An intelligent hierarchical workstation control model for FMS

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.

Presents the master production scheduling (MPS) problem of a flexible manufacturing system (FMS). Earliness/tardiness production scheduling and planning (ETPSP) is one of the solutions used to integrate MRP and JIT effectively. Previous researches on ETPSP have only applied to problems of single/parallel machines with earliness and tardiness penalties on a common due date and capacity. Proposes a revised ETPSP for the purpose of developing an MPS that can fit into the FMS environment where a multiple machine capacity on multi‐processes and batch sizes is included. Outlines the application of an enhanced ETPSP method using a genetic algorithm (GA) solution to solve a multi‐product FMS production problem. Shows that the use of the improved ETPSP model can represent a real life FMS environment and that a solution can be effectively and efficiently obtained using the GA approach.

Production planning in flexible manufacturing systems is concerned with the organization of production in order to satisfy a given master production schedule. The planning problem typically gives rise to several hierarchical subproblems which are then solved sequentially or simultaneously. In this paper, we address one of the subproblems: the part type selection problem. The problem is to determine a subset of part types having production requirements for immediate and simultaneous processing over the upcoming period of the planning horizon, subject to the tool magazine and processing time limitation. Several versions of tabu search (TS) algorithm are proposed for solving the problem. A systematic computational test is conducted to test the performance of the TS algorithms. The best TS algorithm developed is compared to a simulated annealing algorithm.

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>

Interfacing aggregate plans and master production schedules via a rolling horizon feedback procedure

Intelligent Control of Manufacturing Systems Using Open Communication

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.

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.

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.

Production-Monitoring Integration in Planning and Control of FMS

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.

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.

Traditional approaches to planning and control of manufacturing (MRPII) focus on discrete parts manufacturing industries (e.g. automotive). The chemical industry, however, presents unique challenges. Cross-contamination of production is a key issue among some chemical facilities. A considerable amount of capacity is lost as a result of changeovers which involve performing thorough clean-ups to wash away the impurities which may contaminate the next product to be produced. Therefore, planning for sequence-dependent changeovers becomes crucial and complicates the master production scheduling process. This paper shows how improved master production scheduling performance can be obtained by using a two-level master production schedule (MPS) to focus on key plant processes, and by incorporating a scheduling heuristic which considers sequence-dependent changeovers and capacity constraints. This approach is illustrated using actual operating data from a chemical firm typical of many process industry operations. Simulation experiments are reported that test the performance of the proposed master scheduling method in a single-stage sequence-dependent process. The experimental factors include both the introduction of the two-level MPS with the scheduling heuristic, and the effect of changes in the MPS batch size. The results demonstrate that important simultaneous improvements in process changeover time and delivery performance can be achieved using the proposed MPS scheduling approach against a more traditional (single-level) MPS approach which does not consider sequence-dependent changeovers. Further, we find that delivery performance is relatively insensitive to adjustments in the MPS batch size when using the two-level MPS approach.

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. >