混流装配线调度问题的离散粒子群优化解

混流装配线调度问题是JIT生产中的一个重要问题。借鉴二进制遗传算法中的交叉操作过程，对传统的连续型粒子群算法进行改进，使其适用于离散问题的优化处理。然后以丰田公司的汽车组装调度函数作为目标函数，利用改进的离散粒子群算法进行求解。对比分析表明：新算法所得结果优于常用的目标追随法、遗传算法、模拟退火等方法。     

基于车型相似度的装配线排序

通过总结混流装配线排序问题,提出了一种新的装配线排序模型.根据装配车型的关键件不同,引入车型相似度的概念,应用遗传算法解决以最大化相似度总和为目标的混流装配线优化排序问题.经实践中应用,关键件装配更换次数减少了70%.     

多级分销网络设计及库存控制整合优化模型

从供应链集成的角度出发,基于多目标规划,根据排队论探讨了随机性需求下多级分销网络设计与库存控制的整合优化问题,提出了多级分销网络设计和库存控制整合优化的多目标规划模型.针对遗传算法收敛速度慢、易陷入局部最优等缺点,采用了基于并列选择法的遗传-模拟退火算法混合优化策略.实验证明,模拟退火算法提高了遗传算法的全局搜索能力,改善了遗传算法的求解性能.     

基于自适应模拟退火遗传算法的传感器优化配置研究

针对传感器优化配置组合优化问题,提出了一种基于模态置信度准则MAC的优化算法——自适应模拟退火遗传算法。以模态置信度MAC矩阵的最大非对角元的值极小为目标函数,针对满足传感器数量不变的约束条件问题,提出了二重结构编码遗传算法,并将传统的模拟退火算法改良后,作为一个独立的算子置于遗传算法进化过程中;为了避免出现过早收敛的现象,引入了自适应交叉和变异概率。算例结果表明该混合算法对传感器数目与位置同时实现了优化,得到了满足不同精度要求的传感器优化配置方案。     

基于混合动态罚函数改进协同优化算法的船舶结构静动力学优化设计

基于标准协同优化算法,针对已有改进协同优化算法的松弛因子法和罚函数法的缺陷,引入松弛因子构造混合动态罚函数改进协同优化算法,在Isight优化软件中采用了同时具备非支配排序遗传算法和自适应模拟退火算法优点的混合算法优化系统级。将改进的协同优化算法应用到船舶结构的多目标优化设计中,对船舶机舱结构的静力学和动力学特性进行优化,得到最优解并与已有的基于动态罚函数的协同优化算法结果进行比较。优化结果表明,基于混合动态罚函数改进协同优化算法的迭代次数更少,目标值更优且学科间不一致信息更小,对于实际船舶工程上的多目标多学科结构优化有一定应用价值。     

基于遗传模拟退火算法的空袭兵力分配及优化

对遗传模拟退火算法中的交叉、变异操作进行了改进，并实施了最优保留策略，形成了改进遗传模拟退火算法．以突击效果最大化和兵力损失最小化为目标函数，以空袭兵力总量的限制、空袭兵器挂载类型的限制等为约束条件，建立了空袭兵力分配及优化模型．在考虑兵力分配模型特点的基础上，利用改进遗传模拟退火算法求解．通过与多目标数学规划和标准遗传算法优化进行的比较表明，该方法能够有效地解决带约束的多目标优化问题．     

新型多目标遗传算法及其在导弹射击效能优化中的应用

多目标规划是一类重要的优化模型，有着广泛的实际应用，但其求解至今仍是运筹学的一个难点．针对一般约束多目标优化问题，在设计了新的适应度函数和选择算子的基础上，提出一种新型多目标遗传算法．将其应用于导弹对集群目标射击效能优化问题，验证了算法的有效性．     

遗传禁忌搜索算法在混流装配线排序中的应用

针对混流装配线排序问题，提出了一种混合遗传禁忌搜索算法，在每一代遗传演化之后，按一定比例随机选择部分解进行禁总搜索，以提高算法的全局搜索能力和收敛性。通过一个混流装配线排序实验，分别利用遗传算法和遗传禁忌搜索算法进行求解，结果表明遗传禁忌搜索算法具有更好的全局搜索能力和收敛性能。     

混流无等待总装分装流水线任务排序研究

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.     

