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.     

Towards an Integration of Process Planning and Production Planning and Control for Flexible Manufacturing Systems

This introduction article attempts to present some major issues relating to the integration of process planning and production planning and control (PPC) for flexible manufacturing systems (FMSs). It shows that the performance of an FMS can be significantly improved and FMS capabilities more effectively utilized by integrating process planning and PPC functions. The various types of flexibility to be planned and provided for in process planning and manufacturing are summarized in the article, as well as emerging conceptual frameworks for integration, along with their implementation requirements and problems. Distinctive elements that differentiate these frameworks, such as the extent of integration of process planning and PPC activities, number of alternative process plans, and the time at which numerical control programs are generated, are discussed, followed by a brief summary of the articles compiled for this special issue.     

A Dynamic Tool Requirement Planning Model for Flexible Manufacturing Systems

Despite their strategic potential, tool management issues in flexible manufacturing systems (FMSs) have received little attention in the literature. Nonavailability of tools in FMSs cuts at the very root of the strategic goals for which such systems are designed. Specifically, the capability of FMSs to economically produce customized products (flexibility of scope) in varying batch sizes (flexibility of volume) and delivering them on an accelerated schedule (market response time) is seriously hampered when required tools are not available at the time needed. On the other hand, excess inventory of tools in such systems represents a significant cost due to the expensive nature of FMS tool inventory. This article constructs a dynamic tool requirement planning (DTRP) model for an FMS tool planning operation that allows dynamic determination of the optimal tool replenishments at the beginning of each arbitrary, managerially convenient, discrete time period. The analysis presented in the article consists of two distinct phases: In the first phase, tool demand distributions are obtained using information from manufacturing production plans (such as master production schedule (MPS) and material requirement plans (MRP)) and general tool life distributions fitted on actual time-to-failure data. Significant computational reductions are obtained if the tool failure data follow a Weibull or Gamma distribution. In the second phase, results from classical dynamic inventory models are modified to obtain optimal tool replenishment policies that permit compliance with such FMS-specific constraints as limited tool storage capacity and part/tool service levels. An implementation plan is included.     

A Review of Different Approaches to the FMS Loading Problem

Loading in flexible manufacturing systems (FMSs) is affected by the characteristics of the FMS under analysis, by the type of plant where the FMS is introduced, and by the production planning hierarchy where the loading module operates. We propose an analysis of the various aspects that influence the problem formulation, identifying the alternatives available in real systems and possible future evolutions. We then provide a survey of different approaches proposed in the literature to tackle the loading problem. Articles are classified according to the type of FMS analyzed, the objective function, and the constraints. Finally, based on our analysis, we suggest some problem issues which need to be addressed, and also directions for future research.     

A modular simulator for design,planning, and control of flexible manufacturing systems

Flexible manufacturing systems are designed to produce a variety of different part types with high machine utilisation, short lead times and little work-in-progress inventory. Simulation is an efficient tool to verify design concepts, to select machinery, to evaluate alternative configurations and to test system control strategies of an FMS. This paper discusses a general-purpose, user-oriented discrete simulator (the Modular FMS Simulator) which can be used for design as well as for operation and scheduling of FMSs. The package can be implemented in different hierarchical steps of FMS production planning. It is also a useful tool in validating the results of analytical models or heuristic procedures developed for FMS problems. This package contains features for the system hardware and the control hierarchy. The model can study multiple part families, various station types, different number of work-in-process buffers and carts and almost any system layout. It is also possible to analyse the performance of the system. The package contains a set of decision rules from which the user can make his choice.     

Reliability Optimisation of Flexible Manufacturing Systems with Spare Tooling

Tool reliability plays an important role in the performance and justification of flexible manufacturing systems (FMSs). Failure of a single tool can cause downtimes over the entire system. This would cause due dates to be missed and can result in inferior products. Therefore, in order to justify the large capital investment associated with FMSs, the system must perform in a reliable manner to give an acceptable or required rate of return on the investment. In order to arrive at this objective, FMS reliability must be studied at the planning and design stages, tool failures pose a major obstacle to achieving this objective. In this paper, a mathematical model has been developed to determine the spare tooling requirement for the tooling system in an FMS, so that a desired system reliability is achieved and the cost is minimised. The influence of tool sharing on cost, reliability, spares requirement, and tool magazine capacity of the FMS are analysed. The tools and tool transporter are subject to general failure distributions.     

