基于Agent的分布式动态作业车间调度

Agent技术是分布式工业系统建模的一种重要方法．本文对Agent及多Agent技术进 行了简要总结,综述了Agent技术在制造作业车间调度中的应用研究概况,提出了一种基于 合同网协议投标机制的多Agent分布式动态作业车间调度方案．     

柔性作业车间调度的多Agent协商策略

柔性作业车间调度问题是经典作业车间调度问题的扩展。为此,提出一种新的基于招投标的多Agent协商调度策略,并研究各Agent协商时的价格函数。系统主要由工件Agent和机器Agent组成,工件Agent通过招投标的方式,选择合适的机器完成加工任务,机器Agent按照市场机制通过自由竞争获得工件的加工权,根据基于规则的调度策略处理工件。用Java设计仿真实验程序,并通过实验验证所提价格协商函数的有效性。     

面向紧急任务的柔性车间混合动态调度模型*

针对柔性车间紧急任务插入调度困难的问题,提出一种柔性车间混合动态调度模型。提出并阐述多参数任务优先级和最小代价任务集;以最小代价为目标,依据任务优先级和已调度任务与紧急任务的相似性对最小代价任务集实施调度,并确定为执行紧急任务而要中断和释放的已调度任务;最后,通过算例验证该模型在紧急任务插入时,系统得到满意调度解的可行性和有效性。     

TinyOS中DSA调度策略的研究

针对TinyOS任务调度采用非剥夺的先来先服务调度策略,而产生的系统紧急任务不能及时得到响应及节点吞吐量下降情况,该文提出一种新的可抢占时限短作业调度策略——DSA。在绝对时限前执行硬实时任务,满足了系统对实时任务的响应要求,提高处理器的响应速度,对软实时任务实行短作业优先调度策略,提高系统的吞吐量。在TinyOS上测试表明,DSA策略在不影响TinyOS原有性能的情况下,改进了传感器网络承担实时性任务的运行效果。     

基于GPGP协同机制的多Agent车间调度方法研究

车间调度作为车间制造系统的重要组成部分,影响着整个车间制造系统的敏捷性和智能性.但是,由于资源和工艺约束的并存,使得车间调度成为一类NP-hard问题.基于静态的智能算法与动态的多Agent思想,提出了一种结合通用部分全局规划(generalized partial global planning,GPGP)机制与多种智能算法的多Agent车间调度模型,设计了从"初始宏观调度"到"微观再调度"的大规模复杂问题的调度步骤,并构建了一个柔性强且Agent可自我动态调度的仿真系统.同时,从理论上总结了GPGP基本协同机制的策略,实现了二级多目标优化调度.最后使用DECAF仿真Agent软件模拟了车间调度的GPGP协同机制,并与CNP,NONE机制进行了比较.结果表明,所提出的模型不仅提高了调度的效率,而且降低了资源的损耗.     

面向Agent的车辆监控空间数据调度模型

设计面向Agent的车辆监控空间数据调度模型,通过软件Agent实体将静态海量地图数据与实时车辆空间数据的调度分解成原子任务进行处理并显示。该模型充分利用Agent的自治性、主动性和反应性特点,自主处理地图数据和车辆信息调度,在软件层次上实现车辆动态信息显示的实时性,有效解决了海量地图数据显示造成的响应过慢或停顿问题,降低了系统设计的复杂度,为如何将Agent思想和方法学应用于GIS开发提供了参考。     

基于TinyOS的HRRF调度策略

针对传感器网络操作系统TinyOS采用非剥夺的先来先服务调度策略,系统紧急任务不能得到及时响应及节点吞吐量下降的情况,提出了一种可抢占HRRF(Highest-Response-Ratio First)作业调度策略。HRRF算法采用对于实时性较强的任务优先调度策略,满足了系统对实时任务的响应,提高了处理器的响应速度;对于软实时任务采用高响应比(任务等待时间/需运行时间)调度策略,提高了系统的效率。在TinyOS上的测试表明,HRRF策略在不影响TinyOS原有性能的情况下极大改善了传感器网络承担实时性任务的运行效果。     

基于多Agent协商的柔性车间调度系统

针对柔性作业车间调度的特点,提出了一种基于多agent协商的柔性作业车间调度系统。系统由工件agent,机器agent和工序agent组成。Agent之间通过相互发送消息和响应消息进行交互,并且通过消息相应函数按照各agent局部的信息、同时兼顾系统的性能进行决策。工件agent通过招标的方式,选择合适的机器完成加工任务,机器agent通过竞争来获得工件的加工权。最后用Java语言在Eclipse平台上进行程序设计,对柔性作业车间调度的平均滞后问题进行仿真实验,并与传统的分派规则比较,结果显示所提方法的优越性。     

考虑工件移动时间的柔性作业车间调度问题研究

分析生产车间的实际生产状况,建立了考虑工件移动时间的柔性作业车间调度问题模型,该模型考虑了以往柔性作业车间调度问题模型所没有考虑的工件在加工机器间的移动时间,使柔性作业车间调度问题更贴近实际生产,让调度理论更具现实性。通过对已有的改进遗传算法的遗传操作进行重构,设计出有效求解考虑工件移动时间的柔性作业车间调度问题的改进遗传算法。最后对实际案例进行求解,得到调度甘特图和析取图,通过对甘特图和析取图的分析验证了所建考虑工件移动时间的柔性作业车间调度问题模型的可行性和有效性。     

