- Email: [email protected]
An Intelligent Model Generator for FMS Simulation
AN INTELLIGENT MODEL GENERATOR FOR FMS SIMULATION
Liu Fuyan and Lu Shaoyi Hangzhou Institute of Electronic Engineering, Hangzhou, Zhejiang Province, China. Post Code 310037. Abstrct. Usually systems modelling can be time consuming. But a simulation model construction can be automated using artificial intelligence and expert systems tools and techniques. In this paper we present an intelligent model generator which is an implementation of automatic programming techniques. It is used to create simulation models of flexible manufacturing systems under study automatically. The model generator uses the information acquired via an interactive dialog interface to generate a model framework and experiment framework, then a converter envolved in the generator convert it into a target language structure that is the resulting simulation model of the system. The generator is contained in a simulation software environment which is used for study of the behavior of the FMS under various control and schedule policies, and performance evaluation with different system performance measures. In this paper modelling specification acquisition and model construction process are described. And the simulation software is also presented briefly. t
Keywords. Artificial intelligence I expert systems; flexible manufacturing systems; machine tools; modelling; simulation.
INTRODUCTION Systems modelling can be time consuming. Traditionally, a simulation model would be built by means of a general- purpose programming language or a special - purpose simulation language. Then the model would be validated and executed to allow experimentation. But a simulation model construction can be automated using artificial ihtelligence (AI) and expert systems tools and techniques. One approach to the simulation model construction employs automatic programming techniques. The intelligent model or program generator presented here is an implementation of this approach and is written in FORTRAN. The intelligent model generator is used to construct simulation models of flexible manufacturing systems (FMS) of interest automatically.
stations, an automatic material transporting vehicle, fourteen common storages for parts waiting for a required available machine tool, and a central tool base and a tool robot. The track of the vehicle can be chosen as a line or a ring. In order to create a simulation model of an FMS under study, it needs the user to provide the relevant information about the system to be modeled. The information includes the simulation model construction specification and experiment specification. It is obtained via a man - machine interactive dialog. The information is recorded in PASCAL and is ready for the generator to use. With this information, the model generator creates a model framewark and an experiment framework, then a modelling prescription is obtained, by combining the two framewarks, and converted into a target simulation language: extended GPSS- F. That is the resulting simulation model of the FMS (see Fig. 1). Then the
The structure of the FMS under study consists of several machine tools, the number of which can be chosen from 2 to 4, two load and unload
220
available machine tool to work on it. (9) To simulate a machine tool which is busy of working on a part. (0) To simulate a part which is leaving a machine tool after the machine tool has operated a process of the part. (1) To simulate a part which is leaving the simulation system after all processes of the part have been done. (2) To provide a control mechanism to terminate the running system. (3) To provide statistical results relevant to a specified performance evaluation measure. (4) To simulate a machine tool breakdown during the simulation system running. 05) To provide the information, if required by the user, of the shortest time interval heuristically for a part flow coming into the system to avoid the system running into a blockage. (6) To be capable of running the system in piecewise, if required, referring to the total simulation time. (17) To simulate a tool exchange from the central tool base to a machine tool.
program can be compiled and executed. All of those things are done automatically. The extended GPSS - F is the GPSS - F simulation language extended by the authors of this paper from original 69 routines to 97 routines, and provides a much more powerful simulation capabilities for modelling manufacturing systems. The intelligent simulation model generator ineludes a rule base which incorporates knowledge of both the FMS and the target language: extended GPSS - F with general simulation modelling knowledge. The generator uses that knowledge to build the simulation model of the FMS. The generator is surrounded by a larger simulation software environment which can be elassfied as a cooperative simulation and expert system (O'keefe,1986), and is used to study of the behavior of the FMS under various control and schedule policies, and performance evaluation with different system performance measures.
MODELLING SPECIFICATION ACQUISITION
FUNCTIONS OF THE FMS SIMULATION MODELS
The objectives of the model generator are to provide a model framework and an experiment framework, according to the modelling specification acquired via a menu interface, then combine and convert it from FORTRAN into the extended GPSS - F by a converter, resulting a required FMS simulation model automatically. In order to make the intelligent model generator work, it requires modelling specification from the user. The specification acquisition and programming tasks can be obtained automatically. There are three alternatives for the specification acquisition of a simulation model to be built, i. e. a natural language processor, a graphics interface, and an interactive dialog interface.
