扰动机制下的遗传算法求解柔性作业车间调度

针对柔性作业车间调度问题,提出一种扰动机制下的遗传算法,该算法以最大完工时间最小为优化目标.为了克服传统遗传算法早熟的缺点,引入差异度阈值策略对传统遗传算法的结构进行动态调整,同时设计了灾变机制和大变异策略相结合的扰动机制,增强了算法的搜索性能.最后,通过基准案例进行测试并与其他算法的结果进行对比,验证了扰动机制下的遗...     

单件小批量生产作业计划的倒排产算法研究

提出一种用于单件小批量生产中编制作业计划的算法模型———倒排产算法 ,并建立一套包含此模型的作业计划及调度监控集成系统。在科龙等企业的生产部门进行实施 ,获得良好效果     

Crowding-measure-based multiobjective evolutionary algorithm for job shop scheduling

Multiobjective evolutionary algorithm (MOEA) has attracted much attention in the past decade; however, the application of MOEA to practical problems such as job shop scheduling is seldom considered. In this paper, crowding-measure-based multiobjective evolutionary algorithm (CMOEA) is first designed, which makes use of the crowding measure to adjust the external population and assign different fitness for individuals; then CMOEA is applied to job shop scheduling to minimize makespan and the total tardiness of jobs. Finally, the comparison between CMOEA and SPEA demonstrates that CMOEA performs well in job shop scheduling.     

求解作业车间调度问题的快速启发式算法

首先将作业车间调度问题转换为一个搭积木模型,受这个直观模型的启发,提出了一个启发式的搭积木规则,该规则综合考虑了已经搭好的积木的顶高和将要搭积木的剩余高度。基于这个规则,提出了一个求解作业车间调度问题的快速启发式算法,对国际上通用的benchmark例的模拟实验结果表明,提出的算法优于经典的优先分配启发式算法。     

采用多个体交叉的遗传算法求解作业车间问题

为改善目前求解Job-Shop问题中的遗传算法的性能,加快搜索最优调度解的速度,首先分析了目前Job-Shop问题自身的求解难点和遗传算法的特点,并借鉴生物学的依据,提出了多个体交叉的遗传算法。该算法在遗传过程中采用多个体遗传算子,充分利用个体的优良性质,对不可行调度解根据多个体修补原则进行修正,可保证遗传后代的合法性和多样性,能够加快最优调度解的搜索时间。仿真结果充分证明了该算法的有效性。     

求解作业车间调度问题的多种群杂交遗传算法

提出了多种群杂交改进遗传算法,在约束条件处理中引入可能解空间概念;设计了机床编号可变的基于工序的编码。父代个体和交叉变异得到的个体在选择操作中具有同等选择机会,保证最优个体保留到下一代,又能保持子代的多样性。在遗传过程中引入修正种群,实现多种群杂交,以保持种群的多样性。应用实例分析和工程实践表明,算法稳定可靠,运行效率大大提高。     

基于最小完工期的离散型作业车间调度问题的遗传算法设计

针对离散型生产作业中的车间调度问题,以完工期最小为目标,设计了遗传算法,并利用PB语言编程实现该算法.最后,将该算法应用于某一钢铁公司金工车间的车间调度,并与原调度的结果做了比较,证明了本算法在实际应用中的有效性.     

一种小批量作业计划的调度算法

作业计划的制定是一项非常重要的工作,通过对作业计划问题的详尽描述,提出了一种用于小批量生产的调度算法.这种算法以任务级和设备级两个层次逐层推进,来实现作业计划的制定,并且考虑了批量问题.     

Parallel line job shop scheduling using genetic algorithm

Parallel line job shop scheduling involves the optimal allocation and scheduling of jobs in multiple processing lines. Each job is allocated to a particular line and is processed to completion in that line. Also, all jobs allocated to a line are processed in a particular order. The objective of this paper is to find the optimal allocation of jobs to lines and also the optimal order of jobs processed in each line based on individual processing times and set up times. The optimal schedule gives the minimum makespan for the completion of all jobs. The optimization technique used is genetic algorithm.     

An artificial immune algorithm for multiple-route job shop scheduling problem

This paper addresses the multiple-route job shop scheduling problem to minimize makespan. The problem is recognized to be extremely difficult because of its combinatorial nature of integer optimization and the large size of the real problem. The goal is, thus, to obtain near-optimal schedules in a computationally efficient manner. Mathematical formulation of the problem is first presented. Then, an approach based on artificial immune algorithm is proposed. In order to evaluate the effectiveness of the proposed approach, 30 problems in small, medium, and large size are designed and solved using the proposed approach. Problems are also solved using Lingo software and the results are compared. The computational results show that the proposed approach generates high-quality schedules in a timely fashion.     

A heuristic algorithm for solving flexible job shop scheduling problem

This paper deals with the flexible job shop scheduling problem with the objective of minimizing the makespan. An efficient heuristic based on a constructive procedure is developed to obtain high-quality schedules very quickly. The algorithm is tested on benchmark instances from the literature in order to evaluate its performance. Computational results show that, despite its simplicity, the proposed heuristic can obtain effective solutions in very short and nearly zero time and is comparable with even metaheuristic algorithms and promising for practical problems.     

基于遗传退火算法的绿色作业车间调度

绿色制造和智能制造是工业发展的两大趋势,针对目前作业车间能耗大、设备利用率低,以及产品拖期严重等问题,以智能制造业环境中的作业车间为研究对象,建立了以车间总能耗和总拖期惩罚为优化目标的多目标调度模型,并通过设置权重系数来调节优化目标决策偏好;基于遗传算法收敛速度快、全局搜索能力强,以及模拟退火算法突跳性强的特点,设计一...     

