The effect of responsiveness of the control-decision system to the performance of FMS

In Flexible Manufacturing System (FMS), decision-making process is one of the key aspects for its performance enhancement, particularly for shop-floor control, where operation managers need to make a large number of control decisions. A term called ‘response-time’ in decision-making process has been defined in this study, which refers to a lead time in decision-making and its implementation. This paper contributes a methodology for decision-maker to study the decision-making process and identify a suitable decision-making approach, while considering critical factors such as decision automation levels, routing flexibility levels, and control strategies. Considering the complexity in modelling an FMS with routing flexibility levels, control strategies (sequencing and dispatching rules), and decision-making process with information system, computer simulation modelling has been employed to study the makespan performance. The results show that decision-making process with response-time for FMS control performs as good as with the real-time control when routing flexibility level is low. Furthermore, under some specific situations, it even outperforms the real-time control. This research gives insight to decision-maker to identify whether a decision system with response-time will be more suitable and economically justified, or real-time decision-making system is more appropriate.     

Flexibility performance: Taguchi's method study of physical system and operating control parameters of FMS

In present manufacturing environment, the manufacturing flexibility has become one of the strategic competitive tools. Flexibility refers to the availability of alternative resources. These resources may have varied parameters, particularly related to physical and operating system. These physical and operating parameters of alternative resources may influence the system's performance with the changing levels of flexibility and operational control parameters such as scheduling rules. Is increase in a flexibility level provides desired improved performance output? If yes, than under what conditions of physical and operating parameters and under which control strategy (CS)? Is improved performance is present at all increasing levels of flexibility? Flexible manufacturing system (FMS) being consist of numerous physical and operating parameters and complex in nature, the solution to these questions can provide an understanding of the productive levels of flexibility for a given physical and operating parameters of an FMS. This paper establishes the need of modelling of the physical and operating parameters of flexible manufacturing system along with flexibility and presents a simulation study under Taguchi's method analysis of these parameters. The paper contributes an approach to study the impact of variations in physical and operating parameters of an FMS and to identify the level of these variations that do not restrict the advantages of flexibility. The results show that the expected benefits from increasing the levels of flexibility and a superior CS may not be achieved if the physical and operating parameters of alternative machines have variations. Taguchi's method analysis indicates that relative percentage contribution of variations in physical and operating parameters of alternative resources should be negligible or minimum in the performance of FMS. Their increasing relative contribution may restrict the advantages of flexibility. If these variations are higher than increase in flexibility level may be counter productive.     

iCoSim-FMS: An intelligent co-simulator for the adaptive control of complex flexible manufacturing systems

We describe an intelligent co-simulator for real time production control of a complex flexible manufacturing system (CFMS) having machine and tool flexibility. The manufacturing processes associated with the CFMS are complicated with each operation being possibly done by several machining centers. The co-simulator design approach is built upon the theory of dynamic meta-model based supervisory control with the cooperation of its own embedded intelligent blocks. The system is implemented by coupling of the centralized simulation controller (CSC) and real-time simulator for enforcing dynamic strategies of shop floor control. The posteriori adaptive co-simulator is equipped with a concurrent bilateral mechanism for simulation optimization based on appropriate control rules enhancing performance criteria simulation efficiency. A working intelligent adaptive controller prototype (iCoSim-FMS) has been developed to validate the proposed approach and compare its performance with well known FMS heuristic methods.     

An evolutionary algorithm-based decision support system for managing flexible manufacturing

The high investment cost of flexible manufacturing systems (FMS) requires their management to be effective and efficient. The effectiveness in managing FMSs includes addressing machine loading, scheduling parts and dispatching vehicles and the quality of the solution. Therefore the problem is inevitably multi-criteria, and decision maker's judgement may contribute to the quality of the solution and the systems's performance. On the other hand, each of these problems of FMS is hard to optimize due to the large and discrete solution spaces (NP-hard). The FMS manager must address each of these problems hierarchically (separately) or simultaneously (aggregately) in a limited time. The efficiency of the management is related to the response time.

Here we propose a decision support system that utilizes an evolutionary algorithm (EA) with a memory of “good” past experiments as the solution engine. Therefore, even in the absence of an expert decision maker the performance of the solution engine and/or the quality of the solutions are maintained.

The experiences of the decision maker(s) are collected in a database (i.e., memory-base) that contains problem characteristics, the modeling parameters of the evolutionary program, and the quality of the solution. The solution engine in the decision support system utilizes the information contained in the memory-base in solving the current problem. The initial population is created based on a memory-based seeding algorithm that incorporates information extracted from the quality solutions available in the database. Therefore, the performance of the engine is designed to improve following each use gradually. The comparisons obtained over a set of randomly generated test problems indicate that EAs with the proposed memory-based seeding perform well. Consequently, the proposed DSS improves not only the effectiveness (better solution) but also the efficiency (shorter response time) of the decision maker(s).     

