基于粒子群算法的电网检修计划编制

在电网检修计划编制的基本原则和工作流程下,根据粒子群基本算法原理对电网检修计划编制进行数学建模。考虑检修时间作为自变量矢量,考虑期望缺供电量和检修成本作为其目标函数,考虑检修时间、检修资源和安全性等多个因素作为约束。结合粒子群算法原理和多目标优化理论,全局搜索非支配解集,形成帕累托前沿。最后依据管理者不同的偏好,通过加权计算的方式量化评估各优化目标,从而遴选出最优解,也即最符合决策人员预期的检修计划。通过与非劣排序多目标遗传算法和多目标粒子群算法进行对比,证明本文算法具有较高的实用性,提升了电网运行维护的自动化水平。     

Group shop scheduling with uncertain data and a general cost objective

In this paper, a stochastic group shop scheduling problem with a due date-related objective is studied. The group shop scheduling problem provides a general formulation including two other shop scheduling problems, the job shop and the open shop. Both job release dates and processing times are assumed to be random variables with known distributions. Moreover, earliness and tardiness of jobs are penalized at different rates. The objective is to minimize the expected maximum completion cost among all jobs. A lower bound on the objective function is proposed, and then, a hybrid approach following a simulation optimization procedure is developed to deal with the problem. An ant colony optimization algorithm is employed to construct good feasible solutions, while a discrete-event simulation model is used to estimate the performance of each constructed solution that, taking into account its lower bound, may improve the best solution found so far. The proposed approach is then evaluated through computational experiments.     

An ant colony algorithm for scheduling in flowshops with sequence-dependent setup times of jobs

The problem of scheduling in flowshops with sequence-dependent setup times of jobs is considered and solved by making use of ant colony optimization (ACO) algorithms. ACO is an algorithmic approach, inspired by the foraging behavior of real ants, that can be applied to the solution of combinatorial optimization problems. A new ant colony algorithm has been developed in this paper to solve the flowshop scheduling problem with the consideration of sequence-dependent setup times of jobs. The objective is to minimize the makespan. Artificial ants are used to construct solutions for flowshop scheduling problems, and the solutions are subsequently improved by a local search procedure. An existing ant colony algorithm and the proposed ant colony algorithm were compared with two existing heuristics. It was found after extensive computational investigation that the proposed ant colony algorithm gives promising and better results, as compared to those solutions given by the existing ant colony algorithm and the existing heuristics, for the flowshop scheduling problem under study.     

Integrated scheduling of flexible manufacturing system using evolutionary algorithms

Effective sequencing and scheduling of the material handling system (MHS) have an impact on the productivity of the flexible manufacturing system (FMS). The MHS cannot be neglected while scheduling the production tasks. It is necessary to take into account the interaction between machines and MHS. This paper highlights the importance of integration between production schedule and MHS schedule in FMS. The Giffler and Thompson algorithm with different priority dispatching rules is developed to minimize the makespan in the FMS production schedule. Its output is used for MHS scheduling where the distance traveled and the number of backtrackings of the automated-guided vehicles are minimized using an evolutionary algorithms such as an ant colony optimization algorithm and particle swarm optimization (PSO) algorithm. The proposed evolutionary algorithms are validated with benchmark problems. The results available for the existing algorithms are compared with results obtained by the proposed evolutionary algorithms. The analysis reveals that PSO algorithm provides better solution with reasonable computational time.     

