基于工作分解结构的跨企业项目多级网络计划

为解决跨企业项目多级计划的一致性问题,提出了基于工作分解结构的跨企业项目多级网络计划方法.首先,从跨企业项目计划特点出发,给出了跨企业项目工作分解结构的分解规则与方法;然后,在考虑工作分解结构与网络图工序之间对应关系的基础上,提出了基于工作分解结构的跨企业项目多级网络计划生成模型、方法和策略;最后,给出了应用实例,说明了该构建方法的实际应用.     

A model,language design and experiments for man/machine communication in computer aided manipulation

A hierarchical model based on the concept of procedural nets was developed to represent the planning process of manipulator action. The model was adapted to represent the man/machine communication process as a step in this planning process. The human operator develops the global task given to him into a plan—a sequence of subtasks—at some intermediate level of details which he communicates to the computer. The computer develops the plan further to the level of detail necessary for controlling the manipulator and proceeds to monitor the plan execution via position and force-sensor feedback. The language developed from this model is hierarchical, flexible, task-oriented and allows close cooperation between man and machine with a mixed initiative protocol of control allocation. The language is used in experiments which test the impact of language features on man/machine system performance.     

面向柔性焊装线的计划管理系统研究

为了有效地制定适合柔性焊装线的车间生产计划以及掌握计划执行的实时数据信息,提出了一种面向柔性焊装线的计划管理系统。该系统利用产品结构、工艺数据以及车间生产能力数据,通过多数据耦合机制,制定柔性焊装线的生产计划,并通过OPC、N-Ring环网总线等技术,构建实时数据采集与信息交互平台,实现车间计划信息的集成化管理,为现场操作工提供实时的工作状况信息,同时也为上层ERP生产任务的制定提供数据基础。     

Multiple-Objective Scheduling for the Hierarchical Control of Flexible Manufacturing Systems

This paper presents a hierarchical approach to scheduling flexible manufacturing systems (FMSs) that pursues multiple performance objectives and considers the process flexibility of incorporating alternative process plans and resources for the required operations. The scheduling problem is solved at two levels: the shop level and the manufacturing system level. The shop level controller employs a combined priority index developed in this research to rank shop production orders in meeting multiple scheduling objectives. To overcome dimensional complexity and keep a low level of work-in-process inventory, the shop controller first selects up to three production orders with the highest ranking as candidates and generates all possible release sequences for them, with or without multitasking. These sequences are conveyed to the manufacturing system controller, who then performs detailed scheduling of the machines in the FMS using a fixed priority heuristic for routing parts of multiple types while considering alternative process plans and resources for the operations. The FMS controller provides feedback to the shop controller with a set of suggested detailed schedules and projected order completion times. On receiving these results, the shop controller further evaluates each candidate schedule using a multiple-objective function and selects the best schedule for execution. This allows multiple performance objectives of an FMS to be achieved by the integrated hierarchical scheduling approach.     

Dynamic process planning control of hybrid disassembly systems

Disassembly plays a key role in a life cycle economy, since it enables the recovery of resources, as reported by Seliger (Sustainability in manufacturing: recovery of resources in product and material cycles. Springer, Berlin, ISBN 978-3-540-49870-4; 2007). A partly automated disassembly system could adapt to a large variety of products and different degrees of devaluation. However, the deviations between the disassembly plan generated by the planning process and the actual situation of the disassembly system cause difficulties in the workshop execution. Controlling these deviations is necessary for regulating daily operations. The objective of dynamic process planning (DPP) is to reflect the availabilities of the disassembly tools in the real disassembly cell and to offer alternatives when a device or tool is not available. The DPP procedure will propose alternative disassembly processes and tools supported by a knowledge-based network method and it will select the best suitable among them.     