A parametric fuzzy logic approach to dynamic part routing under full routing flexibility

Manufacturing flexibility is a competitive weapon for surviving today’s highly variable and volatile markets. It is critical therefore, to select the appropriate type of flexibility for a given manufacturing system, and to design effective strategies for using this flexibility in a way to improve the system performance. This study focuses on full routing flexibility which includes not only alternative machines for operations but also alternative sequences of operations for producing the same work piece. Upon completion of an operation, an on-line dispatching decision called part routing is required to choose one of the alternatives as the next step. This study introduces three new approaches, including a fuzzy logic approach, for dynamic part routing. The fuzzy part routing system adapts itself to the characteristics of a given flexible manufacturing system (FMS) installation by setting the key parameters of the membership functions as well as its Takagi-Sugeno type rule base, in such a way to capture the bottlenecks in the environment. Thus, the model does not require a search or training for the parameter set. The proposed approaches are tested against several crisp and fuzzy routing algorithms taken from the literature, by means of extensive simulation experiments in hypothetical FMS environments under variable system configurations. The results show that the proposed fuzzy approach remains robust across different system configurations and flexibility levels, and performs favourably compared to the other algorithms. The results also reveal important characteristic behaviour regarding routing flexibility.     

An object-oriented model for FMS control

The flexible manufacturing system (FMS) is a distributed network of heterogeneous programmable manufacturing machinery, such as assembly lines and numerically controlled machines. Despite these interconnected, programmable hardware elements, the success of building a truly flexible manufacturing system has been limited so far, owing to the lack of flexibility in its control software layer. In integrating heterogeneous machinery, many existing FMS control software systems depend structurally on specific machinery and job-scheduling strategies, and thus it is difficult to incorporate new developments in FMS organization and operational requirements. In searching for an open architecture for the FMS control software system, this paper presents an object-oriented FMS data model. Among others, it represents each physical cluster of related machinery (called a flexible manufacturing cell) as an object. To facilitate the integration of heterogeneous physical cells, such cell objects share a common protocol of interacting with the main control process through inheritance from the abstract cell class. Other related physical and abstract entities in FMS are also modelled as objects, with their similarity and difference captured in inheritance hierarchies. To verify the proposed approach experimentally, a prototype FMS control software system named FREE (FMS Runtime Executive Environment) has been implemented on top of a commercial object-oriented database system.     

A knowledge-based decision support system for the management of parts and tools in FMS

Flexible manufacturing systems (FMS) are very complex systems with large part, tool, and information flows. The aim of this work is to develop a knowledge-based decision support system (KBDSS) for short-term scheduling in FMS strongly influenced by the tool management concept to provide a significant operational control tool for a wide range of machining cells, where a high level of flexibility is demanded, with benefits of more efficient cell utilization, greater tool flow control, and a dependable way of rapidly adjusting short-term production requirements. Development of a knowledge-based system to support the decision making process is justified by the inability of decision makers to diagnose efficiently many of the malfunctions that arise at machine, cell, and entire system levels during manufacturing. In this context, this paper proposes three knowledge-based models to ease the decision making process: an expert production scheduling system, a knowledge-based tool management decision support systems, and a tool management fault diagnosis system. The entire system has been created in a hierarchical manner and comprises more than 400 rules. The expert system (ES) was implemented in a commercial expert system shell, Knowledge Engineering System (KES) Production System (PS).     

Modeling and control of workstation level information flow in FMS using modified Petri nets

A flexible manufacturing system (FMS) has a traditional structure of three levels: cell, workstation, and equipment. The workstation level plays an important role in the overall performance of the FMS. This paper focuses on modeling and control of the FMS workstation level information flow. In order to have a unified workstation level system structure, front-end interfaces are introduced as a standard communication medium between the workstation level and the equipment level. A detailed information flow analysis is then carried out on the workstation level. For modeling purposes, a modified Petri net is proposed with its increased modeling capability over an ordinary Petri net. It associates data structures with places and programs with transitions. Finally, the modified Petri net is used to model and control the FMS workstation level information flow. The designed control system has been implemented in a real manufacturing factory with satisfactory performance.     

