Modeling and information structures for supervisory control of flexible manufacturing systems

This chapter describes a framework for computer-aided process planning, scheduling and control for flexible manufacturing systems (FMS) producing One-of-a-Kind Products (OKP). This constitutes a form of mass customisation (MC) using one-piece flow (mass production) to produce variations on a number of different kinds of products (customisation). The basic concept is to produce customised forms of a standard product in the same time and for the same cost as the standard product. There is a need to capture customer requirements that specify the variation of the standard product easily, quickly and accurately. Also, there is a need to be able to respond to changing demands in the market for new variations and options for standard products. This chapter includes a case study of a company that produces a significant number of products daily that require the use of computer-aided engineering software systems and CNC (computer numerical control) equipment. The planning and control of these FMS are complicated because of the non-standard nature of the products.

This paper presents a step-by-step procedure for the design of the production control for flexible manufacturing systems (FMSs). Previous research has showed that there is a definite need to improve the production control for many FMSs in practice. The proposed procedure consists of three main steps: (I) an analysis of the FMS situation and a specification of the problems to be dealt with, (II) the design of the production control hierarchy, and (III) the design of the decision support tools and an information system. Feedback loops complete the procedure.<br> The design procedure can be seen as a synthesis of theory and practice. The procedure is illustrated by a case study.

Flexible Manufacturing Systems (FMSs) are usually composed of general purpose machines with automatic tool changing capability and integrated material handling. The complexity of FMSs requires sophisticated control. In this paper we present a four-level control hierarchy and outline computationally feasible control algorithms for each level. The top level is concerned with the choice of part types and volumes to be assigned to the FMS over the next several months. The second level plans daily or shift production. Production levels are set and tools are allocated to machines so as to minimize holding and shortage costs. Various FMS environments are presented. The third level determines process routes for each part type in order to minimize material handling. Additional tools are loaded on machines when possible to maximize alternate routeing. Routes are then assigned to parts to minimize workload assignment, and these are used by level four for actual routeing, sequencing and material handling path control. The level three model is formulated as a linear program, and heuristics are used for level four. An example is provided to illustrate the completeness of the decision hierarchy and the relationships between levels.

Control of flexible manufacturing systems (FMS) requires the complex interaction of two components: (1) computers to perform automated control and routing activities, and (2) humans to supervise the automation, to monitor system flows and outputs, and to intervene to diagnose and either correct or compensate for unanticipated events. Current academic FMS scheduling research has focused mainly on the first component in the control loop, development of scheduling algorithms for optimization and control. Here, the second component is included in both the definition of the FMS control problem and the corresponding control approach. A more realistic definition of the FMS control environment is presented using a supervisory control framework. Within this context, the concept of aiding a human operator who supervises the predominantly automated FMS operations is developed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Intelligent Control of Manufacturing Systems Using Open Communication

This paper introduces a formal specification model that covers a large class of real Flexible Manufacturing System (FMS). Using this model, a designer expresses the functional capacities of his system and the product flows. Parallel manufacturing processes, having, transformation, assembly, disassembly, test and storage operations, are considered. FMS specification is automatically transformed into a CP-net model. Hence, the verification and the supervisory control techniques based on CP-nets can be applied to the generated CP-net. Thus, a parameterized solution is defined taking into account the flexibility of the specification model. The present work led to the implementation of a specific tool, called MAC-FMS, allowing graphical specification, supervisory control and verification through its interaction with CPN tools environment (Jensen's tool).

In recent years, the scheduling and control of Flexible Manufacturing Systems (FMS) has received considerable attention. Due to complexity considerations, heuristic solutions are often the norm for real-time scheduling and implementation of FMS control activities. However, heuristic solutions often lack consideration of overall system implications, and various practical difficulties may arise in the control of unmanned FMS.The purpose of this research is to address deadlock situations that may arise in unmanned FMS. Petri nets are used to model the sequencing of material through the workstations and the limited capacity of resources available in the FMS. The model is then used for defining restriction policies (i.e., policies that disallow some of the options of the real-time resource allocation strategy) for deadlock avoidance. We propose an on-line restriction policy that avoids deadlock situations, while enabling completion of all active jobs in the FMS. The proposed algorithm is minimally restrictive and suitable for real-time implementation.KeywordsInternal TransitionProduction SequenceRestriction PolicyReachability GraphProduction PlaceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

SUMMARYConsidering the fact that Flexible Manufacturing Systems (FMS) should be able to accommodate a variety of different parts in random order, tool management at cell level and tool transportation, tool data management, tooling data collection, tool maintenance, and manual and/or robotized tool assembly at FMS system level are very important. Tooling information in FMS is used by several subsystems, including: production planning, process control, dynamic scheduling, part programming, tool preset and maintenance, robotized and/or manual tool assembly, stock control and materials storage.The paper summarizes the major tasks to be solved when designing tool management systems for FMS, as well as gives a solution for describing the data structure of a tool data base integrated with a generic tool description method, and shows a sample transaction of the way the FMS real-time control system can access and use this data base.