The FMS simulation models are a data - driven and have a modularized structure. It is written in extended GPSS - F. GPSS - F is a discrete event simulation language which can be used for modelling various discrete event simulation systems. There are no need for special text and no limitations on types of computers to be used. As mentioned earlier, simulation models of the FMS with the structure described before are constructed by the intelligent model generator automatically. The block diagram of the simulation models are shown in Fig. 2 and Fig. 3. The simulation models to be generated have to possess the capabilities to perform the following functions. (1) To manage and control, by a time table, events and activities which are generated in the simulation system. (2) To provide various priority control policies. (3) To provide a variety of system performance evaluation measures and schedule policies for user to choose. (4) To initialize the simulation program. (5) To create a part flow in an identical time interval which is specified by the user via a menu selection. (6) To simulate a machine tool which is available to operate a process of a part. (7) To simulate a machine tool which is setting up to work on a part. (8) To offer a storage for a part which is waiting in a queue for an appropriate
The intelligent model generator presented here implements the automation of the discrete simulation model construction processes. The specification acquisition is performed interactively through an input menu which is contained in the simulation software environment. Once the modelling specification has been defined, the model can be created automatically by the model generator. In general, to conduct an experimental study of the behavior of FMS simulation models requires the knowledge of the FMS under study, the knowledge of how to handle the statistical out221
puts, the knowledge of how the simulation and the real FMS behave, as well as the knowledge of the extended GPSS - F simulation language. The intelligent model generator requires the user to know a little of the above. In building a simulation model of the FMS, all the user needs to do is to follow the instructions, provided by the input menu, and answer questions or make selections. So that it is not necessarily for users to know modelling processes of the system. The menu interface for modelling specification acquisition eliminates the need for a long interactive dialog. This advantage is achieved due to that the necessary information given through the interactive dialogs or menu selections is sufficient for modelling specification. In addition to this, the menu interface for the acquisition of the modelling specification is simple and flexible to manipulate, and easy to understand. It is a user -friendly interface.
search and match mechanizm, a model framewark is created. Then the model framework combines an experiment framework to form a modelling prescription. The experiment framework is obtained through a interpret. It implements a transformation of the experimental specification contained in the input information into an internal program code. The prescription is then converted into an appropriate extended GPSS - F structure by a converter, that means the converter selects appropriate extended GPSS - F construction blocks from the model base, and links them together in a right order corresponding to the prescription, so that a full FMS simulation model is built automatically. In fact, the converter plays a role for managing specification data and the development of the complete simulation model of the FMS. THE SIMULATION SOFTWARE The intelligent model generator is concluded in a simulation software environment. The simulation software is used for studying and analyzing the behavior of the FMS, evaluating the system performance, providing an optimal control and schedule policy and a schedule plan dynamically.
MODEL CONSTRUCTION PROCESS The model generator consists of three parts: part one is a model base, part two is a IFTHEN rule base and part three is a control and search mechanism. The model base contains 77 construction blocks which are used to construct different simulation models of the FMS according to the part processes specification and the FMS structure and configuration.
The simulation software consists of five units (see Fig. 4), i. e. an input menu, an output unit and an animation unit except the automatic model generator and the FMS simulation model which were presented already.
The rules encluded in the rule base of the knowledge- based model generator are derivated from simulation modelling and the extended GPSS- F knowledge. The process of generating an FMS simulation model framework can be viewed as a series of operations of array (G,Z). The operations of the array can be formularized as follows: (G(i-1) ,Z(i-1) ,Ni) = => (G(i-1)+gi ,Z(i-1)+zi),
The input menu provides interactive dialogs for collecting input data or acquirring modelling specification and making selections of system performance evaluation criteria and control and schedule policies. The output unit provides the simulation results which are shown in different ways: numericalIy, graphically such as in bar charts and GANTT diagram, and in a form of tables. It also gives an explanation about the optimal result obtained.
