双资源批量生产柔性作业车间多目标调度方法

针对双资源批量生产柔性作业车间调度问题,提出了一种多目标精细化调度方法。针对双资源批量生产柔性作业车间多目标调度问题特点,建立了一类以制造成本最低和完工时间最短为优化目标的双资源等量分批柔性作业车间调度多目标优化模型;提出了5种双资源批量生产柔性作业车间精细化调度技术;针对模型提出并设计了一种改进的NSGA II算法。通过案例分析验证了该方法的有效性。     

多目标等量分批柔性作业车间调度集成优化方法

针对多目标等量分批柔性作业车间调度问题,提出了一种集成优化方法。构建了一种以完工时间最短、生产成本最低为优化目标的多目标等量分批柔性调度集成优化模型。提出并设计了一种改进的非支配排序遗传算法对模型加以求解。算法中引入面向对象技术处理复杂的实体逻辑关系,采用三段式分段编码技术分别对分批方案、加工顺序、设备进行编码,采用三段式分段交叉和变异的混合遗传算子实现遗传进化,采用两种精细化调度技术进行解码以缩短流程时间。通过案例分析验证了所提方法的有效性。     

工件工序可并行的作业车间调度模型与求解

针对传统作业车间调度模型没有考虑工件工序存在并行性的不足,提出一种以最小化完工时间为目标的工件工序可并行作业车间调度模型,且在模型中考虑了工序加工设备柔性;设计了基于遗传算法的调度算法,其中染色体编码采用分段编码方式,并提出一种适用于工件工序存在并行性的染色体解码方法.实验结果表明,文中算法能够有效地解决工件工序可并行的作业车间调度问题.     

基于Q-learning算法解决多目标柔性作业车间调度问题

近几十年来，柔性作业车间调度问题由于其不确定性和复杂度引起了许多学者的关注。易陷入局部最优一直是元启发式算法解决柔性作业车间调度问题的不足之处，对此提出了一种改进的Q-learning强化学习算法，该改进算法设计并定义了状态空间和动作集，并通过随机生成可行的工序编码，在随机贪婪策略下选择合适的加工机器形成对应可行的机器编码，以最大完工时间和总能耗的多目标优化函数为可行解优劣的衡量标准。最后将所提算法模型使用车间调度问题的标准算例进行了验证，实验结果说明了所提算法的有效性，提升了解决多目标柔性作业车间问题的精度。     

混合鲸鱼优化算法求解柔性作业车间调度问题

提出一种混合正余弦鲸鱼优化算法,将其应用于柔性作业车间调度问题的研究,以最小化最大完工时间为目标;首先进行两段式编码,使连续型鲸鱼优化算法可应用于柔性作业车间调度问题,并对基本鲸鱼优化算法加入非线性收敛因子平衡搜索与开发阶段;以正余弦算法策略改进鲸鱼个体位置更新方式与螺旋方式,提升算法寻优能力;最后以实验数据验证混合正...     

基于遗传算法的车间作业调度问题求解

文章提出了一个求解车间作业调度问题的完备的、强壮的遗传算法。在分析车间作业调度问题的数学模型的基础上,给出了:(1)采用分段结构的染色体编码思想;(2)生成可行调度的算法;(3)计算调度目标函数的算法;(4)三种遗传算子及其辅助算子———修正算子的设计。最后,通过仿真验证了算法的有效性和稳定性。     

混合多目标算法用于柔性作业车间调度问题

在生产调度领域,柔性作业车间调度问题是一个非常重要的优化问题。大多数研究通常优化的目标只是最大完工时间,而在实际中,往往要考虑多个目标。因此,提出了一种新的混合多目标算法用于解决柔性作业车间调度问题,其中考虑了3个目标,分别是:最大完工时间、机器总负载和瓶颈机器负荷。算法设计了有效的编码方式和遗传算子,并采用非支配近邻免疫算法求解非支配最优解。为了提高算法性能,提出了3种不同的局部搜索策略,并将其结合在多目标算法中。在多个数据集上的实验对比结果表明,所提算法优于其它代表性的算法。此外,实验结果还验证了局部搜索技术的有效性。     

基于需求优先的多目标柔性车间调度研究

为满足按时提交客户货物的要求,需要优化企业的生产调度,现实的生产调度问题是传统车间调度问题的扩充,具有多目标、柔性等特性。针对柔性作业车间调度的需要,提出了在精益制造下的基于需求优先的多目标柔性车间调度算法。该算法以工件提前／拖期惩罚代价最小,调度最小生产周期为目标,基于规则的改进启发式调度,在调度过程中通过需求日期计算工件的优先级为每道工序分配合适的机器进行加工,可得到满意的较优解。与其他方法进行对比试验的结果表明,该算法在求解柔性作业车间调度问题是有效的。     