A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem

Biogeography-based optimization (BBO) algorithm is a new kind of optimization technique based on biogeography concept. This population-based algorithm uses the idea of the migration strategy of animals or other species for solving optimization problems. In this paper, the BBO algorithm is developed for flexible job shop scheduling problem (FJSP). It means that migration operators of BBO are developed for searching a solution area of FJSP and finding the optimum or near-optimum solution to this problem. In fact, the main aim of this paper was to provide a new way for BBO to solve scheduling problems. To assess the performance of BBO, it is also compared with a genetic algorithm that has the most similarity with the proposed BBO. This similarity causes the impact of different neighborhood structures being minimized and the differences among the algorithms being just due to their search quality. Finally, to evaluate the distinctions of the two algorithms much more elaborately, they are implemented on three different objective functions named makespan, critical machine work load, and total work load of machines. BBO is also compared with some famous algorithms in the literature.     

改进混合Jaya优化算法求解柔性作业车间调度问题

针对柔性作业车间调度问题(Flexible Job shop Scheduling Problem,FJSP),以最小化最大完工时间为优化目标,在研究现有Jaya优化算法的基础上,结合邻域搜索算法,提出一种改进混合Jaya优化算法.首先,针对MSOS编码方式设计种群初始化方法;其次,提出一种基于Jaya优化算法思想的离散化更新算子,使算法适用于FJSP;然后,设计了2种新型邻域结构,有效增强了算法的局部寻优能力;最后,通过3组著名的FJSP基准算例进行测试,并与相同目标的其他算法进行对比分析.结果 表明,改进混合Jaya优化算法能有效求解FJSP,且比相同目标的其他算法有更强的求解能力.     

An effective hybrid genetic algorithm for the job shop scheduling problem

From the computational point of view, the job shop scheduling problem (JSP) is one of the most notoriously intractable NP-hard optimization problems. This paper applies an effective hybrid genetic algorithm for the JSP. We proposed three novel features for this algorithm to solve the JSP. Firstly, a new full active schedule (FAS) procedure based on the operation-based representation is presented to construct a schedule. After a schedule is obtained, a local search heuristic is applied to improve the solution. Secondly, a new crossover operator, called the precedence operation crossover (POX), is proposed for the operation-based representation, which can preserve the meaningful characteristics of the previous generation. Thirdly, in order to reduce the disruptive effects of genetic operators, the approach of an improved generation alteration model is introduced. The proposed approaches are tested on some standard instances and compared with other approaches. The superior results validate the effectiveness of the proposed algorithm.     

考虑学习效应的单人作业车间多目标调度算法

为减少受学习效应影响的单人作业车间的最大完工时间和工人行走时间,建立了考虑依赖加工时间和的学习效应的单人单工序多机车间调度模型,提出考虑学习效应的多目标贪婪算法(MOGL),融合了带精英策略的非支配排序遗传算法(NSGA-Ⅱ)与基于贪婪的邻域搜索,构造了迭代多目标遗传算法(IMOGA),并基于MO-GL设计了初始解集....     

改进细菌觅食算法求解柔性作业车间调度问题

针对柔性作业车间调度问题的NP难特性,提出一种改进的细菌觅食优化算法。采用集成法策略同时求解柔性作业车间调度问题的机器分配和工序调度子问题。将细菌个体表示为工序串,建立问题和算法的映射关系;分别针对普通细菌个体和当前最优个体设计了多重趋化操作,以增强算法的局部搜索能力;复制操作设置繁殖阈和死亡阈,以提高对历史经验的继承程度;迁移/驱散操作中,结合改进的LPT启发式规则,提出带倾向性的迁移/驱散操作方式。采用正交试验对算法的重要参数进行了优化配置,通过搜索算子优化效果对比实验证明了正交试验的结论;进行了收敛性能对比实验,证明算法具有优秀的全局开发能力和局部探索能力;典型算例实验结果表明,该算法能够有效求解柔性作业车间调度问题。     

A two-stage genetic algorithm for large size job shop scheduling problems

The majority of large size job shop scheduling problems are non-polynomial-hard (NP-hard). In the past few decades, genetic algorithms (GAs) have demonstrated considerable success in providing efficient solutions to many NP-hard optimization problems. But there is no literature available considering the optimal parameters when designing GAs. Unsuitable parameters may generate an inadequate solution for a specific scheduling problem. In this paper, we proposed a two-stage GA which attempts to firstly find the fittest control parameters, namely, number of population, probability of crossover, and probability of mutation, for a given job shop problem with a fraction of time using the optimal computing budget allocation method, and then the fittest parameters are used in the GA for a further searching operation to find the optimal solution. For large size problems, the two-stage GA can obtain optimal solutions effectively and efficiently. The method was validated based on some hard benchmark problems of job shop scheduling.     

基于改进遗传算法的车间调度问题求解

针对车间调度问题(Job Shop Problem,JSP)的特点,提出一种改进遗传算法。该方法利用剩余作业时间最多(MostWork Remaining,MWR)的工件优先排列的启发式规则来产生初始种群,并且在进化过程中采用分代交叉算子进行操作来避免算法早熟。通过分析算例结果表明,该改进遗传算法可以在进化初期就得到比较理想的调度方案,而且优化收敛速度快、结果优,更适用于解决车间调度问题。     

多目标离散灰狼优化算法求解作业车间节能调度问题

针对作业车间节能调度问题,建立了一种以优化总能耗和工件最大完工时间为目标的节能调度模型,并提出一种多目标离散灰狼优化算法进行求解.根据问题的特点,首先采用离散整数编码方式,利用调度规则生成初始种群;其次引入一种基于跟踪模式和搜寻模式的双模式并行搜索方法,并在搜索过程中动态调整两种模式下个体的数目,以协调算法全局和局部搜...