应用混合蚁群算法求解模糊作业车间调度问题

为解决蚁群算法求解时间过长和易陷入局部最优的问题,提出了一种求解模糊作业车间调度问题的混合算法,该算法将蚁群算法用于全局搜索.为了提高搜索效率,根据作业车间调度问题解的特征,提出一种基于关键工序的邻域搜索方法,并使用此邻域搜索方法的禁忌搜索算法嵌入蚁群算法.利用禁忌搜索算法较强的局部搜索能力,提高了蚁群算法的优化能力,改善了作业车间调度问题解的质量.实验结果验证了该混合搜索算法的有效性,其优化效果优于并行遗传算法和禁忌搜索算法.     

求解拆卸线平衡问题的一种变邻域搜索算法

针对拆卸线平衡问题特点,建立以最小化工作站数、平滑指数、危害指数和需求指数为优化目标的多目标数学模型,进而提出一种改进的变邻域搜索算法求解该问题。所提算法采用了一种启发式方法产生初始解,并构造了3种邻域结构,扩大算法搜索范围。采用初始解集进行局部搜索,搜索过程使用一步改进策略,并引入禁忌表方法,提高算法全局寻优能力。通过对大量不同规模测试问题进行算法实验,并与现有方法对比,结果表明,所提变邻域搜索算法在求解效率与求解质量上具有优越性。     

基于混合禁忌搜索算法的供应链排序问题

分析非标准件加工企业供应链的特点,提出协同优化订单分配、生产调度和批量运输调度的多工厂多客户供应链排序问题。以工件的最长订货提前期与总成本加权之和最小化为目标,构建问题的数学模型。在分析解的最优性条件基础上,设计一种基于矢量组编码方法的混合禁忌搜索算法。算法对可行域进行分区,通过基于插入、交换两种邻域操作的禁忌搜索算法选择子区域,采用基于块结构邻域操作的禁忌搜索算法搜索子区域中的优良解。采用所提混合禁忌搜索算法对算例进行优化求解,并对采用不同编码方法、不同启发式算法的算例结果进行比较,结果表明所提出算法的有效性。     

适应性禁忌搜索算法求解带回程的时变速度车辆路径问题

为求解带回程的时变速度车辆路径问题，建立了问题的数学模型并提出适应性禁忌搜索算法求解。适应性禁忌搜索算法为两阶段的启发式方法，改进固定速度下的启发式方法用于生成时变速度下的初始解，然后运用适应性禁忌搜索算法进一步优化，包括邻域生成规则定义，采用Hash表存储搜索过程中的解，检测解的重复状态，定义逃离局部搜索区域规则。对改进的标准问题进行测试，同时与最近邻域搜索算法的结果作比较，结果表明算法是有效的。与固定速度情形相比较，时变速度模型得到的调度方案更加合理。     

解决无等待流水线调度问题的变邻域搜索算法

提出了解决无等待流水线调度问题的变邻域搜索调度算法。采用基于自然数编码的工件序列表达问题的解，采用多重Insert移动邻域和多重Swap移动邻域作为变邻域搜索的两种邻域结构。多重移动有利于算法向包含较优解的区域搜索，因而有较高的求解质量和效率。仿真实验证明了变邻域搜索算法的有效性。     

飞机复合材料部件装配调度算法研究

以优化飞机复合材料部件装配顺序、缩短部件装配时间为目标,构建了复合材料部件装配调度模型,提出了一种启发式算法与禁忌搜索算法相结合的调度算法（HTA-AJSP）,并对其进行了优化,通过采用变邻域搜索和禁忌规则,很好地避免了算法搜索陷入局部最优。通过分析与实例验证,并与启发式算法和局部搜索算法进行比较,所提出的算法在产品装配调度优化问题上取得了比较满意的效果,缩短了装配周期,提高了装配效率。     

飞机复合材料部件装配调度算法研究

以优化飞机复合材料部件装配顺序、缩短部件装配时间为目标,构建了复合材料部件装配调度模型,提出了一种启发式算法与禁忌搜索算法相结合的调度算法(HTA-AJSP),并对其进行了优化,通过采用变邻域搜索和禁忌规则,很好地避免了算法搜索陷入局部最优。通过分析与实例验证,并与启发式算法和局部搜索算法进行比较,所提出的算法在产品装配调度优化问题上取得了比较满意的效果,缩短了装配周期,提高了装配效率。     

基于改进Jaya算法的柔性作业车间调度问题

鉴于柔性作业车间调度问题(FJSP)是广泛存在于制造企业实际生产过程中的复杂NP-hard组合优化问题,针对FJSP的特点,结合Jaya算法与禁忌搜索算法的各自优势,提出一种改进Jaya算法求解该问题.在该算法中,根据离散的Jaya算法公式提出一种扩展离散Jaya算法操作机制,设计了Jaya迭代候选解集方法以及结合相似度和最大完工时间的选择策略,保证了种群的多样性并提高了Jaya算法的搜索能力;提出融合M.G.和N7两种邻域结构的禁忌搜索算法,使混合算法在分散搜索和集成搜索之间达到平衡.通过测试著名的FJSP基准问题,显示了所提算法在质量方面优于当前文献,并通过实验验证了算法的有效性和优越性.     