Performance Analysis of an FMS Operating Under Different Failure Rates and Maintenance Policies

Flexible manufacturing Systems (FMSs) typically operate at 70–80% utilization, which is much higher than the utilization of traditional machines that can operate with as low as 20% utilization. A result is that an FMS may incur four times more wear and tear than a traditional system. This requests the execution of effective maintenance plans on FMSs. While maintenance actions can reduce the effects of breakdowns due to wear-outs, random failures are still unavoidable. It is important to understand the implications of a given maintenance plan on an FMS before its implementation. This paper discusses a procedure that combines simulation and analytical models to analyze the effects of corrective, preventive, and opportunistic maintenance policies on the performance of an FMS. The FMS performance is measured by its operational availability index, which is determined using the production output rate of the FMS under a variety of time between failure distributions and different operational conditions. The effects of various maintenance policies on FMS performance are simulated and the results are compared to determine the best policy for a given system.     

Object-oriented graphical modeling of FMSs

Presented in the article is a method for constructing a graphical model of an FMS by using a new modeling tool called JR-net (Job Resource relation-net). JR-net is an object-oriented graphical tool for modeling automated manufacturing systems (AMSs), such as FMSs, FASs, and AS/RSs. As with the object-oriented modeling paradigm of Rumbaugh et al. (1991), the JR-net modeling framework supports the three stages of models: static layout model (object model); job flow model (functional model); and supervisory control model (dynamic model). In this article, the existing JR-net structure (Park 1992, Han et al., 1995) is extended further to make it a graphical tool for FMS modeling. Using the extended JR-net, a step-by-step procedure for constructing a graphical model of FMSs is presented. Also addressed are issues of classifying FMSs in terms of their generic functions and of utilizing the JR-net model of FMSs.     

Quality control of an unreliable flexible manufacturing system with scrapping and infinite buffer capacity

Manufacturing multiple part types on a flexible manufacturing system (FMS) is increasingly becoming a rule rather than an exception. In such systems, attention has been drawn to the application of zero-defect technologies. However, in practice, this goal has remained elusive and costly. As a result, even though FMSs may be more reliable, producing fewer defective parts, system complexity and more stringent quality standards are rendering quality control in FMSs potentially useful. The goals of this article are threefold. First, we introduce a procedure for measuring and managing the in-process quality control of an FMS, which is described by an Open Queueing Network (OQN), bridging thereby a gap between queueing theory and quality control. Second, by focusing attention on the potential unreliabilities of FMSs, we provide some managerial insights regarding the role, position, and distribution of the quality control effort in an FMS. Finally, we stress the intricate relations between an FMS's operating characteristics and the manufactured quality and its control. Using numerical analyses, we draw some inferences regarding the design of such FMSs when both quality and quantity issues in the FMSs are considered. These simultaneous considerations of quantity and quality flows in an FMS have not been previously considered in the study of FMSs.     

Simulation for maintenance of an FMS: an integrated system of maintenance and decision-making

Flexible manufacturing systems (FMSs) are designed to produce a variety of different part types with high machine utilisation, so the maintenance technique is necessary and very important in an FMS. This paper discusses the maintenance problem in an FMS and its simulation, analyses the maintainability of a real FMS shop, and presents the architecture of an integrated system of maintenance and decision-making/scheduling for manufacturing shop control. The modelling and simulation of the maintenance activities in the shop are shown. The simulation program is written in SLAM II and the special subroutines are realised in Fortran. The interest is directed towards the use of the method of simulation for maintenance and decision-making in FMSs.     

An Analytical Queueing Network Model for Flexible Manufacturing Systems with a Discrete Handling Device and Transfer Blockings

In this paper, we study job shop-like flexible manufacturing systems (FMSs) with a discrete material handling system (MHS). In such FMSs, the MHS is a critical device, the unavailability of which may induce transfer blockings of the machines. The FMS devices therefore are hierarchically structured into primary and secondary devices to manage such blocking and avoid deadlocks in these FMSs.For evaluating the quantitative steady-state performance of such FMSs, we propose an analytical queueing network model that relies on an approximate method proposed for analyzing computer systems with simultaneous possessions of resources. Such a model is obtained using the concept of passive resources and by aggregating the FMS workload data so that models are much more tractable.The analytical results are validated against discrete event simulation and shown to be very encouraging. We also show how to increase their robustness, especially under light workload conditions, by modifying an assumption of the method concerning service time distributions.     

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.     