求解区间柔性作业车间调度的多目标进化算法

针对不确定多目标柔性作业车间调度问题,将工序加工时间采用区间数表示,以区间最大完工时间和区间机器总负荷为优化目标,构建多目标区间柔性作业车间调度模型,并设计一种多目标进化优化算法对该模型进行求解.算法采用混合策略生成初始化种群,并采用贪婪插入法对染色体进行解码,通过基于可能度的占优关系评价个体性能,将区间目标归一化结合拥挤距离反映优化解的分布情况.实验结果验证了所提出算法的有效性.     

考虑多目标的柔性工艺与调度集成优化算法

针对工艺规划与车间调度集成优化问题,在考虑零件的加工工序柔性、工序次序柔性及加工机器柔性的基础上,以最大完工时间、总加工成本和总拖期时间为优化目标,对多目标柔性工艺与车间调度集成问题建模,提出一种基于改进人工蜂群算法的多目标柔性工艺与车间调度集成优化策略,并提出邻域变异操作以及全局交叉操作,对种群进行更新。引入Pareto方法,通过对适应度评价、贪婪准则、Pareto最优解集构造和保存以及解得多样性维护等方面进行改进,设计了一种基于Pareto方法的多目标人工蜂群算法。最后,通过采用基本人工蜂群算法及改进人工蜂群算法对六个工件、五台机床的柔性工艺与车间调度集成问题进行优化,验证了改进算法的有效性。     

An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem

Flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem. Although the traditional optimization algorithms could obtain preferable results in solving the mono-objective FJSP. However, they are very difficult to solve multi-objective FJSP very well. In this paper, a particle swarm optimization (PSO) algorithm and a tabu search (TS) algorithm are combined to solve the multi-objective FJSP with several conflicting and incommensurable objectives. PSO which integrates local search and global search scheme possesses high search efficiency. And, TS is a meta-heuristic which is designed for finding a near optimal solution of combinatorial optimization problems. Through reasonably hybridizing the two optimization algorithms, an effective hybrid approach for the multi-objective FJSP has been proposed. The computational results have proved that the proposed hybrid algorithm is an efficient and effective approach to solve the multi-objective FJSP, especially for the problems on a large scale.     

Research on flexible job shop scheduling under finite transportation conditions for digital twin workshop

Flexible job shop scheduling is one of the most effective methods for solving multiple varieties and small batch production problems in discrete manufacturing enterprises. However, limitations of actual transportation conditions in the flexible job shop scheduling problem (FJSP) are neglected, which limits its application in actual production. In this paper, the constraint influence imposed by finite transportation conditions in the FJSP is addressed. The coupling relationship between transportation and processing stages is analyzed, and a finite transportation conditions model is established. Then, a three-layer encoding with redundancy and decoding with correction is designed to improve the genetic algorithm and solve the FJSP model. Furthermore, an entity-JavaScript Object Notation (JSON) method is proposed for transmission between scheduling services and Digital Twin (DT) virtual equipment to apply the scheduling results to the DT system. The results confirm that the proposed finite transportation conditions have a significant impact on scheduling under different scales of scheduling problems and transportation times.     

基于混合微粒群优化的多目标柔性Job-shop调度

应用传统方法求解多目标柔性Job-shop调度问题是十分困难的,微粒群优化采用基于种群的搜索方式,融合了局部搜索和全局搜索,具有很高的搜索效率.模拟退火算法使用概率来避免陷入局部最优,整个搜索过程可由冷却表来控制.通过对这两种算法的合理组合,建立了一种快速且易于实现的新的混合优化算法.实例计算以及与其他算法的比较说明,该算法是求解多目标柔性Job-shop调度问题的可行且高效的方法.     

教与同伴学习粒子群算法求解多目标柔性作业车间调度问题

针对多目标柔性作业车间调度问题,提出了带Pareto非支配解集的教与同伴学习粒子群算法。首先,以工件的最大完工时间、最大机器负荷和所有机器总负荷为优化目标建立了多目标柔性作业车间调度模型。然后,该算法结合多目标Pareto方法和教与同伴学习粒子群算法,采用快速非支配排序算法产生初始Pareto非支配解集,用提取Pareto支配层程序更新Pareto非支配解集,同时采用混合分派规则产生初始种群,采用开口向上抛物线递减的惯性权重选择策略提高算法的收敛速度。最后,对3个Benchmark算例进行仿真实验。理论分析和仿真表明,与带向导性局部搜索的多目标进化算法(MOEA-GLS)和带局部搜索的控制遗传算法(AL-CGA)相比,对于相同的测试实例,该算法能产生更多更好的Pareto非支配解;在计算时间方面,该算法要小于带向导性局部搜索的多目标进化算法。实验结果表明该算法可以有效解决多目标柔性作业车间调度问题。     

