满足产品需求条件下的车间最优随机生产计划与控制

根据实际需要建立关联方程有延迟且以正好满足产品需求为约束条件的车间生产计划与控制的随机非线性规划模型，即一种求解动态优化问题的静态优化模型，为求解方便将其转化成线性规划模型。提出分别用卡马卡算法和基于卡马卡算法的关联预测法来求解柔性自动化车间（FAM）最优随机生产计划与控制问题，并编制了相应软件。通过算例研究，比较了上述2种方法和Matlab中的线性规划法，结果表明所提方法非常适合将不确定性环境中的FAW产品需求计划最优分解成由FAW中各柔性制造系统（FMS）执行的短期随机计划，尤其适合FMS之间工件传输需经出入库并有1个生产周期延迟的情况。     

Hierarchical Production Planning in Flexible Automated Workshops with Delay Interaction

This paper addresses the hierarchical production planning (HPP) problem for flexible automated workshops (FAWs) with delay interaction, each with a number of flexible manufacturing systems (FMSs). The delay interaction aspect arises from taking into consideration the transfer of parts between FMSs. Any job which requires processing on more than one FMS cannot be transferred directly from one FMS to the next. Instead a semi-finished-product completed in one period must be put into shop storage until the next period at which it can be transferred to the next FMS for further processing. The objective is to decompose medium-term plans (assigned to an FAW by ERP/MRP II) into short-term plans (to be executed by FMSs in the FAW) so as to obtain the lowest production cost. The HPP problem is formulated in this paper by a nonlinear programming model whose constraints are linear but whose objective function is piecewise linear. For the convenience of solving the nonlinear programming model, it is transformed into a linear programming model. Because the model for a general workshop is too large to be solved by the simplex method on a personal computer within acceptable time, Karmarkar’s algorithm and an interaction/prediction algorithm, respectively, are used to solve the model, the former for medium- or small-scale problems and the latter for large-scale problems. With the implementations of these algorithms and with many HPP examples, Karmarkar’s algorithm, the interaction/prediction algorithm and the linear programming method in Matlab 5.0 are compared, showing that the proposed approaches are very effective.     

柔性自动化车间的最优随机生产计划

研究了由多个柔性制造系统组成的柔性自动化车间的最优随机生产计划问题,首先根据实际需要建立车间生产计划的随机非线性规划模型,为求解方便,将其近似转化成确定非线性规划模型,并通过引进约束进一步转化成线性规划模型。由于这种模型规模较大,很难在微机上用单纯形法在可接受的时间内获得其最优解。为此,分别用卡马卡算法和基于卡马卡算法的关联预测法,求解柔性自动化车间最优生产计划问题,并编制了相应软件。最后通过算例研究,比较了卡马卡算法、基于卡马卡算法的关联预测法和Matlab中的线性规划法,结果表明,所提方法非常适合将不确定性环境中的随机产品需求计划,最优分解成由柔性自动化车间中各柔性制造系统执行的短期随机计划。     

Near optimal manufacturing flow controller design

Flow control of flexible manufacturing systems (FMSs) addresses an important real-time scheduling requirement of modern manufacturing facilities, which are prone to failures and other controllable or stochastic discrete events affecting production capacity, such as change of setup and maintenance scheduling. Flow controllers are useful both in the coordination of interconnected flexible manufacturing cells through distributed scheduling policies and in the hierarchical decomposition of the planning and scheduling problem of complex manufacturing systems. Optimal flow-control policies are hedging-point policies characterized by a generally intractable system of stochastic partial differential equations. This article proposes a near optimal controller whose design is computationally feasible for realistic-size systems. The design exploits a decomposition of the multiple-part-type problem to many analytically tractable one-part-type problems. The decomposition is achieved by replacing the polyhedra production capacity sets with inscribed hypercubes. Stationary marginal densities of state variables are computed iteratively for successive trial controller designs until the best inscribed hypercubes and the associated optimal hedging points are determined. Computational results are presented for an illustrative example of a failureprone FMS.     

快速重组制造系统的动态建模和结构体系

提出了基于组态式柔性制造单元组成的阵列式布局的快速重组制造一系统的创新结构 体系,建立了以生产效益为该系统设计和运行决策目标的随机模型,应用排队网络模型静态马洋科夫工件运送方式得出系统重组时缓冲区容量的优化配置和最优工件运送策略的算法,     

基于多agent的敏捷生产调度系统模型的研究

针对敏捷MES中的生产管理特点,提出了基于多agent的敏捷生产调度系统模型,详细介绍了系统的体系结构和各个agent的功能特点,并对其运行机制和agent间的信息交互进行了分析,为企业实现制造的柔性、对动态环境的适应性和快速响应性提供了依据。     

Heuristic solution approaches for combined-job sequencing and machine loading problem in flexible manufacturing systems

Job sequencing and machine loading are two vital and interrelated production planning problems in flexible manufacturing systems (FMSs). In this research, attempts have been made to address the combined job sequencing and machine loading problem using minimization of system unbalance and maximization of throughput as objective functions, while satisfying the constraints related to available machining time and tool slots. This research describes two heuristics to deal with the problems. Heuristic I uses predetermined fixed job sequencing rules as inputs for operation allocation decision on machines, whereas heuristic II uses genetic algorithm based approach for simultaneously addressing job sequences and operation machine allocation issues. Performance of these heuristics has been tested on problems representing three different FMS scenarios. Heuristic II (Genetic algorithm based) has been found more efficient and outperformed heuristic I in terms of solution quality.     

一种基于Petri网的顺序共享资源AMS＊s

Ｐｅｔｒｉ网控制器自动设计及控制程序自动生成方法的研究是自动制造系统ＡＭＳｓ和柔性制造系统ＦＭＳｓ领域研究的热点问题之一〔２～６〕。本文以ＡＭＳｓ计划和调度产生的生产序列ＰＳ和资源需求序列ＲＲＳ为基础，为顺序共资源ＡＭＳｓ设计了一种规格说明语言，以自动生成ＡＭＳｓＰｅｔｒｉ网模型的矩阵形式，并给出了转换步骤。由于引入了资源状态反馈，故生成的Ｐｅｔｒｉ网模型是无死锁的。最后，用一个实例说明了设计过程。     

Dynamic control of flexible manufacturing systems

Flexible manufacturing systems (FMSs) are a relatively new technological and organisational approach to helping companies respond to real-time marketing conditions for their production. Under a proposal of the National Bureau of Standards the FMSs are subdivided into virtual manufacturing cells in a dynamic manner, on the basis of group technology.A method of dynamic optimisation for the design of manufacturing processes, capacity balancing and checking, and also production scheduling or rescheduling in virtual manufacturing cells is described. It can be used during real-time production control in FMSs.     

The State of the Art in Simulation Study on FMS Scheduling: A Comprehensive Survey

Scheduling of flexible manufacturing systems (FMSs) has been one of the most attractive areas for both researchers and practitioners. A considerable body of literature has accumulated in this area since the late 1970s when the first batch of papers was published. A number of approaches have been adopted to schedule FMSs, including simulation techniques and analytical methods. Numerous articles can be found on each of these approaches. This paper reviews scheduling studies of FMSs which employ simulation techniques as an analysis tool, since simulation is the most widely used tool for modelling FMSs. Scheduling methodologies are categorised into simulation of general scheduling studies, multi-criteria scheduling approaches, and artificial intelligence (AI) approaches in FMSs. Comments on the publications, and suggestions for further research and development are given.