Object-oriented approach of MCTPN for modelling Flexible manufacturing systems

Manufacturing industry is facing a stricter challenge than ever before owing to the rapid change in market requirements. Flexible manufacturing systems (FMSs) have a much greater capability than traditional fixed-type production systems for coping with the rapid change. In this paper, a modified coloured-timed Petri net (MCTPN) is developed to model the dynamic activities in an FMS. The MCTPN provides an object-oriented and modular method of modelling manufacturing activities. It includes colour, time, modular and communication attributes. The features of object-oriented modelling allow the FMS to be modelled with the properties of classes, objects, and container trees. Since the system activities can be encapsulated and modularised by the proposed MCTPN, the manufacturing systems can be easily constructed and investigated by the system developers. It makes the concept of software IC possible for modelling complex FMSs. Once all of the MCTPN objects are well defined, the developers need to consider only the interfaces and operations relating to the MCTPN objects. In order to demonstrate the capability of the proposed MCTPN, the FMS in the Manufacturing Automation Technology Research Center (MATRC) of the National Taiwan University will be stimulated and justified by using the proposed MCTPN along with the G2 expert system.     

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.     

An efficient heuristic for scheduling batches of parts in a flexible flow system

Flexible manufacturing systems (FMSs) are a class of automated systems that can be used to improve productivity in batch manufacturing. Four stages of decision making have been defined for an FMS—the design, planning, scheduling, and control stages. This research focuses on the planning stage, and specifically in the area of scheduling batches of parts through the system.The literature to date on the FMS planning stage has mostly focused on the machine grouping, tool loading, and parttype selection problems. Our research carries the literature a step further by addressing the problem of scheduling batches of parts. Due to the use of serial-access material-handling systems in many FMSs, the batch-scheduling problem is modeled for a flexible flow system (FFS). This model explicitly accounts for setup times between batches that are dependent on their processing sequence.A heuristic procedure is developed for this batch-scheduling problem—the Maximum Savings (MS) heuristic. The MS heuristic is based upon the savings in time associated with a particular sequence and selecting the one with the maximum savings. It uses a two-phase method, with the savings being calculated in phase I, while a branch-and-bound procedure is employed to seek the best heuristic solution in phase II. Extensive computational results are provided for a wide variety of problems. The results show that the MS heuristic provides good-quality solutions.     

Design Guidelines for Deadlock-Handling Strategies in Flexible Manufacturing Systems

Deadlock-free operation of flexible manufacturing systems (FMSs) is an important goal of manufacturing systems control research. In this work, we develop the criteria that real-time FMS deadlock-handling strategies must satisfy. These criteria are based on a digraph representation of the FMS state space. Control policies for deadlock-free operation are characterized as partitioning cuts on this digraph. We call these structural control policies (SCPs) because, to avoid deadlock, they must guarantee certain structural properties of the subdigraph containing the empty state; namely, that it is strongly connected. A policy providing this guarantee is referred to as correct. Furthermore, an SCP must be configurable and scalable; that is, its correctness must not depend on configuration-specific system characteristics and it must remain computationally tractable as the FMS grows in size. Finally, an SCP must be efficient; that is, it must not overly constrain FMS operation. We formally develop and define these criteria, formulate guidelines for developing policies satisfying these criteria, and then provide an example SCP development using these guidelines. Finally, we present an SCP that guarantees deadlock-free buffer space allocation for FMSs with no route restrictions.     

Performance management in flexible manufacturing systems

This article treats several performance management decision problems in flexible manufacturing systems (FMSs). This work differs from a number of other studies in that we allow the processing rates at the machines to be varied, and the system has to meet a given throughput goal per unit time. The managerial decision options modeled here include part routing and allocation of tasks to machines, work-in-progress (WIP) levels, capacity expansions, tool-type selection, the setting of throughput goals, and multiperiod production planning. We discuss and explain the insights and implications, partly nonintuitive, gained from our investigations. Finally, extensive numerical evaluations are included to illustrate the economic and performance impact of the various performance management alternatives. These results demonstrate that substantial economic benefits can be achieved by careful tuning of the FMS operational parameters.     

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.     

A fuzzy integrated decision-making support system for scheduling of FMS using simulation

In the complex environment of an automated manufacturing system, decision-making is one of the most important and difficult tasks of a manager in general or a scheduler in particular. Flexible Manufacturing Systems (FMSs) are highly automated and expensive systems. The high investment cost of a FMS justifies the use of computer simulation support. This paper investigates the operational problems of FMSs through simulation, and different combinations of scheduling rules are evaluated by a fuzzy integrated decision-making support system.