基于改进禁忌搜索的多目标自动化仓库调度

针对产品质量和路径的多目标自动化立体仓库调度优化问题,为平衡解的收敛性和多样性,提出一种改进的多目标禁忌搜索算法.该算法的改进在于,一方面为Pareto解空间构造可行的初始解,改造了禁忌搜索的邻域结构;另一方面采用惩罚策略,使搜索能够跳出局部最优.面向出入自动化立体仓库的产品在时间上有特定要求的工业生产过程,建立了兼顾质量和路径的多目标优化模型,并运用改进的多目标禁忌搜索算法对其实现了调度优化求解.实例仿真表明,所提算法对仓库调度优化问题在解的质量及求解效率上都取得了较好的效果.     

多目标柔性车间调度的Pareto混合禁忌搜索算法

针对最小化最大完成时间、总机床负荷及最大机床负荷的多目标柔性作业车间调度问题,提出了一种带有Pareto档案集的混合禁忌搜索算法.该算法为每次迭代产生的邻域解集进行Pareto非支配排序,选择第一前沿的解用于Pareto档案集更新,并给出了一种Pareto档案集快速更新算法.为减小邻域搜索空间,结合问题特征,设计了基于公共关键块结构的插入邻域和交换邻域.通过3个经典算例的实验仿真,以及与其他算法的比较,验证了该算法的可行性和有效性.     

考虑多目标的工艺规划与调度集成问题研究

针对工艺规划与调度集成问题在多目标优化方面的不足,考虑将多目标优化集成到工艺规划与调度集成问题中。以最长完工时间、加工成本及设备最大负载为优化目标,对该多目标工艺规划与调度集成问题进行建模,并提出了一种非支配排序遗传算法,鉴于加工信息的多样性,使用多层结构表示可行解,对该算法的选择及遗传操作等步骤进行了设计。最后,以实例验证了上述模型的正确性及算法的有效性。     

基于组合优化算法的6R机器人逆运动学求解

针对逆运动学求解存在的多解、精度低及通用性差等问题,提出了一种适用于各类6R工业机器人求逆解的组合优化算法。根据经典D-H法建立了机器人运动学模型,以最小化位姿误差为目标,结合运动平稳性原则构造了逆解问题的目标函数,以线性加权和法设计了适应度函数。通过混沌映射初始化种群、收敛因子非线性更新、自适应惯性权重位置调整及引入模拟退火策略等4种措施得到了一种改进的鲸鱼优化算法,并用于逆运动学求解。组合算法将鲸鱼算法求解的结果作为初始值,再利用Newton-Raphson数值法迭代出满足精度要求的运动学逆解。仿真试验结果表明：改进后的鲸鱼算法求解性能得到了较大提高,相比于直接利用鲸鱼算法进行逆运动学求解,组合优化算法具有求解速度快、稳定性好、精度高的特点,证明了该算法求逆的可行性与有效性。     

基于群智能算法的设备布局离散优化研究

针对单向环形设备布局设计问题,建立了新的数学模型.利用多维实数编码及映射方法,将连续粒子群优化算法应用于求解设备单向环形布局问题,提供了求解离散优化问题的新思路.利用遗传算法中的杂交策略扩展了粒子群优化算法,提高了粒子群优化算法性能.建立了问题的图结构描述,以引入蚁群系统算法搜索优化解.给出了两种方法的求解步骤.通过实例计算和结果比较,说明该算法能有效地解决此类离散优化问题,降低成本,提高效率,所得解质量较高,有很好的实用价值.     

随机工时下的多目标柔性作业车间鲁棒调度问题

针对工时不确定条件下的多目标柔性作业车间调度问题,采用2个不确定参数描述随机工时的波动程度和约束条件允许违背程度,将不确定条件下的柔性作业车间调度问题模型转换成确定条件下的鲁棒对等问题模型。在算法设计中采用全局非支配解集保存每代进化过程中产生的非支配解,并选择全局非支配解集中的个体参与变异操作。在交叉和变异操作之后,设计了一种基于变邻域结构的局部搜索策略。最后,运用该算法求解经典基准算例,验证了其有效性。     

求解物流Web服务组合问题的两阶段多目标蚁群算法

针对基于QoS的物流Web服务组合优化问题,提出了两阶段多目标蚁群优化(TMACO)算法。首先,针对原始数据集中存在被支配候选服务而增加算法求解时间的问题,提出了基于Pareto支配的预优化策略;其次,针对属性权重难以确定的问题,提出了不依赖权重的信息素更新策略和启发信息策略;最后,针对基础蚁群算法容易陷入局部最优的问题,提出了懒蚂蚁策略。实验结果表明,TMACO算法具有良好性能,相对于基础蚁群算法、利用解与理想解距离来更新信息素的改进蚁群算法、遗传算法以及用支配程度作为解的个体评价的改进遗传算法,TMACO算法有更高的寻优能力,能够找到更多更优的非劣解。     

