Production Capacity Modeling of Alternative, Nonidentical, Flexible Machines

Analyzing the production capacity of a flexible manufacturing system consisting of a number of alternative, nonidentical, flexible machines, where each machine is capable of producing several different part types simultaneously (by flexibly allocating its production capacity among these part types), is not a trivial task. The production capacity set of such a system is naturally expressed in terms of the machine-specific production rates of all part types. In this paper we also express it in terms of the total production rates of all part types over all machines. More specifically, we express the capacity set as the convex hull of a set of points corresponding to all possible assignments of machines to part types, where in each assignment each machine allocates all its capacity to only one part type. First, we show that within each subset of assignments having a given number of machines assigned to each part type, there is a unique assignment that corresponds to an extreme point of the capacity set. Then, we propose a procedure for generating all the extreme points and facets of the capacity set. Numerical experience shows that when the number of part types is less than four, the size of the capacity set (measured in terms of the number of variables times the number of constraints) is smaller, if the capacity set is expressed in terms of the total production rates of all part types over all machines than if it is expressed in terms of the machine-specific production rates of all part types. When the number of part types is four or more, however, the opposite is true.     

An Efficient Heuristic Approach to Recognize the Infeasibility of a Loading Problem

The success of hierarchical production planning approaches for flexible manufacturing systems lies in the consistency of decision outcomes at various decision levels. For instance, the loading problem, which is solved at a lower level, may not yield a feasible loading solution to a set of part types selected at a higher level. This paper attemps to address the issue of recognizing the infeasibility of a loading solution. We present a modified loading model that includes a penalty for each operation not assigned to any machine. We develop a Lagrangian-based heuristic procedure and provide a sufficient condition on the quality of heuristic solutions that, if satisfied, will enable us to use the heuristic solutions to recognize the infeasibility of a loading problem. The proposed model and the dual-based heuristic can be effectively incorporated in an FMS hierarchical production planning approach that finds a good loading solution by iteratively comparing different part grouping scenarios.     

Development of a Robotic System which Assists Unmanned Production Based on Cooperation between Off-Line Robots and On-Line Robots: Concept, Analysis and Related Technology

In order to deal with labour shortages due to the predicted low birth rate in the 21st century, and create a more friendly production system, we propose a robotic system which assists production in flexible manufacturing environments. In this robotic system off-line robots are proposed to replace or reduce the need for human workers at the shop-floor. The off-line robots carry out assisting or service tasks for the on-line robots which perform manufacturing tasks at the production line or site. In this paper, first, an analysis of a conventional automatic production system using robots is described. Secondly, the concept of a robotic production system using two types of robots is proposed. Thirdly, a methodology for the realisation of the proposed concept and some technical results are presented. Finally, fault recovery using off-line robots and peripheral technology are discussed.     

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.     

基于试制白车身生产线柔性能力改造研究

为解决试制白车身生产线实现两种车型白车身的共线生产问题,展开对该生产线柔性能力的分析研究,通过改造优化白车身BOP制造工艺,调整各焊合零件的工艺流向,调整焊接设备和拼装夹具的布置,提高焊钳的通用性,提高夹具平台柔性化,实现生产场地利用率最大化,生产设备利用率最大化,最大限度发挥白车身生产线的柔性生产能力,实现最大化的低成本样车制造工作。     

Clustering algorithms to optimize the tool handling system management in an FMS

Tool management is recognized as a critical issue in flexible manufacturing facilities management. This article addresses the issue of tool management in a flexible system installed in an avionics components factory. The system is composed of two machining centers equipped with local tool magazines of limited capacity. A tool handling system is in charge of tool movements between the tool room and the two machines. Each machine is able to perform any operation, provided that it is equipped with the suitable tool. In this kind of installation, tool allocation must be determined, and tool movements must be synchronized in order to minimize operating costs, or, equivalently, maximize the productivity of the system. We propose an approach to production planning based on a clustering algorithm, which takes into account the tool requirements of each part program in the production batch. We also propose two different heuristics for the scheduling problem. A case study was conducted on the facility mentioned above. Two conflicting objectives can be identified for this kind of production system: the reduction of tools to be shared among machines and the reduction of workload unbalance. The tests and comparison made demonstrate how the proposed procedure leads to superior results in terms of both objectives.     

