共查询到17条相似文献,搜索用时 140 毫秒
1.
2.
3.
4.
为解决混流产品在无等待多条流水线生产条件下,由于产品生产节拍不一致导致总装分装系统中生产连续性较差的问题,研究总装分装任务排序优化方法,实现在保证批量生产、部件齐套供应前提下,使订单能够按期交货.以最小化总加工时间、最小化总提前/拖期和产品转换惩罚为优化目标,建立了优化数学模型,并设计了改进多种群蚁群算法求解该优化模型.以某机床厂某月生产任务为例进行仿真实验,与多种群蚁群算法、传统蚁群算法对比,验证了该算法性能较好.并与现行的调度方法进行对比,验证了该任务排序方法在混流节拍不一致的多条装配线生产上,能够有效地缩短产品生产周期、降低生产成本,提高订单的准时交付率. 相似文献
5.
应用蚁群算法来解决MAS的任务分配问题这一类典型的组合优化问题.研究表明,在求解复杂优化问题方面该算法具有一定的优越性.首先建立了任务分配的数学模型,并导出分配优化的目标函数;其次利用蚁群算法分布式求解的特点实现任务分配的组合优化.仿真结果表明,该算法比禁忌搜索和随机方法具有更好的求解能力. 相似文献
6.
鉴于格构式输电塔结构具有杆件众多、型式复杂等显著特点,所以建设和发展既安全可靠,又经济合理的此类结构一直是工程界的研究热点和难点。因此,该文提出了一套完整的基于蚁群优化算法的输电塔结构离散变量优化设计方法。该方法是在结构的截面、拓扑和形状变量统一转化为离散变量的基础上,将4类不同层次的优化问题统一为不同规模的标准化旅行商问题,并最终采用蚁群算法实现输电塔结构的优化设计。通过对某一实际输电塔结构的优化设计表明:该文方法不仅可以简单高效的求解输电塔结构的截面、拓扑、形状和布局优化问题,而且清晰明确的阐述了不同优化内容的物理意义和优化准则,实现了优化方法和思路的统一。此外,基于蚁群算法的输电塔结构离散变量优化方法通用性强、易于程序化,而且具有非常好的工程应用前景。 相似文献
7.
8.
对于货物配送过程中零担订单与配送车辆的匹配问题,由于传统的人工匹配模式会导致较高的运输成本和空载率。针对此问题,根据实际业务情况,将订单体积与重量作为约束条件,以平台利润最大化为优化目标建立0-1整数规划模型。根据平台实际业务设计出GAPVR (Genetic Algorithm based on Price-Volume Ratio)算法,并将该算法与CPLEX和平台目前的业务操作采用的FCFS(First Come First Service)算法进行对比。通过在不同订单量下进行数值模拟分析,结果表明在大规模订单量下,设计的GAPVR算法可以有效节约17.24%的运输成本,并在此前提下,可以进一步将空载率降至平均4.73%,比平台当前FCFS算法的空载率降低50%,证明了模型的有效性,对平台的实际运营具有一定的指导意义。 相似文献
9.
扩展蚁群算法是蚁群算法创始人Dorigo提出的一种用于求解连续空间优化问题的最新蚁群算法,但该算法的收敛速度参数和局部搜索参数取值缺乏理论指导,因此其性能受算法参数影响较大.本文提出一种求解连续空间优化的扩展粒子蚁群算法,将粒子群算法嵌入到扩展蚁群算法中用于在线优化扩展蚁群算法参数,减少了参数人为调整的盲目性.从而改善扩展蚁群算法的寻径行为.通过将本文提出的算法与遗传算法、克隆选择算法、蚁群算法、扩展蚁群算法对5种典型测试函数优化的结果对比表明,本文算法在搜索速度和全局搜索能力方面均优于其它算法. 相似文献
10.
11.
资源均衡问题已被证明属于组合优化中的NP-hard问题,随着网络计划的复杂化,传统的数学规划法和启发式算法已很难解决该问题。本文以各种资源标准差的加权之和作为衡量资源均衡的评价指标,建立了资源均衡优化决策的数学模型,其次,自行设计蚁群算法步骤,利用Matlab编程进行实现,将蚂蚁随机分布在可行域中,蚂蚁根据转移概率进行全局搜索或局部搜索,经迭代求解资源平衡的全局最优和对应的各工序的开始工作时间,最后使用单资源均衡和多资源均衡两个算例对算法进行了测试,验证了该算法的有效性。 相似文献
12.
This paper addresses a multi-stage job-shop parallel-machine-scheduling problem with an ant colony optimization system developed. The problem is practically important and yet more complex, especially when customer order splitting in multiple lots for the reduction of operation times in each workstation is allowed. It also includes the decisions of the numbers of parallel machines in workstations dynamically scheduled. In addition, this paper also addresses the multiple-objectives scheduling. For the practical concern, in addition to the production (or quantitative) objectives, the marketing (strategic or qualitative) criteria are also considered. A soft constraint thus may be realized from a thus-called qualitatively evaluated order sequence. The soft constraint with the ant colony optimization solution constructs a penalty function for the multiple qualitative objectives and the results of scheduling obtained by ant colony optimization. For this problem, the ant colony optimization components (including the network representation, tabu lists, transition probabilities, and pheromone trail updating) are also developed and adapted for the multiple objectives. The experiment results of parameter design and different problem sizes are provided. The results of a genetic algorithm also developed for the present problem under the developed system concept are also provided, since in the literature the genetic algorithm has also not been explored for the present problem with multiple objectives and order splitting. The results of both solution techniques show the potential usefulness of the system and are comparable, but the ant colony optimization provides a more computationally efficient better result. 相似文献
13.
Ant colony optimization (ACO) is a metaheuristic that takes inspiration from the foraging behaviour of a real ant colony to solve the optimization problem. This paper presents a multiple colony ant algorithm to solve the Job-shop Scheduling Problem with the objective that minimizes the makespan. In a multiple colony ant algorithm, ants cooperate to find good solutions by exchanging information among colonies which are stored in a master pheromone matrix that serves the role of global memory. The exploration of the search space in each colony is guided by different heuristic information. Several specific features are introduced in the algorithm in order to improve the efficiency of the search. Among others is the local search method by which the ant can fine-tune their neighbourhood solutions. The proposed algorithm is tested over set of benchmark problems and the computational results demonstrate that the multiple colony ant algorithm performs well on the benchmark problems. 相似文献
14.
15.
现阶段,研发型企业的项目处于多项目环境下,为了解决多项目并行时人力资源争夺问题,本文针对该类企业多项目管理中人力资源调度进行优化研究,以考虑项目延期惩罚成本的最小总成本为目标函数,将现实问题抽象建模。基于国内外的研究提出了一种超启发式算法进行求解,该算法将人力资源调度问题分为项目活动分配和人员选择项目活动两个部分,采用蚁群优化作为高层启发式策略搜索低层启发式规则,再进一步根据规则解构造出可行解。最后本研究设计多组仿真实验与启发式规则进行对比,结果表明该算法有较好的搜索性能,为人力资源的调度问题提供了新的解决方案。 相似文献
16.
The flutter/divergence speed of a simple rectangular composite wing is maximized through the use of different ply orientations. Four different biologically inspired optimization algorithms (binary genetic algorithm, continuous genetic algorithm, particle swarm optimization, and ant colony optimization) and a simple meta-modeling approach are employed statistically on the same problem set. In terms of the best flutter speed, it was found that similar results were obtained using all of the methods, although the continuous methods gave better answers than the discrete methods. When the results were considered in terms of the statistical variation between different solutions, ant colony optimization gave estimates with much less scatter. 相似文献