A dynamic flow control policy for failure prone flexible manufacturing systems

For a flexible manufacturing system, determining the feasible production set at a given time is essential for production management. In this paper, we propose methods for estimating the production set for a general multi–part type failure prone flexible manufacturing system. We also discuss how to get an open loop control that can ensure the system producing at the margin of its expected production set and how to estimate the corresponding variance of the expected production set. Some special flexible manufacturing systems are discussed where we also suggest some simpler approximations. Numerical examples are presented to illustrate our algorithm

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.

Intelligent Control of Manufacturing Systems Using Open Communication

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>

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.

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

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.

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.

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. >

Model Based Intelligent Control in Flexible Manufacturing Systems

In this paper we discuss a new matrix-based modeling technique for flexible manufacturing systems (FMS). Four Task Plan Matrices can completely describe the job sequencing and resource assignment details of an FMS. The matrices come directly from plans generated by machine planners, or from the bill of materials and resource requirements matrix. Given the matrices, a rule-based supervisory controller operates in real-time to sequence jobs and assign resources depending on dynamic situational information about parts available, resources idle, and jobs completed. The function of the FMS can be quickly changed when new products are needed simply by changing the four matrices. By deriving explicit mathematical formulas for p-invariants and the resource requirement matrix for generalized reentrant flow lines, NP-complexity problems are avoided, which is often an issue in other modeling techniques.KeywordsManufacturingdiscrete event systemsflexible manufacturing systems.

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).

This paper proposes feedback control algorithms suitable for the top level of a hierarchical approach to controlling Flexible Manufacturing Systems (FMS). Preview and state feedback control techniques are employed to regulate the inventory levels near zero. The resulting controller utilizes inventory state feedback and anticipatory demand feedforward control, referred to as preview control. The preview control takes on the form of demand filtering. Special dynamic properties of the demand filter are discussed. We show how future demand information is related to present production and inventory levels. Production constraints are explicitly handled in the development of the feedback controller. We present numerical results for a test case.

An intelligent hierarchical workstation control model for FMS

Abstract In this study, allocation of functions and system size (number of machines) were manipulated to test their effects on operator performance and subjective response in a flexible manufacturing system (FMS). An experiment was conducted using a cross-over design with three independent variables: number of machines, task allocation, and sequence of task presentation. Operator performance was measured by response time, and subjective response assessed using two questionnaires. Physiological measures were also taken (EKG and respiratory rate). The results revealed that the number of machines in the system did not affect performance or subjective or physiological response while the effect of allocation level was significant for both performance and subjective response. Key Words: flexible manufacturing systemperformancesubjective responseallocation of function