Rule-based production planning in flexible manufacturing systems

Production planning in flexible manufacturing may require the solution of a large-scale discrete-event dynamic stochastic optimization problem, due to the complexity of the system to be optimized, and to the occurrence of discrete events (new orders and hard failures). The production planning problem is here approached for a multistage multipart-type manufacturing shop, where each work cell can share its processing time among the different types of parts. The solution of this problem is obtained by an open-loop-feedback control strategy, updated each time a new event occurs. At each event time, two coupled problems are solved: 1) a product-order scheduling problem, conditioned on estimated values of the production capacities of all component work cells; and 2) a production-capacity planning problem, conditioned on predefined sequences of the product orders to be processed. In particular, the article aims at defining a production planning procedure that integrates both analytical tools, derived from mathematical programming, and knowledge-based rules, coming from experience. The objective is to formulate a hybrid (knowledge-based/analytical) planning architecture, and to analyze its use for multicell multipart-type manufacturing systems.     

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。     

Supervisory Control of Functionally-Expandable Manufacturing Systems

Supervisory controllers have traditionally coordinated the various resources of manufacturing systems, such as flexible manufacturing workcells, for the production of a priori set families of part types. This paper expands on this capability by allowing the control of the production of new part types side by side with previously defined and planned for nominal part types. The proposed basic workcell supervisory-control approach advocates the use of a pair of non-communicating independent supervisors, synthesized individually but working in concert, to achieve the production of existing and new part types: nominal and complementary supervisors, respectively. The nominal supervisor is responsible for controlling the behavior of the nominal system, producing the set of a priori planned-for part types, whereas the complementary supervisor controls the flow of the a priori unplanned-for new part types.     

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.     

Models for capacity acquisition decisions considering operational costs

In this article, we study a capacity acquisition problem by considering technology choice and operational factors in a stochastic environment. The motivation for our work comes from developments in modern flexible technologies and a problem encountered in a real industrial setting. We study the impact of operational factors such as setup times, demand patterns, and inventory/back order costs on the decisions of capacity acquisition and technology choice. We consider three alternatives in capacity and technology decisions: (i) a flexible system, (ii) a dedicated system, and (iii) a combination of these two systems. For each system, we develop a model that integrates investment decisions and operational decisions to determine an optimal amount of capacity to purchase and the time and the types of parts to produce. The objective is to minimize the capacity acquisition cost at the beginning of the planning horizon and the total expected operational costs over an infinite planning horizon. To solve the problem in this article, a solution procedure is proposed. Managerial insights are also derived from extensive computational results.     

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。     

Availability Analysis for Multitask Production Systems

A multitask production system is one in which different subsets of the components can be used to perform distinct functions or tasks. For such a system, some of the components are used intermittently and some may be used continuously. This type of operational protocol is often applied to flexible manufacturing systems. In this paper, we develop models of the operational reliability and of the availability of multitask systems. We construct models for both the case in which preventive maintenance is used and the case in which preventive maintenance is not used. The models presented here constitute an extension to existing models to allow the study of the behavior of modern manufacturing equipment.     

离散型智能电装生产线建设研究及实践

文中根据企业离散型制造、生产对象复杂程度高、品种多样化、生产批量差异大的特点,结合智能制造系统典型特征,通过硬件和软件并行建设,建立全自动生产、人机协作生产和单工位智能装配3类生产模式,实现生产敏捷化。将制造信息转化为可度量的数字、数据和模型,利用新一代信息技术解决制造系统中各种复杂的优化决策问题等,实现设备互联互通和高度集成、产品状态的敏捷感知、实时数据分析、自动决策和精准执行命令,从而建立以“人机协作、数字赋能、智慧管理”为特征的电装智能生产线。     