Establishing information requirements for supervisory controllers in a flexible manufacturing system using GTA

In this article we consider the technological change that has occurred in complex manufacturing systems within the past two decades and the implications it has had on the role of human operators in manufacturing systems control. Our examination ranges from the traditional production line manned by skilled machinists to flexible manufacturing systems (FMS) under supervisory control. On the basis of this study, we raise the question as to whether new advanced manufacturing technology interfaces are supportive of human operators in their responsibilities to manufacturing systems. We address this problem by analyzing supervisory controller information requirements for intervening in complex process control tasks as part of FMS operation. This analysis was conducted using a cognitive engineering research methodology, which has not previously been applied, in the domain of manufacturing. The method of GTA was applied to supervisory control of an FMS and produced detailed information requirements, which facilitated the formulation of general design guidelines for FMS interface design. The guidelines are aimed at supporting human operator process strategy development and decision making. © 2000 John Wiley & Sons, Inc.     

A multi-agent based agile manufacturing planning and control system

In today’s manufacturing enterprise, the performance of customer service level (e.g., short ordering-to-delivery time, low price) is highly dependent on the effectiveness of its manufacturing planning and control system (MPCS). However, most of the current MPCS, employed the hierarchical planning approach, may have some drawbacks, such as structural rigidity, difficulty of designing a control system, and lack of flexibility. Currently, RFID (Radio Frequency Identification) technology has been applied to enhance the visibility, accountability, track ability and traceability of manufacturing system whenever the accurate and detailed manufacturing information (e.g., raw material, WIP, products in factory and products in the down streams) of products will be followed in real-time basis by RFID technique. In addition, a multi-agent approach may be applied in a distributed and autonomous system which allows negotiation-based decision making. Therefore, the objective of this research is to study the application of RFID technique and multi-agent system (MAS) in developing an agent-based agile manufacturing planning and control system (AMPCS) to respond to the dynamically changing manufacturing activities and exceptions.In AMPCS, RFID-based manufacturing control (R-MC) module plays the role of controlling the manufacturing system in which production items and manufacturing resources attached with RFID tag may actively feedback production status to and receive production and operations schedule from advanced manufacturing planning (AMP) module. In addition, a bidding process and algorithm is developed to generate operations schedule by using the characteristics of MAS. Performance analysis (PA) module is responsible not only for evaluating the scheduling results but also for evaluating the performance of production execution. The development of an AMPCS for an automated manufacturing cell demonstrates that the integration of RFID technique and MAS in developing an agile manufacturing planning and control system can really possess the characteristics of visibility, accountability, track ability, responsiveness, and flexibility in a distributed and dynamic manufacturing system.     

A computer simulation model for studying the performance of coordinate measuring machines

The increasing trend toward computer-integrated manufacturing (CIM) in today's industry created a need for an effective process control. The objective of the inspection process is not only preventing shipment of defective parts but also providing a feedback to keep the manufacturing process in control. Through data processing capability, speed, and flexibility of operation, coordinate measuring machines (CMMs) play an important role for computer-integrated manufacturing (CIM). This paper introduces coordinate measuring machines and studies their performance. A computer simulation method for studying the performance of such machines working in a production line is developed. In this paper, CMM performance is measured by its speed and flexibility in performing measurements. In flexible manufacturing systems (FMS), CMMs serve as the inspection work station where arrival time of parts to be measured vary according to the flow of operations. The developed simulation model provides information about the machine, scheduled time for parts to be measured, and delay time for the measuring process.     

Optimization of testability parameters

The main objective of this study was to investigate performance models of automatic diagnostic systems taking into consideration its imperfections such as incorrect isolation and false alarms. This was accomplished by developing an optimization model to assist the decision maker in determining the optimal values of testability parameters which maximize his/her utility function. This will provide the decision maker with a tool to evaluate the parameters set forth by the designer and to assess the real capability of the diagnostic system. This tool also will help the decision maker to check if the correct detection and isolation capability of the system, as well as the imperfections of the system, e.g., incorrect isolation and false alarms are acceptable and satisfactory.

An interactive program was developed and help to implement the optimization modedl. This program provides the decision maker with enough flexibility to avaluate different different strategies and to repeat the decision process after changing one or more of the model's parameter. An example will be presented to show the application of this optimization model.     

System approach to a generic software specification for flexible manufacturing system job flow management

Programmable automation is a key factor to fully exploit the potentiality of flexible manufacturing systems (FMS). In particular, the flexibility of the control software acting at the operational level, allows one to make the control activities easier to program and re-program. This characteristic needs a generic software able to control an arbitrary FMS producing an arbitrary part-mix, according to arbitrary control strategies. Turning attention to the operational job flow management, a typical FMS is modelled as a discrete-event dynamic system in a formal and unambiguous way. The system state and its transitions (i.e. logical and physical entities, their attributes and relationships and the mechanism ruling their modifications in time) are described. The discrete-event model allows one to point out the organization of a generic software for job flow management, in terms of its inputs, outputs, knowledge base and decision-making processes.     