基于Flexsim的车间调度优化

基于三维动画仿真软件Flexsim,文中对航空附件加工车间这种多品种、小批量生产的作业车间（Job-Shop）进行了调度优化研究。介绍了Flexsim连接数据库的技术与遗传算法求解生产调度的方法;在Flexsim中建立虚拟生产车间模型,并且在Flexsim虚拟车间模型内部嵌入C＋＋数据库操纵程序,将仿真模型与生产管理数据库连接,使模型可以实时采集生产数据;最后通过实例说明Flexsim仿真与调度优化相结合的方法可以有效地提高航空附件加工车间的效益,证明了方法的有效性。     

On the identical parallel-machine rescheduling with job rework disruption

This paper investigates an issue of rescheduling on identical parallel machines where the original jobs have already been scheduled to minimize the total completion time, when a single set of jobs to be reworked re-arrives and creates a job rework disruption. Two conflicting rescheduling criteria are considered: the total completion time, as the measure of scheduling cost (efficiency); and the number of jobs assigned to different machines in the original schedule and newly generated schedule, as the measure of disruption cost (stability). Further, the rescheduling problem is defined as a bi-criteria scheduling problem. Two polynomial time algorithms are proposed to lexicographically optimize the two criteria. Besides, the set of all efficient schedules with respect to the two criteria can be also generated in polynomial time.     

Parallel machine match-up scheduling with manufacturing cost considerations

Many scheduling problems in practice involve rescheduling of disrupted schedules. In this study, we show that in contrast to fixed processing times, if we have the flexibility to control the processing times of the jobs, we can generate alternative reactive schedules considering the manufacturing cost implications in response to disruptions. We consider a non-identical parallel machining environment where processing times of the jobs are compressible at a certain manufacturing cost, which is a convex function of the compression on the processing time. In rescheduling it is highly desirable to catch up the original schedule as soon as possible by reassigning the jobs to the machines and compressing their processing times. On the other hand, one must also keep the manufacturing cost due to compression of the jobs low. Thus, one is faced with a tradeoff between match-up time and manufacturing cost criteria. We introduce alternative match-up scheduling problems for finding schedules on the efficient frontier of this time/cost tradeoff. We employ the recent advances in conic mixed-integer programming to model these problems effectively. We further provide a fast heuristic algorithm driven by dual prices of convex subproblems for generating approximate efficient schedules.     

Minimizing makespan subject to minimum flowtime on two identical parallel machines

We consider the problem of scheduling jobs on two parallel identical machines where an optimal schedule is defined as one that gives the smallest makespan (the completion time of the last job) among the set of schedules with optimal total flowtime (the sum of the completion times of all jobs). We propose an algorithm to determine optimal schedules for the problem, and describe a modified multifit algorithm to find an approximate solution to the problem in polynomial computational time. Results of a computational study to compare the performance of the proposed algorithms with a known heuristic shows that the proposed heuristic and optimization algorithms are quite effective and efficient in solving the problem.Scope and purposeMultiple objective optimization problems are quite common in practice. However, while solving scheduling problems, optimization algorithms often consider only a single objective function. Consideration of multiple objectives makes even the simplest multi-machine scheduling problems NP-hard. Therefore, enumerative optimization techniques and heuristic solution procedures are required to solve multi-objective scheduling problems. This paper illustrates the development of an optimization algorithm and polynomially bounded heuristic solution procedures for the scheduling jobs on two identical parallel machines to hierarchically minimize the makespan subject to the optimality of the total flowtime.     

Minimizing Makespan and Preemption Costs on a System of Uniform Machines

It is well known that for preemptive scheduling on uniform machines there exist polynomial time exact algorithms, whereas for non-preemptive scheduling there are probably no such algorithms. However, it is not clear how many preemptions (in total, or per job) suffice in order to guarantee an optimal polynomial time algorithm. In this paper we investigate exactly this hardness gap, formalized as two variants of the classic preemptive scheduling problem. In generalized multiprocessor scheduling (GMS) we have a job-wise or total bound on the number of preemptions throughout a feasible schedule. We need to find a schedule that satisfies the preemption constraints, such that the maximum job completion time is minimized. In minimum preemptions scheduling (MPS) the only feasible schedules are preemptive schedules with the smallest possible makespan. The goal is to find a feasible schedule that minimizes the overall number of preemptions. Both problems are NP-hard, even for two machines and zero preemptions. For GMS, we develop polynomial time approximation schemes, distinguishing between the cases where the number of machines is fixed, or given as part of the input. Our scheme for a fixed number of machines has linear running time, and can be applied also for instances where jobs have release dates, and for instances with arbitrary preemption costs. For MPS, we derive matching lower and upper bounds on the number of preemptions required by any optimal schedule. Our results for MPS hold for any instance in which a job, J_j, can be processed simultaneously by ρ_j machines, for some ρ_j ≥ 1.     

