEEVMP算法与MFFD算法进行了对比,实验结果验证了所提算法的可行性和有效性。最后,对控制模型的两个参数进行了分析,给出了最佳的参数组合。     

A Staged Continuous Tabu Search Algorithm for the Global Optimization and its Applications to the Design of Fiber Bragg Gratings

A novel staged continuous Tabu search (SCTS) algorithm is proposed for solving global optimization problems of multi-minima functions with multi-variables. The proposed method comprises three stages that are based on the continuous Tabu search (CTS) algorithm with different neighbor-search strategies, with each devoting to one task. The method searches for the global optimum thoroughly and efficiently over the space of solutions compared to a single process of CTS. The effectiveness of the proposed SCTS algorithm is evaluated using a set of benchmark multimodal functions whose global and local minima are known. The numerical test results obtained indicate that the proposed method is more efficient than an improved genetic algorithm published previously. The method is also applied to the optimization of fiber grating design for optical communication systems. Compared with two other well-known algorithms, namely, genetic algorithm (GA) and simulated annealing (SA), the proposed method performs better in the optimization of the fiber grating design.     

Study on network traffic forecast model of SVR optimized by GAFSA

There are some problems, such as low precision, on existing network traffic forecast model. In accordance with these problems, this paper proposed the network traffic forecast model of support vector regression (SVR) algorithm optimized by global artificial fish swarm algorithm (GAFSA). GAFSA constitutes an improvement of artificial fish swarm algorithm, which is a swarm intelligence optimization algorithm with a significant effect of optimization. The optimum training parameters used for SVR could be calculated by optimizing chosen parameters, which would make the forecast more accurate. With the optimum training parameters searched by GAFSA algorithm, a model of network traffic forecast, which greatly solved problems of great errors in SVR improved by others intelligent algorithms, could be built with the forecast result approaching stability and the increased forecast precision. The simulation shows that, compared with other models (e.g. GA-SVR, CPSO-SVR), the forecast results of GAFSA-SVR network traffic forecast model is more stable with the precision improved to more than 89%, which plays an important role on instructing network control behavior and analyzing security situation.     

Hybrid flexible flowshop problems: Models and solution methods

This paper considers the problem of hybrid flowshop scheduling. First, we review the shortcoming of the available model in the literature. Then, four different mathematical models are developed in form of mixed integer linear programming models. A complete experiment is conducted to compare the models for performance based on the size and computational complexities. Besides the models, the paper proposes a novel hybrid particle swarm optimization algorithm equipped with an acceptance criterion and a local search heuristic. The features provide a fine balance of diversification and intensification capabilities for the algorithm. Using Taguchi method, the algorithm is fine tuned. Then, two numerical experiments are performed to evaluate the performance of the proposed algorithm with three particle swarm optimization algorithms available in the scheduling literature and one well-known iterated local search algorithm in the hybrid flowshop literature. All the results show the high performance of the proposed algorithm.     

A Lagrangean based branch-and-cut algorithm for global optimization of nonconvex mixed-integer nonlinear programs with decomposable structures

In this work we present a global optimization algorithm for solving a class of large-scale nonconvex optimization models that have a decomposable structure. Such models, which are very expensive to solve to global optimality, are frequently encountered in two-stage stochastic programming problems, engineering design, and also in planning and scheduling. A generic formulation and reformulation of the decomposable models is given. We propose a specialized deterministic branch-and-cut algorithm to solve these models to global optimality, wherein bounds on the global optimum are obtained by solving convex relaxations of these models with certain cuts added to them in order to tighten the relaxations. These cuts are based on the solutions of the sub-problems obtained by applying Lagrangean decomposition to the original nonconvex model. Numerical examples are presented to illustrate the effectiveness of the proposed method compared to available commercial global optimization solvers that are based on branch and bound methods.     

Transfer weight functions for injecting problem information in the multi-objective CMA-ES

The covariance matrix adaptation evolution strategy (CMA-ES) is one of the state-of-the-art evolutionary algorithms for optimization problems with continuous representation. It has been extensively applied to single-objective optimization problems, and different variants of CMA-ES have also been proposed for multi-objective optimization problems (MOPs). When applied to MOPs, the traditional steps of CMA-ES have to be modified to accommodate for multiple objectives. This fact is particularly evident when the number of objectives is higher than 3 and, with a high probability, all the solutions produced become non-dominated. An open question is to what extent information about the objective values of the non-dominated solutions can be injected in the CMA-ES model for a more effective search. In this paper, we investigate this general question using several metrics that describe the quality of the solutions already evaluated, different transfer weight functions, and a set of difficult benchmark instances including many-objective problems. We introduce a number of new strategies that modify how the probabilistic model is learned in CMA-ES. By conducting an exhaustive empirical analysis on two difficult benchmarks of many-objective functions we show that the proposed strategies to infuse information about the quality indicators into the learned models can achieve consistent improvements in the quality of the Pareto fronts obtained and enhance the convergence rate of the algorithm. Moreover, we conducted a comparison with a state-of-the-art algorithm from the literature, and achieved competitive results in problems with irregular Pareto fronts.     

一种模糊优化的全局算法及在三维水平井轨道设计中应用

本基于一种新的全局优化算法(EM)，提出一种求解模糊优化问题的全局优化算法。针对三维水平井轨道设计问题，提出两个模糊模型。最后把算法及模型应用到实际问题中，数值结果表明算法及模型是有效的、正确的。     

一种求解约束优化问题的改进差分进化算法

针对在处理约束优化问题时约束条件难以处理的问题,提出了一种求解约束优化问题的改进差分进化算法.即在每代进化前将群体分为可行个体和不可行个体两类,对不可行个体,用差量法将其逐个转化为可行个体,并保持种群规模不变,经过一序列的进化后,计算所有可行个体的适应度并找到问题的最优解.对5个经典函数进行了优化测试,测试结果表明提出的算法对求解约束优化问题是有效的.     

An algorithm for network dimensioning under reliability considerations

In this paper we introduce a new methodology to adjust link capacities in circuit switched networks taking into account the costing policy and reliability considerations. This methodology, which is an extension of previous work on reliability evaluation using routing models, is based on a cyclic decomposition algorithm which alternates between a routing subproblem and a link capacity adjustment subproblem. The proposed procedure, which is shown to converge to a global optimum for the dimensioning/routing problem, has been tested on a 14 undirected arc problem for various levels of link failure probability. The numerical results are extremely satisfactory and they demonstrate the usefulness of the proposed method for proper network dimensioning.