DEADLOCK-FREE SCHEDULING METHOD BASED ON PETRI NET AND GA

An efficient algorithm for finding an optimal deadlock-free schedule in a manufacturingsystem with very limited buffer is presented. This algorithm is based on the effective genetic algorithm(GA) search method, and a formal Petri net structure is introduced to detect the token player assuringdeadlock-free. In order to make the scheduling strategy generated by GA meet the required constraintof deadlock-free, Petri net is involved to make the implementation of the job scheduling in an FMSdeadlock-free. The effectiveness and efficiency of the proposed approach is illustrated by using anexample.     

支持混流生产的可重组逻辑控制器设计方法

针对可重组制造系统混流生产、快速重组的特点,提出了一种基于Petri网的可重组模块化逻辑控制器设计方法。该控制器包括产品决策逻辑控制器和加工设备逻辑控制器,通过施加不同的条件变量,以明确相互之间的时序关系,并给出了相应的模块连接算法,产品变化通过变量调整可快速重组逻辑控制器。由Petri网自身特性证明,支持混流生产的可重组模块化逻辑控制器是活性、安全和可逆的,可直接转换为用于工业现场控制的顺序功能流程图。实际应用证明,该控制器具有高度模块化、易于重构、支持混流的特点。     

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.     

Designing integrated cellular manufacturing systems with scheduling considering stochastic processing time

This paper addresses a new mathematical model for cellular manufacturing problem integrated with group scheduling in an uncertain space. This model optimizes cell formation and scheduling decisions, concurrently. It is assumed that processing time of parts on machines is stochastic and described by discrete scenarios enhances application of real assumptions in analytical process. This model aims to minimize total expected cost consisting maximum tardiness cost among all parts, cost of subcontracting for exceptional elements and the cost of resource underutilization. Scheduling problem in a cellular manufacturing environment is treated as group scheduling problem, which assumes that all parts in a part family are processed in the same cell and no inter-cellular transfer is needed. Finally, the nonlinear model will be transformed to a linear form in order to solve it for optimality. To solve such a stochastic model, an efficient hybrid method based on new combination of genetic algorithm (GA), simulated annealing (SA) algorithm, and an optimization rule will be proposed where SA and optimization rule are subordinate parts of GA under a self-learning rule criterion. Also, performance and robustness of the algorithm will be verified through some test problems against branch and bound and a heuristic procedure.     

模糊柔性制造系统的混杂Petri网建模与调度

对模糊柔性制造系统的建模和调度进行了研究.定义了一种具有模糊区间速率的混杂Petri网模型,提出了模型的迁移使能和迁移引发语义,定义了弱使能迁移的模糊使能规则,给出了模型动态演变算法.建立了模糊柔性制造系统调度的模糊线性规划模型,并对典型的工业实例进行了分析.研究结果表明,基于所定义的混杂Petri模型能够有效地描述和分析模糊柔性制造系统.     

A scheduling model for serial jobs on parallel machines with different preventive maintenance (PM)

In factories during production, preventive maintenance (PM) scheduling is an important problem in preventing and predicting the failure of machines, and most other critical tasks. In this paper, we present a new method of PM scheduling in two modes for more precise and better machine maintenance, as pieces must be replaced or be repaired. Because of the importance of this problem, we define multi-objective functions including makespan, PM cost, variance tardiness, and variance cost; we also consider multi-parallel series machines that perform multiple jobs on each machine and an aid, the analytic network process, to weight these objectives and their alternatives. PM scheduling is an NP-hard problem, so we use a dynamic genetic algorithm (GA) (the probability of mutation and crossover is changed through the main GA) to solve our algorithm and present another heuristic model (particle swarm optimization) algorithm against which to compare the GA’s answer. At the end, a numerical example shows that the presented method is very useful in implementing and maintaining machines and devices.     