Expert systems: An application in flexible manufacturing

Flexible manufacturing systems (FMS) are needed to provide manufacturing operations with the capability to adjust, in real time, to changes in the manufacturing environment. Realization of the goals of flexible manufacturing is governed by the ability of the FMS to maintain adequate information on the factory to assist in generating scenarios from product planning to operations and performance. This leads to a view where the factory is represented as an integrated information system. To facilitate the analysis of information requirements and the design of information systems for flexible manufacturing, an expert support system (ESS) which can be used to model and study the various structures is described. This ESS uses the information cell model to build these information structures. Petri net representations of these structures and their interactions are then constructed. The ESS may now be used to exercise these models and study their performance using time and cost measures.     

Modeling, controlling, and simulation of local area networks for flexible manufacturing systems using Petri nets

Flexible manufacturing systems (FMSs) are advanced manufacturing systems comprising machining cells, robots, a automated guided vehicles (AGVs) that function under the hierarchical control of computers. The flexibility of an FMS primarily imparted by integrating the functions of different system elements such as machining cells, robots, AGVs usig computers. In this paper, the role of local area networks (LANs) in FMSs is explicated. The contribution of this paper is twofold. First, the differences between the LANs in conventional systems (CLANs) to that of LANs in FMSs (FLANs) are explicated. Secondly, a new class of Petri nets (PNs) called augmented timed PNs are introduced as a modeling tool designing, simulating, analyzing, and finally implementing the LAN in FMS. A software package has been developed to simulate the Petri net models (PNMs). The software package developed aids system designers by simulating the PNM and generating certain performance criteria.     

Dynamic production system diagnosis and prognosis using model-based data-driven method

Advanced manufacturing systems are becoming increasingly complex, subjecting to constant changes driven by fluctuating market demands, new technology insertion, as well as random disruption events. While information about production processes has been becoming increasingly transparent, detailed, and real-time, the utilization of this information for real-time manufacturing analysis and decision-making has been lagging behind largely due to the limitation of the traditional methodologies for production system analysis, and a lack of real-time manufacturing processes modeling approach and real-time performance identification method. In this paper, a novel data-driven stochastic manufacturing system model is proposed to describe production dynamics and a systematic method is developed to identify the causes of permanent production loss in both deterministic and stochastic scenarios. The proposed methods integrate available sensor data with the knowledge of production system physical properties. Such methods can be transferred to a computer for system self-diagnosis/prognosis to provide users with deeper understanding of the underlying relationships between system status and performance, and to facilitate real-time production control and decision making. This effort is a step forward to smart manufacturing for system real-time performance identification in achieving improved system responsiveness and efficiency.     

Discrete event and hybrid systems in robotics and automation: anoverview

Discrete event and hybrid systems modeling has been used extensively in automation, robotics, and manufacturing applications. Different frameworks for dynamic supervisory controllers are used in flexible manufacturing systems (FMS) and automated processes. This article presents an overview of some existing strategies that are used to control systems in real-time based on sensory data     

Path-clearing policies for flexible manufacturing systems

In practical manufacturing settings it is often possible to obtain, in real-time, information about the operation of several machines in a flexible manufacturing system (FMS) that can be quite useful in scheduling part flows. The authors introduce some scheduling policies that can effectively utilize such information and they provide sufficient conditions for the stability of two such policies     

A modeling strategy for hybrid systems based on event structures

This paper considers hybrid systems which are continuous/discrete-time systems interacting with a decision maker which oversees the control and structure of the continuous/discrete-time system. These two segments combine with a processor which evaluates data to produce a three-segment model of a hybrid system having sufficient flexibility to represent a broad range of real-time situations and sufficient generality to incorporate the essential aspects of other models into a single framework. In particular the paper uses a graphically expressive controlled Petri net formulation of the decision maker and any of the usual models for the continuous/discrete-time system. Interaction between the systems occurs via three types of events: continuous/discrete-time events, decision-making events, and processor events. These types of events and their composition are rigorously defined to produce event structures and event histories. These events and event histories are used for the domain of interaction functions which specify the channels of communication between the three essential segments of the hybrid system. The event-based domains allow the disassociation of these communication channels from dependence on particular kinds of models or applications. The range of the interaction functions are binary vector-valued indicating the activation/deactivation processes in the respective segments. The entire modeling strategy is motivated by applications and models found in the literature especially flexible manufacturing systems and the C-net model of a hybrid system.This work was partially supported by the National Science Foundation under Grant No. ECS-8800910.