面向大规模定制的装配线优化调度研究

针对大规模定制生产模式下汽车装配线调度存在的问题,提出一种多目标优化调度的方法,设计了相应的目标函数。提出一种多目标遗传算法,设计了相应的编码、选择和交换方案,在算法实现中对精英策略和选择机制进行了改进。仿真实验说明该算法可行有效,优于VEGA、PGA和NPGA等其他遗传算法。     

An improved particle swarm optimization for the resource-constrained project scheduling problem

In this paper, an improved particle swarm optimization (PSO) algorithm is proposed for the resource-constrained project scheduling problem (RCPSP) which is widely applied in advanced manufacturing, production planning, and project management. The algorithm treats the solutions of RCPSP as particle swarms and employs a double justification skill and a move operator for the particles, in association with rank-priority-based representation, greedy random search, and serial scheduling scheme, to execute the intelligent updating process of the swarms to search for better solutions. The integration combines and overhauls the characteristics of both PSO and RCPSP, resulting in enhanced performance. The computational experiments are subsequently conducted to set the adequate parameters and compare the proposed algorithm with other approaches. The results suggest that the proposed PSO algorithm augments the performance by 9.26, 16.17, and 10.45 % for the J30, J60, and J120 instances against the best lower bound-based PSO currently available, respectively. Moreover, the proposed algorithms demonstrate obvious advantage over other proposals in exploring solutions for large-scale RCPSP problems such as the J60 and J120 instances.     

Nonlinear optimization strategy based on multivariate prediction capability ratios: Analytical schemes and model validation for duplex stainless steel end milling

Given the complex nature of their phenomena and interactions, industrial processes often have multiple variables of interest, usually grouped into critical-to-quality and critical-to-performance characteristics. These variables often have significant correlations, which make engineering problems multivariate. For this reason, Response Surface Methodology, coupled with multivariate techniques, has been widely used as a logical roadmap for modeling and optimization of the characteristics of interest. However, the variability and prediction capability of the numerical solutions obtained are almost always neglected, reducing the likelihood that numerical results are indeed compatible with observable process improvements. To fill this gap, this paper proposes a nonlinear multiobjective optimization strategy based on multivariate prediction capability ratios. For this, rotated Factor Analysis is used as the multivariate technique for grouping process characteristics and composing capability ratios, so that the prediction variance is taken as the natural variability of the process modeled and the expected value distances to the nadir solutions of the latent variables are taken as the allowed variability. Normal Boundary Intersection method, combined with Generalized Reduced Gradient algorithm, is used as the numerical scheme to maximize the prediction capability of Pareto optimal solutions. To illustrate the feasibility of the proposed strategy, we present a case study of end milling without cutting fluids of duplex stainless steel UNS S32205. Rotatable Central Composite Design, with three cutting parameters, was employed for data collection. Traditional multivariate and proposed approaches were compared. The results demonstrate that the proposed optimization strategy is able to provide solutions with satisfactory prediction capability for all variables analyzed, regardless of their convexities, optimization directions, and correlation structure. In addition, while critical-to-quality characteristics are more difficult to control, they have been favored by the proposed optimization regarding prediction capability, which was a desirable result.     

Optimum design of short journal bearings by enhanced artificial life optimization algorithm

This paper presents an optimum design of high-speed short journal bearing using an enhanced artificial life algorithm (EALA) to compute the solutions of optimization problem. The proposed hybrid EALA algorithm is a synthesis of an artificial life algorithm (ALA) and the random tabu search method (R-tabu method) to solve some demerits of the ALA. The emergence is the most important feature of the artificial life which is the result of dynamic interaction among the individuals consisting of the system and is not found in an individual. The artificial life optimization algorithm is a stochastic searching algorithm using the feature of artificial life. The feature of R-tabu method, which prevents converging to the local minimum, is combined with the ALA. One of the features of the R-tabu method is to divide any given searching region into several sub-steps. As the result of the combination of the two methods, the EALA not only converges faster than the ALA, but also can lead to a more accurate solution. In addition, this algorithm can also find all global optimum solutions. We applied the hybrid algorithm to the optimum design of a short journal bearing. The optimized results were compared with those of ALA and successive quadratic programming, and identified the reliability and usefulness of the hybrid algorithm.     

A solution to the facility layout problem having passages and inner structure walls using particle swarm optimization

This paper proposes a new approach called particle swarm optimization (PSO) to derive better solutions for unequal-area facility layouts that are to have inner walls and passages. PSO is a population based optimization tool, has fitness values to evaluate the population, update the population and search for the optimum with random techniques. A heuristic method is adopted for establishing the relationship between the facilities and passages. A comparative study is performed with the existing algorithm and it shows a better performance for the proposed algorithm. The objective of this study is to minimize material flow between facilities while at the same time satisfying the constraints of areas, aspect ratios of the facilities, and inner structure walls and passages. The proposed algorithm based on the PSO in this study was implemented with C++ language.