An aggregation procedure for the deterministic job-shop problem

A new approach is proposed in this paper to solve the job-shop scheduling problem. Instead of considering operations or machines, the construction of partial schedules by dealing with jobs, one after the other, is suggested. A partial schedule for given jobs is characterized by the sequence of their operations on each machine. The principle of the algorithm is to aggregate a new job on the current schedule, i.e. to insert its operations without altering the previous order. Two main theoretical results are presented: firstly, the selection procedure for jobs and secondly, the aggregation algorithm. Next the method is explained using a simple example. Finally, the authors present and comment on computational results for an implementation of the algorithm, for some well-known job-shop problems.     

A dominant class of schedules for malleable jobs in the problem to minimize the total weighted completion time

This paper is about scheduling parallel jobs, i.e. which can be executed on more than one machine at the same time. Malleable jobs is a special class of parallel jobs. The number of machines a malleable job is executed on may change during its execution.In this work, we consider the NP-hard problem of scheduling malleable jobs to minimize the total weighted completion time (or mean weighted flow time). For this problem, we introduce the class of “ascending” schedules in which, for each job, the number of machines assigned to it cannot decrease over time while this job is being processed.We prove that, under a natural assumption on the processing time functions of jobs, the set of ascending schedules is dominant for the problem. This result can be used to reduce the search space while looking for an optimal solution.     

A heuristic for job shop scheduling to minimize total weighted tardiness

This paper considers the job shop scheduling problem to minimize the total weighted tardiness with job-specific due dates and delay penalties, and a heuristic algorithm based on the tree search procedure is developed for solving the problem. A certain job shop scheduling to minimize the maximum tardiness subject to fixed sub-schedules is solved at each node of the search tree, and the successor nodes are generated, where the sub-schedules of the operations are fixed. Thus, a schedule is obtained at each node, and the sub-optimum solution is determined among the obtained schedules. Computational results on some 10 jobs and 10 machines problems and 15 jobs and 15 machines problems show that the proposed algorithm can find the sub-optimum solutions with a little computation time.     

A new Lagrangian Relaxation Algorithm for scheduling dissimilar parallel machines with release dates

In this article we investigate the parallel machine scheduling problem with job release dates, focusing on the case that machines are dissimilar with each other. The goal of scheduling is to find an assignment and sequence for a set of jobs so that the total weighted completion time is minimised. This type of production environment is frequently encountered in process industry, such as chemical and steel industries, where the scheduling of jobs with different purposes is an important goal. This article formulates the problem as an integer linear programming model. Because of the dissimilarity of machines, the ordinary job-based decomposition method is no longer applicable, a novel machine-based Lagrangian relaxation algorithm is therefore proposed. Penalty terms associated with violations of coupling constraints are introduced to the objective function by Lagrangian multipliers, which are updated using subgradient optimisation method. For each machine-level subproblem after decomposition, a forward dynamic programming algorithm is designed together with the weighted shortest processing time rule to provide an optimal solution. A heuristics is developed to obtain a feasible schedule from the solution of subproblems to provide an upper bound. Numerical results show that the new approach is computationally effective to handle the addressed problem and provide high quality schedules.     

Scheduling of manufacturing systems using the Lagrangian relaxationtechnique

The practical solutions for three manufacturing scheduling problems are examined. As each problem is formulated, constraints are added or modified to reflect increasing real world complexity. The first problem considers scheduling single-operation jobs on identical machines. The second problem is concerned with scheduling multiple-operation jobs with simple fork/join precedence constraints on identical machines. The third problem is the job shop problem in which multiple-operation jobs with general precedence constraints are scheduled on multiple machine types Langrangian relaxation is used to decompose each of the scheduling problems into job- or operation-level subproblems. The subproblems are easier to solve than the original problem and have intuitive appeal. This technique results in algorithms which generate near-optimal schedules efficiently, while giving a lower bound on the optimal cost. In resolving the scheduling problem from one time instant to the next, the Lagrange multipliers from the last schedule can be used to initialize the multipliers, further reducing the computation time     

A hybrid scheduling decision support model for minimizing job tardiness in a make-to-order based mould manufacturing environment

In the make-to-order (MTO) mode of manufacturing, the specification of each product is unique such that production processes vary from one product to another making the production schedule complex. In order to achieve high level productivity, the production flow is not arranged in sequence; instead, the job schedule of different production jobs is adjusted to fit in with the multiple-job shop environment. A poor scheduling of jobs leads to high production cost, long production time and tardiness in job performance. The existing of tardiness in the production schedule significantly affects the harmony among the multiple jobs on the shops floor. In order to provide a complete solution for solving MTO scheduling problems with job shifting and minimizing job tardiness, a hybrid scheduling decision support model (SDSM) is introduced. The model is combined by a Genetic Algorithm (GA) and an optimisation module. GA is adopted to solve the complex scheduling problem taking into consideration of the wide variety of processes while the optimisation module is suggested for tackling tardiness in doing the jobs in a cost effective way. The simulation results reveal that the model shortens the generation time of production schedules and reduces the production cost in MTO-based production projects.