(1)
i=1,2,3,4,5;G(i-1), Z(i-1), Ni, gi, zi are integers. G (j - 1) and Z (j - 1) are known and represent the quantities of different kind of blocks chosen from the model base, which are concerned with the amount of processing sequences to be operated for (i -1) types of parts, Ni represents the number of processing sequences of the i - th part type, gi and zi are to be found and related to Ni. To perform the above function IF - THEN rules are used. When a rule in the rule base is fired through a
The animation unit can be used as a simulation aid to assist the user better understanding of the behavior of the system concerned. It displays changes which occure in the state of entities in the simulation system with the advance of the simulation clock when the system runs. The animation unit can also be used to plan a system layout.
222
FEATURES OF THE SIMULATION SOFTWARE
ing to the real system. ACKNOWLEDGMENTS
The features of the FMS simulation software described above can be summarized briefly as the followings. (1) Simulation models of the FMS under study are generated automatically through the intelligent model generator. (2) The simulation software can be used for studying and anaIyzing the behavior of the FMS under various control and schedule policies and evaluating the system performance with different performance criteria. (3) It has a user friendly interface. including input menu selections and result explanations. The manipulation of the input menu is simple. flexible and easy to understand. (4) It is capable of simulating a machine tool breakdown in the FMS and providing a dynamic schedule plan. (5) It is able to offer an optimal result concernc."
This work was supported financially by the Academy of Electronic Science and Research in China. REFERENCES Joseph. M. Mellichamp and A. R. Venkatkchalam (1990). An interactive debugging expert system for GPSS/H simulation models. Simulation.81. 13-19. Liu. Fuyan and Shaoyi Lu (1989). Views on expert simulation systems. Systems Simulation. 8. 19-22. Liu. Fuyan and Shaoyi Lu (1991). A software package: GFTCP for simulation modelling of FMS. In S. Hu and S. Jiang (Ed.). in Proceedings of International Conference on Information and Systems. Vo!. 1. International Academic Publishers. BEIJING. pp. 311-319. Lu. Shaoyi and Fuyan Liu (1990). Knowledge representation and its application in manufacturing. Modular Machine Tool &. Automatic Manufacturing .194.38 - 42. Lu. Shaoyi and Fuyan Liu (1991). An automatic generator for modelling an FMS. In S. Hu and S. Jiang (Ed.). in Proceedings of International Conference on Information and Systems. Vo!. 3. International Academic Publishers. BEIJING. pp. 1237 -1240. Forsyth. Richard (1984). Expert systems principles and case studies. Chapman and Hall. LONDON. O·keefe. Robert (987). Simulation and expert systems - A taxonomy and some examples. Simulation.~ 10-16. Schmidt. B. (1980). GPSS - FORTRAN. J. Wiley and Sons. N. Y. Shannon. Robert. E. Ricgard Alayer and Heimo H. Adelsberger (1985). Exper systems and simulation. Simulation. 44. 275-284.
CONCLUSION As a summary. a knowledge - based simulation model generator has been developed in this paper. It is used to create simulation models of FMS under study automatically. Both the simulation model construction process and the functions of the FMS simulation models have been described. The knowledge- based model generator is surrounded by a larger simulation software environment including an input menu interface which provides information about modelling specification to the model generator. The function of each unit envolved in the software and the features of the simulation software are also presented. In the end. it should be pointed out that the software needs to be improved. For example. system performance measures all are single - objectives. but multi - objectives are more useful in practice; the schedule control of tools in the tool base is rather simplified compar223
INITIALIZE THE SYS TEM
INTERACTIVE D1ALOGS
RESUL T OUTPUT
Y
GOAL SELECTOR SIMULATION MODEL A PART FLOW
Fig. I. ~ription of the model generator.
GENERATOR IDENTIFY APART
INPUT DATA
~
START FROM THE LAST RUN?
PRIORITY CONTROL Y
WAITING IN A QUEUE THE SYSTEM
MAIN BODY OF
READ IN DATA
A PART LEA YES
THE MODEL
THE SYSTEM STATISTICAL
DATA FILES
STOP Fig. 2. Block diagram of the simulation model. Fig. 3. Block diagram of the main body of the model.
ANIMATION UNIT
Fig. 4. The simulation software structure.