基于最大-最小蚁群系统的装配序列规划

提出一种结合了蚁群系统与最大-最小蚂蚁系统优点的装配序列规划(Assembly sequence planning, ASP)方法。对近十年基于蚁群优化的ASP文献中采用的优化指标、装配信息模型、实例零件数等进行综述和比较。为提高序列的装配效率区分度,研究方向性、并行性、连续性、稳定性和辅助行程等5项指标的自动量化方法,将其融入到蚁群优化多目标启发式函数和适应值函数中。为提高对最优序列的搜索能力,以装配几何可行性为基础,从蚂蚁数量的确定、最大-最小信息素的界定、初始零件分配位置的绩效考核机制以及对并行零件组强制优化机制等方面,设计针对性解决方案,提出基于最大-最小蚁群系统的ASP算法。开发基于Siemens NX的装配规划系统AutoAssem。以阀门为实例,验证了算法内部各项优化措施的有效性,同时与优先规则筛选法、遗传算法及粒子群算法进行比较,分析该算法在运行效率和序列性能方面的优势。     

面向云计算环境任务调度的改进蚁群算法

云计算环境下的任务调度问题是一个NP完全问题,其目的是在各个处理节点上合理分配任务,优化调度策略以保证有效完成任务。以总任务完成时间最短和计算成本最低为优化目标,针对蚁群优化算法易陷入局部最优的缺陷,提出了一种求解该问题的改进蚁群算法。该算法将遗传算法的二点交叉算子融入到蚁群优化算法中,以提高蚁群优化算法的局部搜索能力。通过在云仿真平台Cloud Sim上进行仿真实验,结果表明改进蚁群算法缩短了总任务完成时间,降低了计算成本,从而证明了该算法能有效地解决云计算环境下的任务调度问题,并且其优化能力和收敛速度优于蚁群优化算法和改进离散粒子群算法。     

A new multi-objective ant colony algorithm for solving the disassembly line balancing problem

The disassembly line is the best choice for automated disassembly of disposal products. Therefore, disassembly line should be designed and balanced so that it can work as efficiently as possible. In this paper, a mathematical model for the multi-objective disassembly line balancing problem is formalized firstly. Then, a novel multi-objective ant colony optimization (MOACO) algorithm is proposed for solving this multi-objective optimization problem. Taking into account the problem constraints, a solution construction mechanism based on the method of tasks assignment is utilized in the algorithm. Additionally, niche technology is used to embed in the updating operation to search the Pareto optimal solutions. Moreover, in order to find the Pareto optimal set, the MOACO algorithm uses the concept of Pareto dominance to dynamically filter the obtained non-dominated solution set. To validate the performance of algorithm, the proposed algorithm is measured over published results obtained from single-objective optimization approaches and compared with multi-objective ACO algorithm based on uniform design. The experimental results show that the proposed MOACO is well suited to multi-objective optimization in disassembly line balancing.     

Parallel processor scheduling for minimizing total weighted tardiness using ant colony optimization

In the modern business environment, meeting due dates and avoiding delay penalties are very important goals that can be accomplished by minimizing total weighted tardiness. We consider a scheduling problem in a system of parallel processors with the objective of minimizing total weighted tardiness. Our aim in the present work is to develop an efficient algorithm for solving the parallel processor problem as compared to the available heuristics in the literature and we propose the ant colony optimization approach for this problem. An extensive experimentation is conducted to evaluate the performance of the ACO approach on different problem sizes with the varied tardiness factors. Our experimentation shows that the proposed ant colony optimization algorithm is giving promising results compared to the best of the available heuristics.     

A max–min ant colony system for assembly sequence planning

An improved ant colony optimization (ACO)-based assembly sequence planning (ASP) method for complex products that combines the advantages of ant colony system (ACS) and max–min ant system (MMAS) and integrates some optimization measures is proposed. The optimization criteria, assembly information models, and components number in case study that reported in the literatures of ACO-based ASP during the past 10 years are reviewed and compared. To reduce tedious manual input of parameters and identify the best sequence easily, the optimization criteria such as directionality, parallelism, continuity, stability, and auxiliary stroke are automatically quantified and integrated into the multi-objective heuristic and fitness functions. On the precondition of geometric feasibility based on interference matrix, several strategies of ACS and MMAS are combined in a max–min ant colony system (MMACS) to improve the convergence speed and sequence quality. Several optimization measures are integrated into the system, among which the performance appraisal method transfers the computing resource from the worst ant to the better one, and the group method makes up the deficiency of solely depending on heuristic searching for all parallel parts in each group. An assembly planning system “AutoAssem” is developed based on Siemens NX, and the effectiveness of each optimization measure is testified through case study. Compared with the methods of priority rules screening, genetic algorithm, and particle swarm optimization, MMACS is verified to have superiority in efficiency and sequence performance.     