一种新调度类型及其在作业车间调度中的应用

研究改进遗传算法解决作业车间调度问题,问题染色体的编码采用基于工序的编码。针对传统的调度类型的局限性,提出全主动调度及其基于工序编码的产生机制。为了克服传统遗传算法求解调度问题易于早熟收敛的缺点,设计基于优先工序交叉(Precedence operation crossover,POX)和改进子代产生模式的遗传算法。用改进的遗传算法求解传统调度问题、交货期调度问题和提前/拖期(Earliness/Tardiness, E/T)调度问题,研究半主动、主动和全主动三种不同的调度解码机制对遗传算法提供解质量的影响。     

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.     

柔性装配单元任务规划的时间优化方法

详细研究了柔性装配单元中的任务规划问题,采用时延Petri网对系统行为进行建模并提出了任务规划的时间优化算法。为装配序列规划提供了重要的评价指标,并可用于指导装配系统设计。     

A distributed knowledge-based approach to flexible automation: The contract net framework

This article applied distributed artificial intelligence to the real-time planning and control of flexible manufacturing systems (FMS) consisting of asynchronous manufacturing cells. A knowledge-based approach is used to determine the course of action, resource sharing, and processor assignments. Within each cell there is an embedded automatic planning system that executes dynamic scheduling and supervises manufacturing operations. Because of the decentralized control, real-time task assignments are carried out by a negotiation process among cell hosts. The negotiation process is modeled by augmented Petri nets —the combination of production rules and Petri nets—and is excuted by a distributed, rule-based algorithm.     

A Petri net approach for simple assembly line balancing problems

A simple assembly line balancing problem of type-1 (SALBP-1) aims to minimize the number of workstations for a given cycle time. In the relevant literature, several heuristics based on a branch-and-bound procedure, tabu search, and genetic algorithms (GAs) were proposed to solve SALBP-1. In this paper, an algorithm based on the reachability analysis of Petri nets is developed for SALBP-1. The proposed algorithm searches enabled transitions (or assignable tasks) in the Petri net model of precedence relations between tasks, and then the task minimizing the idle time is assigned to the station under consideration. The algorithm is coded in MATLAB, and its efficiency is tested on Talbot’s and Hoffmann’s benchmark datasets according to some performance measures and classifications. A computational study validates its effectiveness on Tonge’s 70-task problem by comparison with optimal solutions of traditional heuristics and a GA.     

Hybrid evolutionary algorithm with marriage of genetic algorithm and extremal optimization for production scheduling

This paper presents a hybrid evolutionary algorithm with marriage of genetic algorithm (GA) and extremal optimization (EO) for solving a class of production scheduling problems in manufacturing. The scheduling problem, which is derived from hot rolling production in steel industry, is characterized by two major requirements: (i) selecting a subset of orders from manufacturing orders to be processed; (ii) determining the optimal production sequence under multiple constraints, such as sequence-dependant transition costs, non-execution penalties, earliness/tardiness (E/T) penalties, etc. A combinatorial optimization model is proposed to formulate it mathematically. For its NP-hard complexity, an effective hybrid evolutionary algorithm is developed to solve the scheduling problem through combining the population-based search capacity of GA and the fine-grained local search efficacy of EO. The experimental results with production scale data demonstrate that the proposed hybrid evolutionary algorithm can provide superior performances in scheduling quality and computation efficiency.     

CAPACITATED LOT SIZING AND SCHEDULING PROBLEMS USING HYBRID GA／TS APPROACHES

The capacitated lot sizing and scheduling probbm that involves in determining the production amounts and release dates for several items over a given planning horizon are given to meet dynamic order demand without incurring backloggings. Thi: problem considering overtime capacity is studied. The mathematical model is presented, and a genelic algorithm (GA) approach is developed to solve the problem. The initial solutions are generated after using heuristic method. Capacity balancing procedure is employed to stipulate the feasibility of the solutions. In addition, a technique based on Tabu search (TS) is inserted into the genetic algorithm to d;al with the scheduled overtime and help the convergence of algorithm. Computational simulation is conducted to test the efficiency of the proposed hybrid approach, which turns out to improve both the solution quality and execution speed.     

