Analysis of dynamic control strategies of an FMS under different scenarios

This paper presents a simulation study aimed at evaluating the performances of a flexible manufacturing system (FMS) in terms of makespan, average flow time, average delay time at local buffers and average machine utilization, subject to different control strategies which include routing flexibilities and dispatching rules. The routing strategies under evaluation are ‘no alternative routings’; ‘alternative routings dynamic’; and ‘alternative routings planned’. Above routing strategies are combined with seven dispatching rules, and studied in different production volume which varies from 50 to 500 parts. In addition, impacts of both infinite and finite local buffer capacities are analyzed. Since an FMS usually deals with a variety of products, effects of changing the part mix ratio are also discussed. Finally, machine failure is also introduced in this research to study the effects of machine reliability on the system. Simulation results indicate that the ‘alternative routings planned’ strategy outperforms other routing strategies if the local buffer size is infinity. However, there is no particular dispatching rule that performs well in all buffer size settings but infinity buffer size is not the best choice with respect to the four performance measures. In addition, the four performance measures, except machine utilization, under different control strategies seem quite insensitive to the variation in part mix ratios.     

Estimating customer service levels in automated multiple part-type production lines: An analytical method

We develop an approximate analytical method to estimate the customer service levels in automated multiple part-type production lines. The production line consists of several processing stations in series with finite intermediate buffers, one for each part-type. The main contributions include the analysis of multiple part-type systems with machine setups, bypass routings and stations having combinations of shared and dedicated machines. This research is motivated by observations of real production lines. We use the continuous material approximation in modeling the system behaviour and develop a new approximate decomposition method to analyze the performance of the system. Validation experiments conducted on production lines with different configurations show good accuracy in the estimation of customer service levels compared to simulation. We use an example case study to demonstrate the application of the model in the performance improvement of a system that is based on a real production line. The analytical model is proposed as a reliable and fast performance analysis tool for the optimization of automated multiple part-type production lines with complex configurations.     

Scheduling in a manufacturing shop with sequence-dependent setups

In this paper, the problem of scheduling multiple jobs in a flexible manufacturing cell with multiple machine stations is addressed. Due to the large capital investments that usually characterize flexible manufacturing systems (FMS), an area of control of great interest to system users is that of maximizing the system performance through the minimization of machine idle and setup times. The magnitude of total time spent on machine setups and idle times is influenced by the availability of jobs, job mix, similarities of jobs and job scheduling procedure used. Similar jobs on the same machine require less setup times. Similarly, the use of an adequate scheduling method also reduces total idle and setup times. Such reduction improves the flow times of jobs. In this paper, a heuristic algoritm for scheduling jobs with sequence dependent setup times in a FMS is presented. The measure of performance for evaluating schedule adequacy is the production makespan.     

Clustering algorithm for solving group technology problem with multiple process routings

Cell formation is an important problem in the design of a cellular manufacturing system. Most of the cell formation methods in the literature assume that each part has a single process plan. However, there may be many alternative process plans for making a specific part, specially when the part is complex. Considering part multiple process routings in the formation of machine-part families in addition to other production data is more realistic and can produce more independent manufacturing cells with less intercellular moves between them. A new comprehensive similarity coefficient that incorporates multiple process routings in addition to operations sequence, production volumes, duplicate machines, and machines capacity is developed. Also, a clustering algorithm for machine cell formation is proposed. The algorithm uses the developed similarity coefficient to calculate the similarity between machine groups. The developed similarity coefficient showed more sensitivity to the intercellular moves and produced better machine grouping.     

Experimental performance evaluation of Profibus-FMS

Examines the performance characteristics of the industrial communications network via an experimental model of a manufacturing automation system. In the study presented in this article, an experimental model of a Profibus-based manufacturing automation system was developed. The experimental model consisted of two robots, two conveyor belts, a PLC, an NC machine, and an operator station. Using the experimental model, this study evaluated the delay characteristics of a message transmitted through the fieldbus message specification (FMS) services. FMS is the application layer protocol of Profibus and Foundation Fieldbus. The message delay was measured at each sublayer of the Profibus protocol stack. In this article, after an overview of progress in industrial communication networks and a brief introduction to Profibus and the FMS, the experimental model of the Profibus-based manufacturing automation system developed in this study is presented, and the FMS service delay characteristics obtained from the experimental model are discussed     

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.     

Modelling and performance maximization of an integrated automated guided vehicle system using coloured Petri net and response surface methods

The objective of this study is to model and maximize performance of an integrated Automated Guided Vehicle System (AGVS), which is embedded in a pull type multi-product, multi-stage and multi-line flexible manufacturing system (FMS). This study examines the impact of guide-path flexibility on system performance through the development of three different guide-path configurations which range from dedicated to flexible relationships between automated guided vehicles (AGVs) and machine/assembly station resources. The system is modelled using coloured Petri net method (CPN) and the simulation results lead to identify the resource redundancy which can be rectified to achieve lower overall cost of the system through the development of flexible guide-path configurations. The study is extended to seek global near-optimal conditions for each guide-path configuration using response surface method, which yields improvements in system throughput and cycle time along with a decrease in the numbers of AGVs.     