基于Pareto解集蚁群算法的拆卸序列规划

为提高产品拆卸序列规划的效率,分析拆卸序列规划问题中的多个优化目标平衡问题,提出一种基于Pareto解集的多目标蚁群优化算法求解此类拆卸规划问题,并给出拆卸序列的构建过程。通过利用拆卸矩阵推导拆卸可行条件,获得可以执行拆卸操作的零件及其可行的拆卸方向。通过利用零件的轴向包围盒(Axis aligned bounding boxes,AABB)计算零件的拆卸行程。考虑拆卸方向改变次数、拆卸总行程、拆卸零件数量为优化目标,通过利用蚁群算法搜索可行解并计算各个解之间的支配关系,得到Pareto解集,实现求解优化的拆卸序列,给出算法的具体步骤。最后以单杠发动机为拆卸实例,利用所提方法进行拆卸序列规划求解,通过分析试验结果,并对比典型的单目标蚁群规划算法,证明了该方法的高效性和可行性。     

Improved multi-objective ant colony optimization algorithm and its application in complex reasoning

The problem of fault reasoning has aroused great concern in scientific and engineering fields.However,fault investigation and reasoning of complex system is not a simple reasoning decision-making problem.It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints.So far,little research has been carried out in this field.This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes.Three optimization objectives are considered simultaneously: maximum probability of average fault,maximum average importance,and minimum average complexity of test.Under the constraints of both known symptoms and the causal relationship among different components,a multi-objective optimization mathematical model is set up,taking minimizing cost of fault reasoning as the target function.Since the problem is non-deterministic polynomial-hard(NP-hard),a modified multi-objective ant colony algorithm is proposed,in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives.At last,a Pareto optimal set is acquired.Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set,through which the final fault causes can be identified according to decision-making demands,thus realize fault reasoning of the multi-constraint and multi-objective complex system.Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model,which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system.     

多目标批量生产柔性作业车间优化调度

研究批量生产中以生产周期、最大提前/最大拖后时间、生产成本以及设备利用率指标(机床总负荷和机床最大负荷)为调度目标的柔性作业车间优化调度问题。提出批量生产优化调度策略,建立多目标优化调度模型,结合多种群粒子群搜索与遗传算法的优点提出具有倾向性粒子群搜索的多种群混合算法,以提高搜索效率和搜索质量。仿真结果表明,该模型及算法较目前国内外现有方法更为有效和合理。最后,从现实生产实际出发给出多目标批量生产柔性调度算例,结果可行,可对生产实践起到一定的指导作用。     

一种求解柔性多目标JIT作业车间调度优化问题的方法

针对JIT作业车间多种工艺路线的工件调度问题,考虑了生产过程受多因素的影响。在混合遗传算法与拉格朗日松弛法结合的基础上,提出一种混合改进算法,采用分层多目标分层协调策略,建立了柔性多目标优化的适应函数模型,利用遗传算法更新拉格朗日乘子得到问题的最优解,仿真实例验证了该模型与求解方法是现实可行的。     

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。     

基于混合的多目标遗传算法的多目标流水车间逆调度问题求解方法*

将逆优化理论与方法引入车间调度领域,探讨近年来车间调度领域出现的一种新方法“逆调度”。研究多目标流水车间逆调度问题,建立考虑调度效率和调度稳定性的数学模型,综合考虑了加工参数改变量、系统改变量以及完工时间和等目标。提出一种基于混合的多目标遗传算法(Hybrid multi-objective genetic algorithm, HMGA)的求解方法,将多种策略进行混合以提高算法性能,主要包括快速非支配排序遗传算法(Non-dominated sorting genetic algorithm II, NSGAII)中的快速非支配排序方法、两种多样性保持策略、混合的精英保留策略,以及改进的局部搜索策略等。通过实例测试与方差分析(Analysis of variance, ANOVA),验证了该算法的有效性。     

