考虑作息时间的流水车间调度模型与算法

针对实际车间调度问题中由于作息时间而导致机器不可用的问题,建立了以最小化最大完工时间为优化目标的流水车间调度模型,并提出了一种改进的遗传算法.在调度模型中对机器可用时段进行约束来模拟考虑作息时间的开工与停工状态;在算法设计中通过结合禁忌搜索对基本遗传算法进行改进,提高了问题求解的质量.针对加工可恢复和不可恢复两种情况,...     

柔性调度多目标优化的混合粒子群算法研究

研究多目标柔性调度问题,提出了一种基于多目标粒子群优化算法和局域搜索技术相结合的新算法.建立以最大完成时间、机器总负载和最大机器负载为目标函数的多目标数学调度模型.将粒子群算法运用到机器分配子问题;局域搜索技术运用到工序排列子问题,对粒子群算法得到的结果进行再调度.粒子群优化算法的全局搜索能力与局域搜索技术相结合,加快了算法的收敛速度.最后通过与其他算法进行测试比较,验证了该算法的可行性及有效性.     

Threshold accepting and Ant-colony optimization algorithms for scheduling m-machine flow shops with lot streaming

This paper addresses the problem of making sequencing and scheduling decisions for n jobs m machines flow shops under lot sizing environment. Lot streaming (lot sizing) is the process of creating sublots to move the completed portion of a production sublots to down stream machines. There is a scope for efficient algorithms for scheduling problems in m-machine flow shop with lot streaming. In recent years, much attention is given to heuristics and search techniques. On this concern this paper proposes Ant-colony optimization algorithm (ACO) and threshold accepting algorithm (TA) to evolve best sequence for makespan/total flow time criterion for m-machine flow shop involved with lot streaming and setup time. The following two algorithms are used to evaluate the performance of the proposed ACO and TA: (i) Baker's algorithm (BA), an optimal solution procedure for two-machine flow shop problem with lot streaming and makespan objective criterion and (ii) genetic and hybrid genetic algorithm for m-machine flow shop problem with lot streaming and makespan and total flow time criteria.     

应用于复杂场合的多批次投递物料路径规划方法研究

为提高物料运输调度效率,减少作业时间,将2批次物料投递问题划分为3个阶段,将3个阶段的总作业时间最少作为目标函数,提出了一个混合整数规划模型。通过定义单双向车道约束和运输车辆路口会车约束,并引入多种群遗传算法,研究了多台物料运输小车的物料投递作业调度模型及算法。与单种群遗传算法和某公司历史数据相比,结果表明:该算法可在避免局部搜索的基础上大幅度提高系统的运算精度与速度,减少物料配送时间,可靠性高。该研究结果为提高企业的经济效益提供了参考,也为多批次物料投递作业优化及决策提供了新的思路。     

Optimisation of engineering system using a novel search algorithm: the Spacing Multi-Objective Genetic Algorithm

A large number of real-world issues are among difficult and multi-objective problems. Recently, it has been recognised that the evolutionary algorithms optimise well these types of problems. This paper proposes a novel multi-objective search algorithm that is called the Spacing Multi-Objective Genetic Algorithm (Spacing-MOGA). The innovation of the proposed Spacing-MOGA lies in a new survival selection algorithm called Spacing Distance. This research eliminates some of the disadvantages of other algorithms such as the Non-dominated Sorting Genetic Algorithm II (NSGAII). The proposed Spacing-MOGA is applied to five test benchmark functions and also to the design of I-Beam. Then, the results are compared with other algorithms such as NSGAII, Adaptive Weighted Particle Swarm Optimisation (AWPSO), and Non-dominated Sorting Particle Swarm Optimiser (NSPSO) based on the test metrics: Hypervolume, Spacing, Spread, and Generational Distance. Furthermore, for further demonstration of the ability of the proposed Spacing-MOGA, the experimental results are evaluated by the t-test.     

基于特征库和基因再造技术的带钢层流冷却系统多目标优化

针对热轧带钢层流冷却系统粗调区给定冷却路径和目标卷取温度的二维度多目标优化问题,提出了基于特征库和基因再造技术的多目标优化遗传算法,用来锁定粗调区集管的最佳开闭特征库。该算法通过历代Pareto前沿面的交集来建立特征库,从中挖掘出集管开闭的较优特征,将其嵌入至下一代种群,可有效抑制种群进化的漫游性和随机性;特征库采用动态竞争机制,使种群个体在全局寻优空间呈现更理想的并行搜索特性;特征库的随机舍取策略保证了历代Pareto前沿面在空间分布的均匀性,提高了系统对二维度多目标的均衡把控能力;最后,基于基因再造技术是驱动算法收敛于全局最优目标解群的强力引擎,是提高系统控制精度的有效措施。编写了基于MFC的仿真程序,仿真结果验证了该多目标优化策略的有效性和先进性。     