In recent years, a great deal of research has been focused on preventing deadlock in Flexible Manufacturing Systems. Policies based largely on Petri net models have been presented in the literature. Recently, a quite different approach has been developed based on supervisory control theory, and it has been adapted to solve the nonblocking maximally permissive control problem in various resource allocation systems, such as an Automatic Guided Vehicle system and a Production Cell. In this chapter, the authors obtain the corresponding control policy for a Flexible Manufacturing System, and from it derive an equivalent distributed control using the recent theory of supervisor localization.

Tools for Design and Optimal Control of Production Line and Flexible Manufacturing Systems

This paper presents an innovative Multi-Agent approach related to an advanced real time control of flexible manufacturing systems. The proposed architecture is based on the paradigm of Distributed Intelligence and Multi-Agent Systems. The developed Multi-Agent prototype system integrates the following functions: Scheduling, dispatching, monitoring and error handling. A new negotiation protocol for manufacturing systems is presented in this paper. The purpose of this protocol is to assign dynamic operations to the resources of the Manufacturing System in order to accomplish the proposed tasks. This protocol is able to deal with exceptions.

Deadlock prevention plays an important role in the modeling and control of flexible manufacturing systems (FMS). This paper presents a novel and computationally efficient method to design optimal control places, and an iteration approach that only computes the reachability graph of a plant Petri net model once in order to obtain a maximally permissive liveness-enforcing supervisor for an FMS. By using a vector covering approach, a minimal covering set of legal markings and a minimal covered set of first-met bad markings (FBM) are computed. At each iteration, an FBM from the minimal covered set is selected. By solving an integer linear programming problem, a place invariant is designed to prevent the FBM from being reached and no marking in the minimal covering set of legal markings is forbidden. This process is carried out until no FBM can be reached. In order to make the considered problem computationally tractable, binary decision diagrams (BDD) are used to compute the sets of legal markings and FBM, and solve the vector covering problem to get a minimal covering set of legal markings and a minimal covered set of FBM. Finally, a number of FMS examples are presented to illustrate the proposed approaches.

PurposeA major factor to the success of flexible manufacturing systems (FMSs) is their ability to transport work pieces between different workstations. FMS have now become more advanced and material‐handling systems have become progressively more sophisticated, it is not exceptional to have automated steering of tools to workstations as well. Such system design will improve the tool‐handling capability and the system productivity while holding tool cost to a minimum. Tool cost could represent as much as 25 percent of the operating cost. The purpose of this paper is to propose a new colored Petri net (CPN)‐based approach to the design and development of a tool sharing control system that is intended to help use of the proper and minimal number of tools for a manufacturing system.Design/methodology/approachA new black token timed PN model is first developed, to reduce the complexity of the graphical representation a new CPN model is developed. The new CPN model also allows to find the optimal sequence. The optimal sequence has no effect on the work in process (WIP) but it influences the number of tools used in the system. The main input to the PN model for a manufacturing system is the process plan. Next, all the invariants and total number of possible elementary circuits are determined using the Integrated Net Analyzer (INA) software. Output from the INA software is exported to the Excel spreadsheet. The Excel spreadsheet can be designed to calculate the total number of tokens, processing time, cycle time, etc. of each elementary circuit. Subsequently, the constraints used in Lingo will be created according to critical circuit rules. Finally, linear programming (LP) technique is used to optimize the WIP and tool inventory. Lingo software is used for the LP, the constraints from the Excel sheet will be the input data to the Lingo program, and based on those constraints the Lingo will provide the optimal values for the desired parameters. The output from Lingo will be used to recalculate the cycle time of each elementary circuit in the Excel sheet. The system is then analyzed before and after the implementation of the CPN model.FindingsA new CPN model based on tool‐sharing philosophy for an FMS with N part types and M stations is proposed.Originality/valueThe paper presents a new CPN‐based approach to the design and development of a tool sharing control system, that is, intended to help use of the proper and minimal number of tools for a manufacturing system. The new CPN model also allows to find the optimal sequence. The idea is new and pure and has not been presented before using the methodology adopted in this paper.

This paper describes the use of an ARIMA (autoregressive-integrated-moving-average) model and its equivalent state space models to produce rule-based knowledge for flexible manufacturing systems (FMS) that can be used to investigate a wide variety of problems including machine breakdown, material shortage, and changes of scheduling rules. One great advantage of using the proposed models is the ease with which the simulation results can be summarised, analysed and captured, as well as the availability of the mathematical representation of the knowledge that can be kept in a knowledge database for evaluation and selection of alternative FMS strategies in a real-time environment. Various case studies are used to illustrate the methodology and the development of ARIMA and state space models, the analysis includes the system cost and stability of changes or interventions, the relationships among the simulation inputs and outputs, and the formulation of the production rule-base for the FMS scheduler. Management can use this integrated approach to describe and predict the dynamic behaviour of a complex FMS.

An approach for integrated flexible manufacturing system (FMS) control is developed. Decentralized control is examined and shown to compare favorably with conventional centralized control. It complies with the requirements of flexibility, reactivity, and accessibility for use on FMSs. The principles of a coordination founded on an event-based approach are given. A presentation model and its implementation principles are presented. >