交货期惩罚下柔性车间调度多目标Pareto优化研究

针对传统作业车间调度问题的局限性,结合实际生产过程的特点和约束条件,建立路径柔性的作业车间调度仿真模型。采用连续空间蚁群算法,对柔性车间作业进行多变量、多约束下的调度布局优化设计,在考虑各个机器提前/拖期完工的惩罚值,所有机器上的总负荷、成品合格率和最大设备利用率等性能指标更加合理情况下,为每次迭代产生的邻域解集作为Pareto非支配排序,防止算法操作过程中劣解的产生,提高求解效率。并与自适应免疫算法和交换序列混合粒子群法的优化结果进行对比,该算法可有效改善基本蚁群算法的停滞现象和全局寻优能力差的缺点。目前,该方法已在某机械公司进行示范,在提高加工效率、降低生产成本、减少协作费等方面效果显著。     

Integrating theory of constraints and particle swarm optimization in order planning and scheduling for machine tool production

Order planning and scheduling has become a significant challenge in machine tool enterprises, who want to meet various demands of different customers and make full use of existing resources in enterprises simultaneously. Based on the Theory of Constraints, a three-stage order planning and scheduling solution is proposed to optimize the whole system performance with bottleneck resources' capability as the constraints. After the identification of bottleneck resources, multicriteria priority sequencing is made with order per-contribution rate, order delivery urgency, and customer importance as the evaluation criteria, and the evaluation result deduced from the ideal point function can decide the production mode of all orders and products. Then, a PSO-based multiobjective optimization model is set up with minimizing bottleneck machines' makespan and minimizing total products' tardiness as the two objectives. Finally, the proposed solution is applied in one machine tool enterprise by integrating into Baosight MES (Manufacturing Execution System) system. In addition, some comparisons are carried out to evaluate the proposed PSO optimization method. The comparison with actual report shows that PSO can satisfy enterprise's needs better than before; the comparisons with genetic algorithm and ant colony optimization algorithms indicate that PSO is more effective than the others because of its faster convergence rate.     

基于Pareto蚁群算法的拆卸线平衡多目标优化

为提高产晶拆卸效率,针对拆卸线平衡问题建立了数学模型.该模型以最小拆卸线闲置率、负荷均衡和最小拆卸成本为优化目标.结合拆卸线平衡问题的具体特点,提出了一种改进的基于Pareto解集的多目标蚁群优化算法.算法采用小生境技术,引导蚂蚁搜索到分布良好的Pareto最优解集,并以被支配度和分散度为个体评价规则.实验测试结果表明了该算法的可行性.最后,结合企业生产实际,给出了所提模型与算法的具体应用.     

基于蚁群算法的单台批加工设备调度

从半导体生产线上批加工设备的实际情况着手,考虑了工件动态时间到达和菜单间整定时间等问题,利用蚁群算法,实现了半导体生产线上单台批加工设备的优化调度。基于实际生产线模型仿真验证的结果表明,能够在合理的时间内取得满意解。     

基于MapReduce和多目标蚁群算法的制造云服务动态选择算法

基于云计算技术和云服务技术研究了云服务的动态选择问题,给出了云制造服务层次化模型,提出了一种基于MapReduce和多目标蚁群算法的制造云服务动态选择算法(CSSMA)。依据CSSMA设计了多目标蚁群算法、Map函数、Reduce函数和优化策略等,并将其分布式并行运行于制造云平台中。仿真实验结果表明:CSSMA具备良好的处理大规模问题的能力,适用于制造云服务动态选择问题的求解。