Analysis of dynamic due-date assignment models in a flexible manufacturing system

This paper investigates the effects of dynamic due-date assignment models (DDDAMs), routing flexibility levels (RFLs), sequencing flexibility levels (SFLs) and part sequencing rules (PSRs) on the performance of a flexible manufacturing system (FMS) for the situation wherein part types to be produced in the system arrive continuously in a random manner. The existing DDDAMs considered are dynamic processing plus waiting time and dynamic total work content. A new model known as dynamically estimated flow allowance (DEFA) has also been developed and investigated. The routing flexibility of the system and the sequencing flexibility of parts are both set at three levels. A discrete-event simulation model of the FMS is used as a test-bed for experimentation. The performance measures evaluated are mean flow time, mean tardiness, percentage of tardy parts and mean flow allowance. The statistical analysis of the simulation results reveals that there are significant interactions among DDDAMs, RFLs, SFLs and PSRs for all the performance measures. The proposed DEFA model provides the minimum percentage of tardy parts in all the experiments. Regression-based metamodels have been developed using the simulation results. The metamodels are found to provide a good prediction of the performance of the FMS within the domain of their definition.     

Simulation modelling and analysis of part and tool flow control decisions in a flexible manufacturing system

This paper presents the details of a simulation study carried out for analyzing the impact of scheduling rules that control part launching and tool request selection decisions of a flexible manufacturing system (FMS) operating under tool movement along with part movement policy. Two different scenarios have been investigated with respect to the operation of FMS. In scenario 1, the facilities such as machines, tool transporter and part transporter are assumed to be continuously available without breakdowns, whereas in scenario 2, these facilities are prone to failures. For each of these scenarios, a discrete-event simulation model is developed for the purpose of experimentation. A number of scheduling rules are incorporated in the simulation models for the part launching and tool request selection decisions. The performance measures evaluated are mean flow time, mean tardiness, mean waiting time for tool and percentage of tardy parts. The results obtained through the simulation have been statistically analyzed. The best possible scheduling rule combinations for part launching and tool request selection have been identified for the chosen FMS.     

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.     

Integration of product design,process planning,scheduling, and FMS control using XML data representation

An efficient model for communications between CAD, CAPP, and CAM applications in distributed manufacturing planning environment has been seen as key ingredient for CIM. Integration of design model with process and scheduling information in real-time is necessary in order to increase product quality, reduce the cost, and shorten the product manufacturing cycle. This paper describes an approach to integrate key product realization activities using neutral data representation. The representation is based on established standards for product data exchange and serves as a prototype implementation of these standards. The product and process models are based on object-oriented representation of geometry, features, and resulting manufacturing processes. Relationships between objects are explicitly represented in the model (for example, feature precedence relations, process sequences, etc.). The product model is developed using XML-based representation for product data required for process planning and the process model also uses XML representation of data required for scheduling and FMS control. The procedures for writing and parsing XML representations have been developed in object-oriented approach, in such a way that each object from object-oriented model is responsible for storing its own data into XML format. Similar approach is adopted for reading and parsing of the XML model. Parsing is performed by a stack of XML handlers, each corresponding to a particular object in XML hierarchical model. This approach allows for very flexible representation, in such a way that only a portion of the model (for example, only feature data, or only the part of process plan for a single machine) may be stored and successfully parsed into another application. This is very useful approach for direct distributed applications, in which data are passed in the form of XML streams to allow real-time on-line communication. The feasibility of the proposed model is verified in a couple of scenarios for distributed manufacturing planning that involves feature mapping from CAD file, process selection for several part designs integrated with scheduling and simulation of the FMS model using alternative routings.     

A comparative performance analysis of deadlock avoidance control algorithms for FMS

A deadlock condition for flexible manufacturing systems is characterized by a set of parts, which have been processed but cannot be discharged by a set of machines or buffers. To avoid such problems, it is necessary to adopt suitable control policies which limit the resource allocation in the system, thus affecting the overall system performance. In the present work, we address the problem of evaluating and comparing the performance of deadlock avoidance control policies applied to FMS. The problem is discussed for both untimed and timed models, and for models both with and without deadlock avoidance control policies. Different control algorithms, among the most common in the literature, have been considered. Imperfect deadlock avoidance control policies are also considered. In addition, some indices are proposed to assess the structural properties of FMS with respect to deadlock occurrence and their performance. Two different application examples are analyzed, with the help of a commercial simulation package. Finally, an adaptive algorithm which can learn from system evolution to avoid deadlocks is illustrated.     