多路阀阀芯节流槽拓扑结构的优化设计

针对挖掘机多路换向阀开启过程中所受稳态液动力使得操纵力过大的问题，基于ANSYS对阀芯节流槽进行热流固多物理场可视化研究，通过分析半圆形节流槽阀芯下流动状态，提出新型节流槽拓扑结构，并建立Non-Parametric Regression响应面模型，研究新型节流槽结构尺寸对稳态液动力与质量流率的影响。结合多目标遗传算法寻优求解，并对比分析优化前后流动状态及阀芯所受稳态液动力等。结果表明：新型节流槽结构能够降低稳态液动力，有效提高了多路阀开启过程的换向性能。     

求解Job-Shop问题的改进搜索范围自适应遗传算法

为改善遗传算法求解Job-Shop问题时较差的局部搜索能力,并提高搜索最优解的速度,提出了一种改进的搜索范围自适应遗传算法。该算法采用一种新型的交叉操作,通过交叉和变异搜索过程提高遗传算法的局部搜索能力,同时采取MWKR优先规则优化初始种群。对作业车间调度问题进行仿真研究,结果表明该算法能找到问题的最优解,是可行和有效的。     

改进果蝇算法求解零空闲流水车间调度问题北大核心

针对传统果蝇算法在求解零空闲流水车间调度问题时,存在精度及稳定性较差的问题,提出了一种改进果蝇优化算法用以求解该类问题。该方法以最大完工时间最小化为优化目标,在传统果蝇算法的基础上,建立了多种群中心搜索模式,改进嗅觉搜索方式,引入破坏重建、插入领域局部搜索;并将免疫算法激励度引入果蝇视觉觅食阶段;最后通过测试案例,验证了该改进算法的有效性。结果显示该算法不仅能有效提高全局寻优效果,而且具有较好的稳定性,为求解零空闲流水车间问题提出了一种新方法。     

基于BP-GA的拼焊板拉深成形工艺优化

基于BP神经网络与遗传算法建立了多目标优化模型,对拼焊板拉深质量评价指标进行详细分析,提出了一种改进的拼焊板拉深成形多目标优化函数。将所建立的BP-GA模型用于某车型中柱拼焊板拉深成形,将有限元分析结果作为训练样本,得到模型最大厚度、最小厚度(拼焊板两侧),以及焊缝移动最大误差3.23%、6.16%、0.47%、2.64%、6.65%。利用遗传算法寻找最佳的工艺参数,实验证明,基于BP-GA的拼焊板拉深成形工艺优化模型,能够对生产实践提供有效指导。     

An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry

The focus of this research is on a hybrid method combining immune algorithm with a hill climbing local search algorithm for solving complex real-world optimization problems. The objective is to contribute to the development of more efficient optimization approaches with the help of immune algorithm and hill climbing algorithm. The hybrid algorithm combines the exploration speed of immune algorithm with the powerful ability to avoid being trapped in local minimum of hill climbing. This hybridization results in a solution that leads to better parameter values. This research is the first application of immune algorithm to the optimization of machining parameters in turning and also shape design optimization problems in the literature. The results of single-objective benchmark problem, multi-objective disc-brake problem, an automobile shape design optimization problem taken from the literature and case studies for multi-pass turning operation have demonstrated the superiority of the proposed hybrid over the other techniques in terms of solution quality and convergence rates.     

变双曲圆弧齿线齿轮切削仿真及参数优化

变双曲圆弧齿线圆柱齿轮（VH-CATT）是一种将圆弧齿线运用于齿轮齿线上的新型齿轮。由于缺少专用的加工机床,导致加工时其切削参数的调整较为复杂。为解决这个问题,根据其啮合原理及加工过程,利用ABAQUS对其切削加工过程进行模拟仿真;利用得到的数据,根据正交试验方法建立切削力的预测模型。利用鲸鱼算法,建立以加工效率与较小切削力为目标的函数优化模型,并通过加权求和法与归一化处理将它转化为单目标函数优化模型,通过鲸鱼算法得到优化后的切削参数。结果表明：所提出的单目标函数优化模型能够很好地对切削参数进行选定优化,以得到更好的加工效果;优化后的切削参数为主轴转速n=189.3 r/min,每齿进给量fz=0.046 mm,切削深度ap=1.89 mm。     

MOGA和FEM相结合实现万能型钢轧机机架圆角的多目标优化

文章把多目标遗传算法(MOGA)和有限单元法(FEM)有机地结合起来,建立了一种新的结构优化方法。用MATLAB遗传算法工具箱函数编制了MOGA和FEM相结合的多目标优化程序,对万能型钢轧机机架圆角进行多目标优化,充分发挥了有限单元法数值计算准确及遗传算法求极值的高效性和全局性的优点。与ANSYS自带的零阶和一阶优化方法相比,本优化方法取得了较好的优化效果,为解决复杂的多目标工程优化问题提供了一种新的更有效的途径。     