A specification language for planning and fault recovery in manufacturing systems

This paper presents the syntax and semantics of a component-oriented rule-based language for specifying the formal models of manufacturing systems. A model captures the state of a component of the system in a set of first-order logic predicates, and it captures the semantics of the operations performed by this component in a set of rules that determine the preconditions and postconditions of an operation. The models are then used to plan the sequence of operations of each class of jobs to be manufactured by these systems. A plan-oriented fault detection and correction strategy is proposed. This strategy can automatically handle any combination of faults that may occur when monitoring the operations of manufacturing systems. A fault-tree is consulted prior to executing the scheduled operations of a plan, and the faults that affect the execution of these operations are handled subsequently. Resuming the original cyclic schedule is attempted, whenever feasible. As a proof of concept, a prototype implementation of both the main constructs of the component-oriented rule-based language and the planning and fault-recovery algorithms presented in this paper have been completed. This prototype is implemented on a Unix-based system in the Ada programming language. The specification of a manufacturing system is first expressed in the proposed language. These statements are then translated into Ada code. This code is next compiled by a Verdix Ada compiler and is executed in order to create and populate the model data structure of the system. A detailed plan of execution and a set of fault-recovery plans may then be derived for a job to be manufactured on this system.     

一种面向航空产品的分级网络计划方法

航空产品项目计划编制的传统方法是人工绘制单层甘特网络计划，这种方法因信息不完整、修改困难、无法插入文档流等问题，不能满足现代航空产品项目管理的需求。为此，提出了一种面向航空产品项目的分级网络计划模型，将文档流与控制流分离，实现了文档流的插入与更改。给出了建立分级网络计划的方法，分析了文档流与控制流的相容性，并计算了分级网络计划的时间参数。最后，以某型飞机机翼研制项目为应用对象，验证了该方法的有效性。     

Application of an efficient modified particle swarm optimization algorithm for process planning

In the modern manufacturing system, many flexible manufacturing system and NC machines are introduced to improve the production efficiency. Therefore, most parts have a large number of flexible process plans. However, a part can use only one process plan in the manufacturing process. So, the process planning problem has become a crucial problem in the manufacturing environment. It is a combinatorial optimization problem to conduct operations selection and operations sequencing simultaneously with various constraints deriving from the practical workshop environment as well as the parts to be processed. It is a NP-hard problem. In order to solve this problem effectively, this paper proposes a novel modified particle swarm optimization (PSO) algorithm to optimize the process planning problem. To improve the performance of the approach, efficient encoding, updating, and random search methods have been developed. To verify the feasibility and effectiveness of the proposed approach, seven cases have been conducted. The proposed algorithm has also been compared with the genetic algorithm and simulated annealing algorithm. The results show that the proposed modified PSO algorithm can generate satisfactory solutions and outperform other algorithms.     

Integrated setup planning and operation sequencing (ISOS) using genetic algorithm

Process planning is an essential component for linking design and manufacturing process. Setup planning and operation sequencing are two most important functions in the implementation of CAD/CAPP/CAM integration. Many researches solved these two problems separately. Considering the fact that the two functions are complementary, it is necessary to integrate them more tightly so that performance of a manufacturing system can be improved economically and competitively. This paper presents a generative system and genetic algorithm (GA) approach to process plan the given part. The proposed approach and optimization methodology analysis constraints such as TAD?(tool?approach?direction), tolerance relation between features and feature precedence relations to generate all possible setups and operations using workshop resource database. Tolerance relation analysis has a significant impact in setup planning for obtaining the part accuracy. Based on technological constraints, the GA algorithm approach, which adopts the feature-based representation, simultaneously optimizes the setup plan and sequence of operations using cost indices. Case studies show that the developed system can generate satisfactory results in optimizing the integrated setup planning and operation sequencing in feasible condition.     

Integrated optimization of production planning and scheduling for a kind of job-shop

This paper addresses an integrated job-shop production planning and scheduling problem with setup time and batches. It not only considers the setup cost, work-in-process inventory, product demand, and the load of equipment, but also the detailed scheduling constraints. That is a way different from the traditional hierarchical production planning method. The hierarchical methods do not consider the detailed scheduling constraints, so it cannot guarantee to obtain a feasible production plan. Here the integrated problem is formulated as a nonlinear mixed integer program model. And in order to simultaneously optimize the production plan and the schedule, an improved hybrid genetic algorithm (HGA) is given. In the model, the detailed scheduling constraints are used to compute the accurate load of a device in order to obtain a feasible production plan. The heuristic scheduling rules such as the shortest processing time (SPT) and the longest processing time (LPT) are used to generate a better initial solution. Also, a subsection coding strategy is offered to convert the planning and scheduling solution into a chromosome. At last, a comparison is made between the hybrid algorithm and a hierarchical production planning and scheduling method, showing that the hybrid algorithm can solve the problem effectively.     

