一种运用图论进行FMS无死锁调度的方法

在制造系统中 ,必须防止死锁的发生。本文提出一种在制造系统 (带有限缓冲区 )中搜索最优的无死锁调度算法。此算法建立在遗传算法的基础上 ,运用图论算法来保证无死锁调度结果。为了保证遗传算法生成的调度策略能够满足所要求的约束 ,运用图论方法选择无死锁个体 ,添加缓冲区 ,从而在保证了系统的主要性能指标的同时 ,得到系统可行的无死锁的调度结果。本文的主要创新之处在于提出了一种考虑系统缓冲区的无死锁调度方法。     

A deadlock-free scheduling with sequence-dependent setup times

This paper presents a novel approach to job shop scheduling with sequence-dependent setup times. The scheduling modeling is based on timed Petri nets. Control arcs are introduced to alleviate deadlocks, thus ensuring sequence-dependent setups. Based on siphon-related truncation, an efficient branch and bound algorithm for deadlock-free scheduling is devised. In addition, an effective priority rule is defined to handle large-scale problems. A series of experiments illustrate the validity of the priority rule and the scalability of our method for large-scale problems.     

RECONFIGURABLE PRODUCTION LINE MODELING AND SCHEDULING USING PETRI NETS AND GENETIC ALGORITHM

In response to the production capacity and functionality variations, a genetic algorithm (GA) embedded with deterministic timed Petri nets(DTPN) for reconfigurable production line(RPL) is proposed to solve its scheduling problem. The basic DTPN modules are presented to model the corresponding variable structures in RPL, and then the scheduling model of the whole RPL is constructed. And in the scheduling algorithm, firing sequences of the Petri nets model are used as chromosomes, thus the selection, crossover, and mutation operator do not deal with the elements in the problem space, but the elements of Petri nets model. Accordingly, all the algorithms for GA operations embedded with Petri nets model are proposed. Moreover, the new weighted single-objective optimization based on reconfiguration cost and E/T is used. The results of a DC motor RPL scheduling suggest that the presented DTPN-GA scheduling algorithm has a significant impact on RPL scheduling, and provide obvious improvements over the conventional scheduling method in practice that meets duedate, minimizes reconfiguration cost, and enhances cost effectivity.     

基于遗传算法和模型仿真的调度规则决策方法

为了完成特定生产环境下的调度规则选择问题,提出一种将遗传算法和过程仿真相结合的调度规则求解方式。在该求解方式中,遗传算法采用分段整数编码,每个染色体都代表一组可用于描述具体调度方案的规则组合;遗传操作包括选择、交叉、变异三种类型;为获得适应度函数值,利用基于某扩展Petri网的生产过程模型进行仿真,以在每一代种群中,得到与每个染色体相对应的各项性能指标值,进而以一种集成层次分析法和方案模糊评判的决策优化方法求取相应的适应度函数值。另外,为了改善串行遗传算法不切实际的解答时间,用主从式并行遗传算法代替传统遗传算法,保证了解在时间上和质量上的可行性。     

Application of Petri Nets and a Genetic Algorithm to Multi-Mode Multi-Resource Constrained Project Scheduling

Multi-mode and multi-resource constrained scheduling of a project is a complex task. This paper addresses the use of a Petri net as a modelling and scheduling tool in this context. The benefits of Petri nets in project scheduling are discussed. We propose extensions to Petri nets to suit scheduling of activities in a decision CPM. We also propose the use of a P-matrix for token movements in Petri nets. A genetic algorithm is used to find a better solution. Petri-net-aided software including genetic-algorithm-based search and heuristics is described to deal with a multi-mode, multi-constrained scheduling problem with pre-emption of activities.     

基于PetriNets和混合遗传算法的双资源JSP动态优化调度

以带有控制器的Petri网为建模工具对柔性生产调度中的离散事件建模,利用遗传算法和模拟退火算法获得调度结果,并通过Petri网进行控制.用于解决作业车间的加工受到机床、操作工人等生产资源制约条件下的优化调度.以生产周期为目标进行的优化调度,将遗传算法和模拟退火相结合.通过多种交叉、变异、概率更新选择、再分配策略等遗传和模拟操作,得到目标的最优或次优解.对算法进行了仿真研究,仿真结果表明该算法是有效性.     

