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.     

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.     

simBio: a Java package for the development of detailed cell models

Quantitative dynamic computer models, which integrate a variety of molecular functions into a cell model, provide a powerful tool to create and test working hypotheses. We have developed a new modeling tool, the simBio package (freely available from http://www.sim-bio.org/), which can be used for constructing cell models, such as cardiac cells (the Kyoto model from Matsuoka et al., 2003, 2004a, b, the LRd model from Faber and Rudy, 2000, and the Noble 98 model from Noble et al., 1998), epithelial cells (Strieter et al., 1990) and pancreatic β cells (Magnus and Keizer, 1998). The simBio package is written in Java, uses XML and can solve ordinary differential equations. In an attempt to mimic biological functional structures, a cell model is, in simBio, composed of independent functional modules called Reactors, such as ion channels and the sarcoplasmic reticulum, and dynamic variables called Nodes, such as ion concentrations. The interactions between Reactors and Nodes are described by the graph theory and the resulting graph represents a blueprint of an intricate cellular system. Reactors are prepared in a hierarchical order, in analogy to the biological classification. Each Reactor can be composed or improved independently, and can easily be reused for different models. This way of building models, through the combination of various modules, is enabled through the use of object-oriented programming concepts. Thus, simBio is a straightforward system for the creation of a variety of cell models on a common database of functional modules.     

Loading problems with tool management in flexible manufacturing systems: A few integer programming models

In automated production systems like flexible manufacturing systems (FMSs), an important issue is to find an adequate workload for each machine for each time period. Many integer linear programming (ILP) models have been proposed to solve the FMS loading problems, but not all of them take tools into account. Those that do not consider tooling are quite unrealistic, especially when setup times are important with respect to processing times. When tool loading has to be handled by the model, the load assignment may have to be changed completely. In this article we consider FMSs with a tool management of the following type: the system works in time periods whose durations are fixed or not; and tools are loaded on the machines at the beginning of each time period and stay there for the whole time period. Tool changes may occur only at the end of each time period when the system is stopped. We present some integer programming models for handling these situations with several types of objectives. Emphasis is laid on the ILP formulations. Computational complexities are discussed.     

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.     

Scheduling tasks and vehicles in a flexible manufacturing system

Due to their increasing applicability in modern industry, flexible manufacturing systems (FMSs), their design, and their control have been studied extensively in the recent literature. One of the most important issues that has arisen in this context is the FMS scheduling problem. This article is concerned with a new model of an FMS system, motivated by the practical application that takes into account both machine and vehicle scheduling. For the case of a given machine schedule, a simple polynomial-time algorithm is presented that checks the feasibility of a vehicle schedule and constructs it whenever one exists. Then a dynamic programming approach to construct optimal machine and vehicle schedules is proposed. This technique results in a pseudopolynomialtime algorithm for a fixed number of machines.     

A case study in FMS production planning and dispatching

The speedy development and extensive application of computers have helped play a significant role in a new technological revolution. The importance of FMS flexibility in producing a variety of products and adapting rapidly to customer requirements makes FMSs attractive. Further, FMSs are most appropriate for largevariety and medium- to high-volume production environments. However, the module of the FMS production planning system is not perfect. This paper focuses on a new scheme for FMS production planning and dispatching under the realistic assumptions promoted by a particular flexible manufacturing factory. Some practical constraints such as fixture uniqueness, limited tool magazine capacity, and a given number of pallets are considered. The simulation results indicate that the scheme provides a good production plan, according to the short-term plans from the MIS Department. Some conclusions are drawn and a discussion is presented.     

An Object-Oriented Approach for Flexible Manufacturing Control Systems Analysis and Design Using the Unified Modeling Language

In reacting to global competition and rapidly changing customer demands, industrial business organizations have developed a strong interest in flexible automation. The aim of flexible automation focuses on achieving agility in handling uncertainties from internal or external environments. Modeling complex structures, promoting reuse, and shortening the development time cycle are particularly significant aspects in the analysis and design of CIM systems, where heterogeneous elements have to be integrated in a complex control architecture. The design methodology for FMS control software involves the abstraction of an FMS and the estimation of the system performances. The aim of this activity is to suggest the optimal configuration of an FMS for given specifications, through simulation tools. In the software engineering field, object-oriented (OO) approaches have proven to be a powerful technique with respect to such aspects. The unified modeling language (UML), by using OO design methodologies, can offer reusability, extendibility, and modifiability in software design. Also, it bridges the gap that exists between the OO analysis and design area and the area of OO programming by creating an integrative metamodel of OO concepts. The specific goal of this paper is to formulate a new methodology for developing reusable, extendible, and modifiable control software for an FMS in an object-oriented environment. It is demonstrated that, with few diagrams, UML can be used to model such systems without being associated with other modeling tools.     