正交-遗传试验算法的分析与应用

为了解决并联机器人机构的优化设计问题,提出一种基于正交试验设计法和遗传算法的优化方法。在简要讨论正交试验设计法和遗传算法的基本原理基础上,对两种方法的寻优算法、各个参数的对应关系作了比较分析,探讨了用正交表构造遗传算法中初始种群的方法。提出一种适用于设计变量多且适应度函数难求的“一代”正交-遗传试验法的思路和方法。将该方法应用于一种新型四自由度并联机器人机构的结构优化设计,得出以机构全域条件数为目标函数的机构结构优化尺寸方案。实例证明这种优化方法行之有效。     

混合粒子群算法在混流装配线优化调度中的应用

应用粒子群算法求解混流装配线的优化调度问题,给出粒子的构造方法,并针对算法中存在过早收敛的问题,提出了一种与局部优化和粒子微变异方法相结合的混合粒子群算法.给出了一个实例,实例应用粒子群算法和混合粒子群算法分别进行求解,与其他一些方法比较表明,混合粒子群算法可以有效、快速地求得混流装配线优化调度问题的解.     

考虑任务拆分特性与簇准备时间的并行机调度

在印制电路板钻孔任务调度等工程实际中,普遍存在一类具有任务拆分特性与簇准备时间的并行机调度问题,尚缺乏高效的优化模型和方法。针对该问题,首先建立以总拖期最小为目标的数学模型,以约束的形式将两个现有优势定理嵌入其中。为了高效求解实际规模问题,进一步提出嵌入优势定理的模拟退火算法。最后,基于随机生成的算例构造计算实验,以验证所建模型和算法的有效性。实验结果表明,嵌入优势定理的数学模型在问题求解规模和计算效率方面均优于现有数学模型,嵌入优势定理的模拟退火算法同样优于现有模拟退火算法。     

Lagrangean relaxation approach to joint optimization for production planning and scheduling of synchronous assembly lines

This paper focuses on simultaneous optimisation of production planning and scheduling problem over a time period for synchronous assembly lines. Differing from traditional top-down approaches, a mixed integer programming model which jointly considers production planning and detailed scheduling constraints is formulated, and a Lagrangian relaxation method is developed for the proposed model, whereby the integrated problem is decomposed into planning, batch sequencing, tardiness and earliness sub-problems. The scheduling sub-problem is modelled as a time-dependent travelling salesman problem, which is solved using a dynasearch algorithm. A proposition of Lagrangian multipliers is established to accelerate the convergence speed of the proposed algorithm. The average direction strategy is employed to solve the Lagrangian dual problem. Test results demonstrate that the proposed model and algorithm are effective and efficient.     

基于混合算法的飞机多层次装配计划优化及应用

分析了飞机装配过程的多层次任务网络特点,在满足工位、AO、工序多层次的时间约束、资源约束,并充分考虑任务移交情况下对工期影响的基础上,建立了资源约束下的多层次装配计划优化模型,提出一种针对多层次网络特性的离散粒子群算法与禁忌搜索结合的混合算法对该问题进行求解,并以某飞机机身的装配项目进行实例计算,验证了多层次计划优化模型与算法的有效性。     

Mathematical model and metaheuristics for simultaneous balancing and sequencing of a robotic mixed-model assembly line

This article presents the first method to simultaneously balance and sequence robotic mixed-model assembly lines (RMALB/S), which involves three sub-problems: task assignment, model sequencing and robot allocation. A new mixed-integer programming model is developed to minimize makespan and, using CPLEX solver, small-size problems are solved for optimality. Two metaheuristics, the restarted simulated annealing algorithm and co-evolutionary algorithm, are developed and improved to address this NP-hard problem. The restarted simulated annealing method replaces the current temperature with a new temperature to restart the search process. The co-evolutionary method uses a restart mechanism to generate a new population by modifying several vectors simultaneously. The proposed algorithms are tested on a set of benchmark problems and compared with five other high-performing metaheuristics. The proposed algorithms outperform their original editions and the benchmarked methods. The proposed algorithms are able to solve the balancing and sequencing problem of a robotic mixed-model assembly line effectively and efficiently.     