Reliability analysis of flexible manufacturing systems

High productivity is the primary goal of flexible manufacturing systems (FMSs) in which semi-independent workstations are integrated using automated material-transport systems and hierarchical local networks. Availability of various subsystems and of the system as a whole is a prerequisite for achieving functional integration as well as high throughput. An FMS also has inherent routing and operation flexibilities that provide it with a certain degree of fault tolerance. A certain volume of production can thus be maintained in the face of subsystem (i.e., machines, robots, material handling system, etc.) failures. In this article, we propose two reliability measures, namely, part reliability (PR) and FMS reliability (FMSR) for manufacturing systems and present algorithms to evaluate them. We also consider the dynamic or time-dependent reliability analysis as a natural generalization of the static analysis. The methods outlined use an algorithm that generates process-spanning graphs (PSGs), which are used to evaluate the reliability measures.     

基于RGV轨导柔性线环境离散型零件生产过程管理与实践

为满足航空中小型结构件快速交付的需要,以提升航空中小型结构件制造高精度、自动化和高效率为目标,通过对航空中小型结构件加工现状进行梳理分析,搭建柔性生产线自动排程与调度平台,实现柔性生产线工艺设计与准备过程管理,简化工艺准备流程,推进快换工装的应用并进行自动化程序管理;在柔性生产线加工运行过程中采用可视化管理方法,推动自动化环境下加工过程资源保障集中化管理。最终使该管理方法在某项目中进行成功实践,取得了良好的经济效益。     

车间生产系统及设备布局

分析了车间生产系统的类型与特点、设备布局的方式与相关技术。以钢圈生产线为例,介绍了根据零件特点和生产任务要求进行设备布局与物料搬运系统设计的方法。     

Flexible versus dedicated technology: A capacity expansion model

Over the past two decades, flexible manufacturing systems have been adopted in a variety of industries. Shorter product lives have necessitated acquiring flexible facilities. Further, the economies of scope that can be derived from producing products with different seasonality have made flexible facilities attractive. At the same time, dedicated facilities (such as transfer lines) have not disappeared because they offer economies of scale beneficial in high volume manufacturing environments. Hence, when producing a wide variety of products with different seasonality and demand growth rates, firms need to consider both economies of scale and economies of scope in deciding whether to acquire capacity of flexible or dedicated technology or both. In this paper, we consider the problem of making capacity acquisition decisions, when there is a choice of flexible and dedicated technologies available in a deterministic multi-product manufacturing environment with demand assumed to be nondeclining over time. We present a novel formulation of the problem and show the equivalence of the formulation to the uncapacitated plant location formulation. This enables us to use a very efficient solution procedure (Erlenkotter 1978) to solve even large problems optimally. We then present the results of an extensive computational study performed to evaluate the impact of key problem parameters on the proportion of flexible and dedicated capacity acquired. An interesting result of the study is that the proportion of flexible capacity acquired decreases with increasing scale economies. We also find that investment in flexible technology is significant even when it is relatively more expensive than dedicated technology.     

The Application and Evaluation of Banker''s Algorithm for Deadlock-Free Buffer Space Allocation in Flexible Manufacturing Systems

Deadlock-free operation is essential for operating highly automated manufacturing systems. The seminal deadlock avoidance procedure, Banker's algorithm, was developed for computer operating systems, an environment where very little information regarding the future resource requirements of executing processes is known. Manufacturing researchers have tended to dismiss Banker's algorithm as too conservative in the manufacturing environment where future resource requirements are well defined by part routes. In this work, we investigate this issue by developing variants of Banker's algorithm applicable to buffer space allocation in flexible manufacturing. We show that these algorithms are not overly conservative and that, indeed, Banker's approach can provide very good operational flexibility when properly applied to the manufacturing environment.     

Resource-requirements planning in flexible manufacturing systems

In this paper, we address the problem of planning the requirements of some of the important resources in flexible manufacturing systems. Specifically, we model the problem of estimating the required numbers and types of machines and tools in the context of a cellular layout. A two-stage procedure is developed which first forms the part families, using the complete-linkage clustering method based on a new similarity index defined in terms of the tooling requirements, and then subsequently estimates the resource requirements to manufacture the part families using an integer programming model. Several variations of the model are discussed and a numerical example is given.