基于双反馈控制和分阶段调度的光纤生产调度

针对光纤生产中生产计划执行过程存在的生产定单产出数量不精确、完成时间难以控制等问题进行分析,提出建立基于双反馈控制和分阶段的生产调度系统对生产定单生产进行控制。该生产调度系统在光纤生产流程中设置三个反馈点：拉丝产出、筛选产出和入库,分别负责反馈生产流程各部分的生产情况,实时获得生产定单生产情况变动信息,及时掌握生产动态。通过对这些信息的处理并将其与计划相比较从而得出差异,形成调整指令,实时地控制原材料的投入量,提高生产定单产品生产数量的精确度;根据光纤生产特点将调度过程划分为两个阶段,采用不同的调度方法对定单生产进行调度,并在第二阶段引入台阶调度法,以牺牲部分控制精度的代价提高调度效率,缩短生产定单结束阶段的生产时间。案例证明所提方法的有效性。     

基于变压器产品的动态过程管理系统开发与应用

变压器企业属于单件小批量离散制造业,产品多为定制产品,订单随机性强、不确定因素较多。产品在管理过程中存在产品设计信息的统一和共享困难,项目计划进度无法实现过程控制,无法快速准确的调整计划,在生产制造阶段,经常出现插单,生产计划时常变更,管理人员制订生产计划主观性强,车间计划执行有资源冲突现象。因此,产品研发设计过程和经营管理过程中要适时动态管理,以此提高过程管理的适应性。将项目管理、工作流与生产计划调度结合,提出了基于变压器产品的动态过程管理系统体系架构,并结合变压器企业具体应用,采用JAVA语言和JSP技术开发了动态过程管理系统,增强了企业过程管理的灵活性、准确性。     

Database model for hierarchical control of manufacturing cell systems in a manufacturing shop

To access a large amount of static and dynamic data sets in achieving flexible manufacturing system (FMS) control functions, a corresponding database structure must be designed to effectively support the control. Within the context of a hierarchical manufacturing control architecture, this paper discusses the development of a semantic database structure for the control needs of manufacturing cell systems in a shop. A conceptual data model is first developed using entity-relationship diagrams (ERD), and the database is then implemented from a relational data model by a database management system. An application program is used to illustrate the production control command decomposition in a manufacturing shop that consists of several manufacturing cells. Feedback of control commands execution is also discussed. It is shown that the data flows in the hierarchical manufacturing control can be effectively supported by the developed databases and query interface.     

Optimization of flexible process planning by genetic programming

The traditional manufacturing system research literature generally assumed that there was only one feasible process plan for each job. This implied that there was no flexibility considered in the process plan. But, in the modern manufacturing system, most jobs may have a large number of flexible process plans. So, flexible process plans selection in a manufacturing environment has become a crucial problem. In this paper, a new method using an evolutionary algorithm, called genetic programming (GP), is presented to optimize flexible process planning. The flexible process plans and the mathematical model of flexible process planning have been described, and a network representation is adopted to describe the flexibility of process plans. To satisfy GP, it is very important to convert the network to a tree. The efficient genetic representations and operator schemes also have been considered. Case studies have been used to test the algorithm, and the comparison has been made for this approach and genetic algorithm (GA), which is another popular evolutionary approach to indicate the adaptability and superiority of the GP-based approach. The experimental results show that the proposed method ispromising and very effective in the optimization research of flexible process planning.     