Finite element modeling for maximum temperature in friction stir welding and its validation

This paper presents a new scheduling method for a flexible manufacturing system (FMS) in a Petri net framework. Petri nets can concisely model multiple lot sizes for each job, the strict precedence constraint, multiple kinds of resources, and concurrent activities. To decrease the likelihood of rejecting the critical markings, our algorithm adopts an improved checking method for previous generated marking. To reduce the computation complexity, an elaborate scheme is applied, which performs A* search locally and backtracking search globally in the reachability graph of the Petri net. Some numerical experiments are carried out to demonstrate usefulness of the algorithm.     

基于Petri网和模拟退火遗传算法的并行测试研究

针对自动测试系统中并行测试任务调度复杂、难以优化的问题，提出了一种Petri网技术和模拟退火遗传算法相结合的任务调度优化算法。首先为并行测试系统建立时间Petri网模型，然后将激发的变迁序列集作为并行测试任务调度路径。为了得到最优路径，引入模拟退火遗传（GASA）算法进行搜索。在搜索过程中，将能激发的变迁序列作为染色体，进行选择、交叉和变异。为了防止算法出现收敛过早，陷入局部最优解的现象，还要对个体进行模拟退火操作，最后得到测试完成时间最短的任务调度序列。     

Identification and elimination of redundant control places in petri net based liveness enforcing supervisors of FMS

In the past two decades, a number of Petri-net-based approaches were proposed for deadlock prevention in flexible manufacturing systems (FMS). An FMS is modeled as a Petri net, and then the controller or the liveness enforcing supervisor (LES) is computed as a Petri net. A live Petri net (LPN) guarantees deadlock-free operations of the modeled FMS. An LES consists of a number of control places (CPs) and their related arcs. To-date most of the attention has been paid to make the underlying Petri net models live without questioning whether or not all of the computed CPs are necessary. It is often the case that the number of CPs determined by these approaches is not minimal. Reducing it in order to reduce the complexity of the controlled system is an important issue that was not tackled before. To address this problem, this paper proposes a redundancy test for an LES of an FMS. The proposed approach takes an LPN model, controlled by n CPs, as input and in the existence of any redundant CPs it produces redundant and necessary CPs. The proposed approach is applicable to any LPN consisting of a Petri net model (PNM), controlled by means of a set of CPs.     

自动制造系统的一种死锁避免策略

基于Petri网的结构分析理论，提出了自动制造系统Petri网模型的一种死锁控制方法，在这种策略的控制下，避免了系统中死锁的产生，从而许多制造系统的Petri网模型具有活性，提出了一种保证所有严格极小信标至少含有一个托肯的方法，对冗余严格极小信标的研究，提高了Petri网复杂自动制造系统的建模能力。结果表明，在设计无死锁的Petri网格型时，不是所有的严格极小信标都要考虑，从而简化了设计结果和控制算法。     

A multi-objective genetic algorithm for scheduling in flow shops to minimize the makespan and total flow time of jobs

In this paper the problem of permutation flow shop scheduling with the objectives of minimizing the makespan and total flow time of jobs is considered. A Pareto-ranking based multi-objective genetic algorithm, called a Pareto genetic algorithm (GA) with an archive of non-dominated solutions subjected to a local search (PGA-ALS) is proposed. The proposed algorithm makes use of the principle of non-dominated sorting, coupled with the use of a metric for crowding distance being used as a secondary criterion. This approach is intended to alleviate the problem of genetic drift in GA methodology. In addition, the proposed genetic algorithm maintains an archive of non-dominated solutions that are being updated and improved through the implementation of local search techniques at the end of every generation. A relative evaluation of the proposed genetic algorithm and the existing best multi-objective algorithms for flow shop scheduling is carried by considering the benchmark flow shop scheduling problems. The non-dominated sets obtained from each of the existing algorithms and the proposed PGA-ALS algorithm are compared, and subsequently combined to obtain a net non-dominated front. It is found that most of the solutions in the net non-dominated front are yielded by the proposed PGA-ALS.     

Deadlock control of concurrent manufacturing processes sharing finite resources

A novel deadlock control policy is developed for modeling the concurrent execution of manufacturing processes with limited shared resources through a class of nets, ES³PR. A relevant property of the system behavior is that it is deadlock-free. Recent work has shown that deadlock situations in a plant system can be easily characterized by the structural analysis of the system, particularly, in terms of unmarked or insufficiently marked siphons in its Petri net model. The strict minimal siphons in a plant ES³PR net model are divided into elementary and dependent ones. The proposed deadlock prevention policy is to make all siphons satisfy maximal cs-property when the elementary siphons in the plant Petri net model are properly supervised via explicitly adding monitors for them with appropriate initial markings. Compared with the existing approaches in the literature, the advantage of the policy is that a much smaller number of supervisory places (monitors) are added and unnecessary iterative processes are avoided. Finally, its application is illustrated by a flexible manufacturing example.     