Production planning in virtual cell of reconfiguration manufacturing system using genetic algorithm

This paper focuses on the scheduling problem of the reconfiguration manufacturing system (RMS) for execution level, where the final objective is to output a production plan. The practical situation in Chinese factory is analyzed, and the characteristics are summarized into the contradiction between flow and job shop production. In order to handle this problem, a new production planning algorithm in virtual cells is proposed for RMS using an improved genetic algorithm. The advantages of this algorithm have three parts: (1) the virtual cell reconfiguration is formed to assist making production plans through providing relationship among task families and machines from cell formation; (2) The operation buffer algorithm is developed for flow style production in cells, which can realize the nonstop processing for flow style jobs; and (3) The multicell sharing method is proposed to schedule job shop jobs in order to fully utilize manufacturing capability among machines in multicells. Based on the above advantages, an improved genetic algorithm is developed to output scheduling plan. At last, the algorithm is tested in different instances with LINGO and the other genetic algorithm, and then the scheduling solution comparison shows the proposed algorithm can get a better optimum result with the same time using the comparison algorithm.     

基于混合遗传算法的混流混合车间协同调度问题

为解决一类具有多品种混流生产特征和作业车间与流水车间集成的混流混合车间协同调度问题,给出了以在制品成本最小为目标的混流混合车间调度问题模型;采用零件加工、部件装配、产品总装的三段协同编码方法,给出了一种集成模拟退火算法的混合遗传算法,并在模拟退火算法中引入变温度参数来平衡算法效率。最后,通过某冰箱混流装配企业典型实例验证了模型和算法的有效性。     

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。     

Non-identical parallel machine scheduling with fuzzy processing times using genetic algorithm and simulation

This paper addresses non-identical parallel machine scheduling problem with fuzzy processing times (FPMSP). A genetic algorithm (GA) approach embedded in a simulation model to minimize maximum completion time (makespan) is proposed. The results are compared with those obtained by using longest processing time rule, known as the most appropriate dispatching rule for such problems. This application illustrates the need for efficient and effective heuristics to solve FPMSPs. The proposed GA approach yields good results and reaches them fast and several times in one run. Moreover, due to its advantage of being a search algorithm, it can explore alternative schedules providing the same results.     

Synthesis of feedback control elements for discrete event systems using Petri net models and theory of regions

This paper describes a method for constructing a Petri-net-based controller for a discrete event system (DES) modelled by a Petri net. Assuming that an uncontrolled Petri net model of the DES and a set of forbidden state specifications are given, feedback control elements, i.e. a set of places and related transitions, with initial marking, are computed using the theory of regions, which is a formal synthesis technique for deriving Petri nets from automaton-based models. When feedback control elements are added to the uncontrolled Petri net model, the controlled (closed-loop) Petri net model of the system is obtained. The controlled Petri net model obtained is maximally permissive while guaranteeing that forbidden states do not occur. The proposed method is computationally efficient and does not suffer from the state explosion problem. Two examples are provided to show the applicability of the proposed method.     

Scheduling of complex manufacturing systems with Petri nets and genetic algorithms: a case on plastic injection moulds

This paper introduces significant improvements on a previous published work that addresses complex production scheduling problems using Petri nets (PNs) and genetic algorithms (GAs). The PN model allows a formal representation of the manufacturing system and of the special constraints of this kind of system, while the GA generates a near-optimal schedule through the structure provided by the PN. The corresponding manufacturing system is associated with a flexible job shop environment with features such as the fabrication of multiple parts and precedence relationships between such parts and assembly operations, in which the objective is the minimisation of the total weighted tardiness. As part of the modelling stage, a mixed integer linear programming formulation is proposed for this framework. The fabrication of a chess mould in a Colombian company is used in two ways: to introduce a proposed normalisation operator that improves the results by reducing the search space of the GA and to illustrate the use of PN modelling the special aforementioned constraints as well as the encoding of the chromosome used by the GA. The proposed approach was tested on randomly generated instances, and their performance was measure against optimal solutions or solutions provided by algorithms presented in previous work. The results confirm the relevance of this approach to schedule such complex manufacturing systems.     

遗传算法在多品种装配生产排序中的应用

针对多品种装配顺序的安排问题,以总工艺辅助时间最小为目标,表达为求解旅行商问题(TSP),提出并设计了合理的遗传算法。计算和仿真结果显示,该遗传算法的实用性和有效性。