An image acquisition system for real-time seam tracking

Process planning is a function that establishes the technological requirements necessary to convert a part from its raw material to the finished form. Generally, the result of process planning is delivered to the workshop to guide the manufacturing process in the form of process plan. However, a part always has multi alternative process plans for the processing means and techniques are not unique, therefore, optimization and selection of process plans is an important task of flexible process planning. In this paper, the flexibility of process planning and the AND/OR network adopted to represent the flexibility of process plans were described, and a mathematical model for the optimization of flexible process planning based on the AND/OR network was established. On this basis, a new heuristic method, called cross-entropy (CE) approach, was proposed to optimize flexible process planning. In order to facilitate the implementation of the CE-based approach, the new sample representation and probability distribution parameter were introduced; meanwhile, the new sample generation mechanism was presented and the updating expression of probability distribution parameter was deduced. Case studies, used for comparing this approach with genetic algorithm (GA) and genetic programming (GP)-based approach, were discussed to indicate the performance and adaptability of the proposed CE-based approach in terms of the solution quality and computational efficiency of the algorithm. The results show that the CE-based approach is effective for the optimization research of flexible process planning.     

基于MES的车间生产能力平衡问题的研究

针对车间生产能力分配不均衡的问题,分析了基于制造执行系统MES的车间任务分配与控制结构,根据不同工作中心的生产能力建立生产能力矩阵,同时根据制造件能力需求统计建立制造能力需求矩阵,基于此建立了车间生产能力需求计划与实际生产能力平衡分析模型,建立计算生产能力偏差率的数学模型,用模型分析生产能力需求和实际生产能力的协调性,分析了离散制造车间生产能力分配中的不确定性因素,最后给出了模型在车间计划生成管理与能力平衡管理中的应用实例。     

Production planning of a flexible manufacturing system in a material requirements planning environment

Early flexible manufacturing system (FMS) production planning models exhibited a variety of planning objectives; typically, these objectives were independent of the overall production environment. More recently, some researchers have proposed hierarchical production planning and scheduling models for FMS. In this article, we examine production planning of FMS in a material requirements planning (MRP) environment. We propose a hierarchical structure that integrates FMS production planning into a closed-loop MRP system. This structure gives rise to the FMS/MRP rough-cut capacity planning (FMRCP) problem, the FMS/MRP grouping and loading (FMGL) problem, and the FMS/MRP detailed scheduling problem.We examine the FMRCP and FMGL problems in detail and present mathematical programming models for each of these problems. In particular, the FMRCP problem is modeled as a generalized assignment problem (GAP), and a GAP-based heuristic procedure is defined for the problem. We define a two-phase heuristic for the FMGL problem and present computational experience with both heuristics. The FMRCP heuristic is shown to solve problems that exhibit a dependent-demand relation within the FMS and with FMS capacity utilization as high as 99 percent. The FMGL heuristic requires very little CPU time and obtains solutions to the test problems that are on average within 1.5 percent of a theoretical lower bound.This FMS/MRP production planning framework, together with the resulting models, constitutes an important step in the integration of FMS technology with MRP production planning. The hierarchical planning mechanism directly provides for system-level MRP planning priorities to induce appropriate production planning and control objectives on the FMS while simultaneously allowing for necessary feedback from the FMS. Moreover, by demonstrating the tractability of the FMRCP and FMGL problems, this research establishes the necessary groundwork upon which to explore systemwide issues pertaining to the coordination of the hierarchical structure.     

Sheet metal cutting and piercing operations planning and tools configuration by an expert system

This paper describes the development of an expert system for both process planning and die design of sheet metal cutting and piercing operations. Knowledge for the system is acquired from practical handbooks, theory and empirical knowledge from industrial partners and is expressed as “crude” rules, transformed by the knowledge engineer into production rules. These are then coded into constructs of the CLIPS expert system development environment. This process is explained using a number of examples. The expert system comprises of five modules: initial calculations (including data input), process planning, finishing operation, die and press selection, and tools selection. Each module draws upon one or more among 18 rule pools defined around the important notions characterising the problem studied. The results of the expert system can be used by both the designer deciding on the production feasibility (manufacturability) for a sheet metal component and by the manufacturer receiving advice either on the hardware needed or on the process plan.     

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.     

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。