Modeling FMS with decision Petri nets

Decision point extended timed Petri nets or decision Petri nets (DPN) are introduced as an extended modeling framework for FMS performance evaluation. The decision point extension allows the explicit modeling of the control of the flow of tokens in timed Petri nets and hence represents the control of the flow of material, resources, and information in FMS. Further, the concept of a bounded transition is proposed to conveniently model the blocking logic in an FMS with limited buffer capacities. The motivation to present these conventions is to develop a user-friendly graphic model to represent FMS designs for analysis by discrete event simulation. DPN affords concise models that can be conveniently developed and easily transformed into discrete event simulation models. With the help of a simple FMS example, which includes a number of part types, loading rules, dispatching rules, and probabilistic branching (at an inspection station), we illustrate the DPN model development. As an illustration of the ease with which it can be tranformed into a simulation model, we have developed a generalized simulator called ROBSIM and outline here its methodological basis. The proposed concepts should be of interest to users of discrete event simulation in FMS design or elsewhere to tap the potential of basic Petri net concepts for graphic representation and specification purposes. In particular, our work should encourage other researchers to develop extensions relevant to their own areas of interest.     

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.     

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.     

Performance Analysis of Flexible Manufacturing Systems with a Single Discrete Material-Handling Device

We present an analytical model for performance prediction of flexible manufacturing systems (FMSs) with a single discrete material-handling device (MHD). This configuration of FMS is significant for many reasons: it is commonly found in industry, it simplifies material-handling control, it is amenable to analytical modeling, and it forms a building block for more complex systems. Standard queueing models are inadequate to analyze this configuration because of the need to take into consideration many nontrivial issues such as state-dependent routing, interference from the MHD, and the analysis of the MHD. To account for state-dependent routing, we develop an iterative method that is built around mean value analysis. To analyze the MHD interference, we use two queueing network models. In the first, we ignore queueing at the MHD but model the interference from the MHD by inflating the station service times. The second network models the queueing for the MHD and estimates the blocking (inflation) times needed for the first model. By iterating between the two networks, we are able to predict the performance of this configuration of FMS. Our analytical estimates are validated against discrete event simulation and shown to be quite accurate for initial system design.     

Experimental investigation of an FMS due-date scheduling problem: Evaluation of machine and AGV scheduling rules

Although extensive research has been conducted to solve design and operational problems of automated manufacturing systems, many of the problems still remain unsolved. This article investigates the scheduling problems of flexible manufacturing systems (FMSs). Specifically, the relative performances of machine and automated guided vehicle (AGV) scheduling rules are analyzed against various due-date criteria. First, the relevant literature is briefly reviewed, and then the rules are tested under different experimental conditions by using a simulation model of an FMS. The sensitivity to AGV workload, buffer capacity, and processing-time distribution is also investigated to assess the robustness of the scheduling rules.     

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.     

Effectiveness of flexible routing control

Flexibility in part process representation and in highly adaptive routing algorithms are two major sources for improvement in the control of flexible manufacturing systems (FMSs). This article reports the investigation of the impact of these two kinds of flexibilities on the performance of the system. We argue that, when feasible, the choices of operations and sequencing of the part process plans should be deferred until detailed knowledge about the real-time factory state is available. To test our ideas, a flexible routing control simulation system (FRCS) was constructed and a programming language for modeling FMS part process plans, control strategies, and environments of the FMS was designed and implemented. In addition, a scheme for implementing flexible process routing called data flow dispatching rule (DFDR) was derived. The simulation results indicate that flexible processing can reduce mean flow time while increasing system throughput and machine utilization. We observed that this form of flexibility makes automatic load balancing of the machines possible. On the other hand, it also makes the control and scheduling process more complicated and calls for new control algorithms.     

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.     

Dynamic flexibility metrics for capability and capacity

In production environments, such as Flexible Manufacturing Systems (FMSs), the schedule can be disturbed by the occurrence of unplanned events. Machines stop for major failures, maintenance, tool changes due to wear, or tool reassignments. The rescheduling process, however, can be costly. In this study, a dynamic measure of flexibility which helps to determine an appropriate time for rescheduling an FMS has been defined and investigated. Flexibility is defined as a function of Capability and Capacity. Accordingly, two metrics have been developed to monitor the capability and capacity efficiency of each machine in the system for responding to the dynamic system status. The value of each metric falls between 0 and 1 at all times. Higher values in the capability metric mean better machine selection and part distribution strategies among the machines. Higher values for the capacity metric mean higher machine utilization in the production plan. Based on the interaction between the metrics and their respective behavior in the system, four states have been identified and characterized. Simulations of various scenarios can be used to demonstrate the use of these metrics for monitoring FMS operations and determining appropriate times for rescheduling and tool reassignment.