大规模带复杂任务约束的装配线平衡问题优化

介绍了装配线平衡问题的传统模型,分析了传统启发式算法与遗传算法在解决现今生产中的大规模带复杂任务约束问题时的弊端.针对传统模型的局限性,给出了修正模型,然后集合数种组合优化算法的优点,对传统启发式算法的候选规则与任务分配规则进行改进,给出了一种可行、高效率的优化算法,最后用实例验证了算法的优良性能.     

A heuristic approach for balancing mixed-model assembly line of type I using genetic algorithm

The mixed model assembly line is becoming more important than the traditional single model due to the increased demand for higher productivity. In this paper, a set of procedures for mixed-model assembly line balancing problems (MALBP) is proposed to make it efficiently balance. The proposed procedure based on the meta heuristics genetic algorithm can perform improved and efficient allocation of tasks to workstations for a pre-specified production rate and address some particular features, which are very common in a real world mixed model assembly lines (e.g. use of parallel workstations, zoning constraints, resource limitation). The main focus of this study is to study and modify the existing genetic algorithm framework. Here a heuristic is proposed to reassign the tasks after crossover that violates the constraints. The new method minimises the total number of workstation with higher efficiency and is suitable for both small and large scale problems. The method is then applied to solve a case of a plastic bag manufacturing company where the minimum number of workstations is found performing more efficiently.     

Topology optimization of fluid problems using genetic algorithm assisted by the Kriging model

A non‐gradient‐based approach for topology optimization using a genetic algorithm is proposed in this paper. The genetic algorithm used in this paper is assisted by the Kriging surrogate model to reduce computational cost required for function evaluation. To validate the non‐gradient‐based topology optimization method in flow problems, this research focuses on two single‐objective optimization problems, where the objective functions are to minimize pressure loss and to maximize heat transfer of flow channels, and one multi‐objective optimization problem, which combines earlier two single‐objective optimization problems. The shape of flow channels is represented by the level set function. The pressure loss and the heat transfer performance of the channels are evaluated by the Building‐Cube Method code, which is a Cartesian‐mesh CFD solver. The proposed method resulted in an agreement with previous study in the single‐objective problems in its topology and achieved global exploration of non‐dominated solutions in the multi‐objective problems. © 2016 The Authors International Journal for Numerical Methods in Engineering Published by John Wiley & Sons Ltd     

A multilevel genetic algorithm for the optimum design of structural control systems

A multilevel genetic algorithm (MLGA) is proposed in this paper for solving the kind of optimization problems which are multilevel structures in nature and have features of mixed‐discrete design variables, multi‐modal and non‐continuous objective functions, etc. Firstly, the formulation of the mixed‐discrete multilevel optimization problems is presented. Secondly, the architecture and implementation of MLGA are described. Thirdly, the algorithm is applied to two multilevel optimization problems. The first one is a three‐level optimization problem in which the optimization of the number of actuators, the positions of actuators and the control parameters are considered in different levels. An actively controlled tall building subjected to strong wind action is considered to investigate the effectiveness of the proposed algorithm. The second application considers a combinatorial optimization problem in which the number and configuration of actuators are optimized simultaneously, an actively controlled building under earthquake excitations is adopted for this case study. Finally, some results and discussions about the application of the proposed algorithm are presented. Copyright © 2002 John Wiley & Sons, Ltd.     

JIT mixed-model sequencing with batching and setup considerations via search heuristics

This research addresses the problem of sequencing items for production when it is desired that the production sequences result in minimal usage rates–surrogate measures for flexibility in a JIT environment. While seeking sequences with minimal usage rates, the number of required setups for the sequences is also considered, along with feasible batch-sizing combinations. The general intent is to find minimum usage-rate sequences for each associated number of setups and total batches. This multiple objective problem is addressed via a three-dimensional efficient frontier. Because the combinatorial nature of sequencing problems typically provides an intractable search space for problems of ‘real world’ size, the search heuristics of simulated annealing and genetic algorithms are presented and used to find solutions for several problem sets from the literature. Experimentation shows that the simulated annealing approach outperforms the genetic algorithm approach in both objective function and CPU performance.