Scheduling FMS with alternative routings using Petri nets and near admissible heuristic search

This paper proposes and evaluates a near admissible heuristic search strategy and its application to a kind of flexible manufacturing system (FMS) scheduling in a Petri net framework. Petri nets can concisely model the strict precedence constraint, multiple kinds of resources, and concurrent activities. To cope with the complexities for scheduling of FMS with alternative routings, this paper proposes an admissible heuristic function based on the execution of P-timed Petri nets and presents an improved dynamic weighting A* strategy using the proposed heuristic function. The search scheme does not need to predict the depth of solution in advance and the quality of the search result is also controllable. Some numerical experiments are carried out to demonstrate usefulness of the algorithm.     

A Heuristic Search Approach Using Approximate Solutions to Petri Net State Equations for Scheduling Flexible Manufacturing Systems

This paper proposes a new heuristic search approach based on an analytic theory of the Petri net state equations for scheduling flexible manufacturing systems (FMSs) with the goal of minimizing makespan. The proposed method models an FMS using a timed Petri net and exploits approximate solutions of the net's state equation to predict the total cost (makespan) from the initial state through the current state to the goal. That is, the heuristic function considers global information provided by the state equation. This makes the method possible to obtain solutions better than those obtained using prior works (Lee and DiCesare, 1994a, 1994b) that consider only the current status or limited global information. In addition, to reduce memory requirement and thus to increase the efficiency of handling larger systems, the proposed scheduling algorithm contains a procedure to reduce the searched state space.     

装配生产线Petri网模型优化算法研究

运用Petri网方法,建立了装配生产线模型,给出了装配生产线调度控制系统的优化算法:按周期计划安排生产;按日进度计划用Petri网预测缺件;关键工序点的在制品控制     

基于Petri网和混合算法的作业车间优化

采用赋时变迁Petri网,建立了一种作业车间调度模型.通过为机器分配工序来消解因机器库所共享而引起的冲突,得到了表示调度方案的标志图,给出了一种生成可行调度标志图的方法.同时,提出了一种变迁激发序列编码的离散版粒子群算法,并将模拟退火算法嵌入到该粒子群算法中,以提高算法的优化性能.仿真结果验证了混合算法的可行性和有效性.     

生产时间可变间歇过程的Petri网模型及其调度

讨论了生产时间可变的多产品间歇过程的最优调度问题,给出了间歇过程在复杂中间的无限存储策略、有限存储策略、无中间存储策略和混杂存储策略下p-时间Petri网模型的描述方法,进而给出了基于可行调度集和修正分枝界定的间歇过程最短生产时间的最优调度算法.该算法利用一间歇过程最短生产时间不大于另一间歇过程最短生产时间的条件,有效地限制了对解空间的搜索,进而改善了算法的计算性能.仿真算例表明了所述方法的有效性.     

考虑任务行程时间的多载量自动导引车系统防死锁任务调度

针对多载量自动导引车（AGV）系统的任务调度和缓冲区死锁问题,提出了考虑任务行程时间的防死锁任务调度方案。以最小化延迟率和交通负荷不均衡度为目标,建立了任务调度模型;分析了任务调度中的实际约束,并在任务行程时间约束下构建了预测模型;针对任务调度模型,提出了一种基于人工免疫-灰狼优化（AI-GWO）算法的多目标防死锁任务调度方法,利用死锁避免规则禁止即将引发工位缓冲区死锁的任务运行,并融合AI-GWO算法对任务执行顺序进行多目标优化;最后,根据AGV负载均衡度进行AGV任务分配。仿真结果表明,上述任务行程时间预测模型具有较高的准确率,任务调度模型及防死锁调度方法具有较好的优化性能和计算效率,从而显著提高了物流系统的任务准时率和路径网络的交通负荷均衡度。     

基于模糊Petri网推理的半导体生产线动态调度研究

针对动态调度的特点,为了综合优化半导体生产线性能指标,尝试使用基于模糊Petri网推理的方法进行动态调度。首先分析了影响动态调度决策的生产线状态信息;然后建立了模糊Petri网形式化推理机,继而构建了面向半导体生产线的模糊Petri网推理模型;最后使用实际半导体生产线模型,将提出的方法与FIFO、EDD和CR策略进行了仿真比较。结果表明基于模糊Petri网推理的动态调度方法能够改善半导体生产线多种性能指标。