Optimization of hole-making operations: a tabu-search approach

This paper reports a tabu-search approach to minimize the total processing cost for hole-making operations. Four issues, namely, tool travel scheduling, tool switch scheduling, tool selection, and machining speed specification have been simultaneously addressed in this study. The total processing cost consists of tooling cost, machining cost, non-productive tool travelling cost, and tool switching cost. This problem has a structure similar to the Travelling Salesman Problem (TSP) and hence is NP-complete. In addition, the problem under consideration is more complex since the cost associated with each operation is both sequence-dependent and position-dependent. To provide an efficient solution procedure, a tabu search approach is used. To improve the search performance two new neighbourhood generation and move selection policies have been proposed and tested. The decisions on the above issues can be made simultaneously based on the output of the proposed algorithm. The results obtained from computational experiments show that the total processing cost can be significantly reduced within a reasonable search time. The effects of some search parameters and diversification strategies on the search performance have also been investigated.     

A multi-objective particle swarm optimisation for filter-based feature selection in classification problems

Feature selection has the two main objectives of minimising the classification error rate and the number of features. Based on binary particle swarm optimisation (BPSO), we develop two novel multi-objective feature selection frameworks for classification, which are multi-objective binary PSO using the idea of non-dominated sorting (NSBPSO) and multi-objective binary PSO using the ideas of crowding, mutation and dominance (CMDBPSO). Four multi-objective feature selection methods are then developed by applying mutual information and entropy as two different filter evaluation criteria in each of the proposed frameworks. The proposed algorithms are examined and compared with a single objective method on eight benchmark data sets. Experimental results show that the proposed multi-objective algorithms can evolve a set of solutions that use a smaller number of features and achieve better classification performance than using all features. In most cases, NSBPSO achieves better results than the single objective algorithm and CMDBPSO outperforms all other methods mentioned above. This work represents the first study on multi-objective BPSO for filter-based feature selection.     

差异演化算法在机械优化设计中的应用

针对在机械设计中常遇到一类非凸、多峰及非线性函数优化难的问题,给出了一种新的求解策略即差异演化算法。同时,为进一步提高差异演化算法的优化性能,对算法进行了一些改进。最后通过对两个机械优化设计实例进行仿真试验,结果表明本算法求解效率高、收敛速度快、全局搜索能力强。     

基于偏好信息的热连轧多目标负荷分配优化模型

热连轧负荷分配本质上是一个多目标优化问题,并且不同目标函数的重要性不一样。针对这个问题,引入偏好信息对多目标差分进化算法进行改进,仿真结果表明,该算法能够按照给定的偏好信息寻找到相应的Pareto解。并设计了基于该改进算法的热连轧多目标负荷分配优化模型,通过仿真验证了其合理性。     

基于Petri网和GA的多目标动态优化调度问题研究

以最小化生产周期、机床负载和机床与工人的费用为调度目标,基于受控Petri网和遗传算法提出了一种多目标作业车间调度方法,用于解决作业车间的加工受到机床、操作工人双资源制约条件下的动态优化调度.首先,给出了基于Parikh矢量的Petri网控制器简化设计方法,并应用该方法构建了基于紧急订单的受控Petri网模型;然后设计了基于Pareto的遗传算法,能够获得Pareto最优解集;尤其重要的是能够针对车间的动态扰动给出相应的调度方案.最后,通过实例验证了该方法的可行性和有效性.     

混合算法求解装配线平衡与多技能工分配研究

针对装配线平衡和多技能员工分配集成优化问题,构建以装配线平衡率和员工技能训练收益两个不同指标的数学模型,并提出混合粒子群优化算法对该模型进行求解.依据集成问题特征,采用整数编码,保证解的可行性;提出定向变异方式,通过计算变异点的所有变异结果的适应度值,选择适应度高的个体;设计邻域结构进行局部搜索,提高解的质量和搜索效率...     

基于遗传蚁群混合算法的孔群加工路径优化

为了提高孔群的数控加工效率,以孔群加工路径最短为目标函数,采用遗传蚁群混合算法对孔群加工路径规划问题进行研究。该混合优化算法的前期采用遗传算法、后期采用蚁群算法。在遗传算法向蚁群算法转换过程中,提出一种GSA遗传解到信息素转化策略。该策略以在遗传解endpop中选取前90%个个体和再随机产生的10%个个体合并后组成的新矩阵作为信息素值的转化依据;同时探讨了遗传算法中遗传算子的最佳组合问题。实例计算结果表明:与传统分批按编号加工的路径相比较,采用最佳组合算子和GSA转化策略后的遗传蚁群混合算法求解问题所获得的孔群加工路径缩短了70.9%,比单一遗传算法具有更高的求解精度,理论上可以明显地提高孔群的数控加工效率。