An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems

Scheduling for the flexible job-shop is very important in both fields of production management and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. The combining of several optimization criteria induces additional complexity and new problems. Particle swarm optimization is an evolutionary computation technique mimicking the behavior of flying birds and their means of information exchange. It combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. Simulated annealing (SA) as a local search algorithm employs certain probability to avoid becoming trapped in a local optimum and has been proved to be effective for a variety of situations, including scheduling and sequencing. By reasonably hybridizing these two methodologies, we develop an easily implemented hybrid approach for the multi-objective flexible job-shop scheduling problem (FJSP). The results obtained from the computational study have shown that the proposed algorithm is a viable and effective approach for the multi-objective FJSP, especially for problems on a large scale.     

An effective heuristic for flexible job-shop scheduling problem with maintenance activities

Most production scheduling problems, including the standard flexible job-shop scheduling problem (FJSP), assume that machines are continuously available. However, in most realistic situations, machines may become unavailable during certain periods due to preventive maintenance (PM). In this paper, a flexible job-shop scheduling problem with machine availability constraints is considered. Each machine is subject to preventive maintenance during the planning period and the starting times of maintenance activities are either flexible in a time window or fixed beforehand. Moreover, two cases of maintenance resource constraint are considered: sufficient maintenance resource available or only one maintenance resource available. To deal with this variant FJSP problem with maintenance activities, a filtered beam search (FBS) based heuristic algorithm is proposed. With a modified branching scheme, the machine availability constraint and maintenance resource constraint can be easily incorporated into the proposed algorithm. Simulation experiments are conducted on some representative problems. The results demonstrate that the proposed filtered beam search based heuristic algorithm is a viable and effective approach for the FJSP with maintenance activities.     

罩式退火炉生产优化调度方法及其应用

罩式退火炉的生产调度过程是一个典型的多工序.多约束、有重入的多机并行调度 问题,难于解析建模.对此问题,将离散事件仿真技术与改进的遗传算法相结合,提出罩式退 火炉生产的优化调度方法.生产现场的实际应用表明所提方法大大提高了生产设备的利用率 和生产效率.     

An object-oriented approach for multi-objective flexible job-shop scheduling problem

Flexible manufacturing systems are very complex to control and it is difficult to generate controlling systems for this problem domain. Flexible job-shop scheduling problem (FJSP) is one of the instances in this domain. It is a problem which inherits the job-shop scheduling problem (JSP) characteristics. FJSP has additional routing sub-problem in addition to JSP. In routing sub-problem each operation is assigned to a machine out of a set of capable machines. In scheduling sub-problem the sequence of assigned operations is obtained while optimizing the objective function(s). In this paper an object-oriented (OO) approach is presented for multi-objective FJSP along with simulated annealing optimization algorithm. Solution approaches in the literature generally use two-string encoding scheme to represent this problem. However, OO analysis, design and programming methodology help to present this problem on a single encoding scheme effectively which result in a practical integration of the problem solution to manufacturing control systems where OO paradigm is frequently used. OO design of FJSP is achieved by using UML class diagram and this design reduces the problem encoding to a single data structure where operation object of FJSP could hold its data about alternative machines in its own data structure hierarchically. Many-to-many associations between operations and machines are transformed into two one-to-many associations by inserting a new class between them. Minimization of the following three objective functions are considered in this paper: maximum completion time, workload of the most loaded machine and total workload of all machines. Some benchmark sets are run in order to show the effectiveness of the proposed approach. It is proved that using OO approach for multi-objective FJSP contributes to not only building effective manufacturing control systems but also achieving effective solutions.     

多目标粒子群优化算法在柔性车间调度中的应用

将粒子群优化(Particle Swarm Optimization,PSO)算法和混沌搜索方法结合在一起,提出一种求解多目标柔性作业车间调度问题(Flexible job shop scheduling problem,FJSP)的新算法,利用混沌对PSO的参数进行自适应优化来有效平衡算法的全局搜索和局部开挖能力,并采用混沌局部优化策略来改善算法的搜索性能.此外,为了搜索到问题的所有非劣解,采用基于模糊逻辑的适应度函数来评价粒子.对于四个典型FJSP实例的实验验证了算法的可行性和有效性.