A hybrid method based on genetic algorithm and dynamic programming for solving a bi-objective cell formation problem considering alternative process routings and machine duplication

Cell formation is one of the first and most important steps in designing a cellular manufacturing system. It consist of grouping parts with similar design features or processing requirements into part families and associated machines into machine cells. In this study, a bi-objective cell formation problem considering alternative process routings and machine duplication is presented. Manufacturing factors such as part demands, processing times and machine capacities are incorporated in the problem. The objectives of the problem include the minimization of the total dissimilarity between the parts and the minimization of the total investment needed for the acquisition of machines. A normalized weighted sum method is applied to unify the objective functions. Due to the computational complexity of the problem, a hybrid method combining genetic algorithm and dynamic programming is developed to solve it. In the proposed method, the dynamic programming is implemented to evaluate the fitness value of chromosomes in the genetic algorithm. Computational experiments are conducted to examine the performance of the hybrid method. The computations showed promising results in terms of both solution quality and computation time.     

LEARNING-BASED SCHEDULING OF FLEXIBLE MANUFACTURING SYSTEMS USING SUPPORT VECTOR MACHINES

Dispatching rules are usually applied to dynamically schedule jobs in flexible manufacturing systems (FMSs). Despite their frequent use a significant drawback is that the performance level of the rule is dictated by the current state of the manufacturing system. Because no rule is better than any other for every system state, it would be highly desirable to know which rule is the most appropriate for each given condition. To achieve this goal we propose a scheduling approach using support vector machines (SVMs). By using this technique and by analyzing the earlier performance of the system, “scheduling knowledge” is obtained whereby the right dispatching rule at each particular moment can be determined. Simulation results show that the proposed approach leads to significant performance improvements over existing dispatching rules. In the same way it is also confirmed that SVMs perform better than other traditional machine learning algorithms as the inductive learning when applied to FMS scheduling problem, due to their better generalization capability.     

Simulation and graphical animation of advanced manufacturing systems

Automated production systems, known as Flexible Manufacturing Systems (FMS), offer a promising solution for the low productivity of mid-volume, mid-variety batch production. These systems consist of integrated machine modules and material handling equipment under computer control.This paper describes a general purpose discrete-event Flexible Manufacturing Systems Simulator (FMSSIM). The package is programmed in FORTRAN and is capable of simulating different configurations, material handling systems, and topologies including bidirectional tracks. The simulator checks blockage of routes due to interference of carts and simulates random failures and repairs of the various components in the system. The simulator provides the user with a wide range of priority rules to select from, and enables the user to define his own rules if required. The simulator produces reports on various vital system performance statistics and also displays the movement of parts through the system on a refreshed CRT. This simulator is a modular, user-oriented package which allows the designer to evaluate a wide range of systems with varied design parameters and select an efficient flexible manufacturing system. Some general trends in the behaviour of the flexible manufacturing systems are identified and guidelines for their design and operation are suggested.     

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.     

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).     

A multiple-objective grouping genetic algorithm for the cell formation problem with alternative routings

This paper addresses the cell formation problem with alternative part routings, considering machine capacity constraints. Given processes, machine capacities and quantities of parts to produce, the problem consists in defining the preferential routing for each part optimising the grouping of machines into manufacturing cells. The main objective is to minimise the inter-cellular traffic, while respecting machine capacity constraints. To solve this problem, the authors propose an integrated approach based on a multiple-objective grouping genetic algorithm for the preferential routing selection of each part (by solving an associated resource planning problem) and an integrated heuristic for the cell formation problem.     

Operations planning and scheduling problems in an FMS: An integrated approach

Operations planning and scheduling (OPS) problems in advanced manufacturing systems, such as flexible manufacturing systems, are composed of a set of interrelated problems, such as part-type batching, machine grouping, tool loading, routing, part input sequencing and on-line scheduling. In this paper, an integrated, simulation-based approach to the OPS problems is discussed. A detailed simulation model is developed using FORTRAN and SLAM II which integrates loading, part inputting, routeing and dispatching issues of the OPS. An experimental frame is developed which provides statistical analysis of the simulation output by developing an experimental design. Statistical analyses concerning a number of system parameters (e.g. loading strategies and scheduling rules) are performed on a set of performance measures.     

Real time fuzzy scheduling rules in FMS

This paper presents a real-time fuzzy expert system to scheduling parts for a flexible manufacturing system (FMS). First, some vagueness and uncertainties in scheduling rules are indicated and then a fuzzy-logic approach is proposed to improve the system performance by considering multiple performance measures. This approach focuses on characteristics of the system's status, instead of parts, to assign priorities to the parts waiting to be processed. Secondly, a simulation model is developed and it has shown that the proposed fuzzy logic-based decision making process keeps all performance measures at a good level. The proposed approach provides a promising alternative framework in solving scheduling problems in FMSs, in contrast to traditional rules, by making use of intelligent tools.