224
Liu Fuyan and Lu Shaoyi Hangzhou Institute of Electronic Engineering, Hangzhou, Zhejiang Province, China. Post Code 310037. Abstrct. Usually systems modelling can be time consuming. But a simulation model construction can be automated using artificial intelligence and expert systems tools and techniques. In this paper we present an intelligent model generator which is an implementation of automatic programming techniques. It is used to create simulation models of flexible manufacturing systems under study automatically. The model generator uses the information acquired via an interactive dialog interface to generate a model framework and experiment framework, then a converter envolved in the generator convert it into a target language structure that is the resulting simulation model of the system. The generator is contained in a simulation software environment which is used for study of the behavior of the FMS under various control and schedule policies, and performance evaluation with different system performance measures. In this paper modelling specification acquisition and model construction process are described. And the simulation software is also presented briefly. t
Keywords. Artificial intelligence I expert systems; flexible manufacturing systems; machine tools; modelling; simulation.
INTRODUCTION Systems modelling can be time consuming. Traditionally, a simulation model would be built by means of a general- purpose programming language or a special - purpose simulation language. Then the model would be validated and executed to allow experimentation. But a simulation model construction can be automated using artificial ihtelligence (AI) and expert systems tools and techniques. One approach to the simulation model construction employs automatic programming techniques. The intelligent model or program generator presented here is an implementation of this approach and is written in FORTRAN. The intelligent model generator is used to construct simulation models of flexible manufacturing systems (FMS) of interest automatically.
stations, an automatic material transporting vehicle, fourteen common storages for parts waiting for a required available machine tool, and a central tool base and a tool robot. The track of the vehicle can be chosen as a line or a ring. In order to create a simulation model of an FMS under study, it needs the user to provide the relevant information about the system to be modeled. The information includes the simulation model construction specification and experiment specification. It is obtained via a man - machine interactive dialog. The information is recorded in PASCAL and is ready for the generator to use. With this information, the model generator creates a model framewark and an experiment framework, then a modelling prescription is obtained, by combining the two framewarks, and converted into a target simulation language: extended GPSS- F. That is the resulting simulation model of the FMS (see Fig. 1). Then the
The structure of the FMS under study consists of several machine tools, the number of which can be chosen from 2 to 4, two load and unload
220
available machine tool to work on it. (9) To simulate a machine tool which is busy of working on a part. (0) To simulate a part which is leaving a machine tool after the machine tool has operated a process of the part. (1) To simulate a part which is leaving the simulation system after all processes of the part have been done. (2) To provide a control mechanism to terminate the running system. (3) To provide statistical results relevant to a specified performance evaluation measure. (4) To simulate a machine tool breakdown during the simulation system running. 05) To provide the information, if required by the user, of the shortest time interval heuristically for a part flow coming into the system to avoid the system running into a blockage. (6) To be capable of running the system in piecewise, if required, referring to the total simulation time. (17) To simulate a tool exchange from the central tool base to a machine tool.
program can be compiled and executed. All of those things are done automatically. The extended GPSS - F is the GPSS - F simulation language extended by the authors of this paper from original 69 routines to 97 routines, and provides a much more powerful simulation capabilities for modelling manufacturing systems. The intelligent simulation model generator ineludes a rule base which incorporates knowledge of both the FMS and the target language: extended GPSS - F with general simulation modelling knowledge. The generator uses that knowledge to build the simulation model of the FMS. The generator is surrounded by a larger simulation software environment which can be elassfied as a cooperative simulation and expert system (O'keefe,1986), and is used to study of the behavior of the FMS under various control and schedule policies, and performance evaluation with different system performance measures.
MODELLING SPECIFICATION ACQUISITION
FUNCTIONS OF THE FMS SIMULATION MODELS
The objectives of the model generator are to provide a model framework and an experiment framework, according to the modelling specification acquired via a menu interface, then combine and convert it from FORTRAN into the extended GPSS - F by a converter, resulting a required FMS simulation model automatically. In order to make the intelligent model generator work, it requires modelling specification from the user. The specification acquisition and programming tasks can be obtained automatically. There are three alternatives for the specification acquisition of a simulation model to be built, i. e. a natural language processor, a graphics interface, and an interactive dialog interface.
The FMS simulation models are a data - driven and have a modularized structure. It is written in extended GPSS - F. GPSS - F is a discrete event simulation language which can be used for modelling various discrete event simulation systems. There are no need for special text and no limitations on types of computers to be used. As mentioned earlier, simulation models of the FMS with the structure described before are constructed by the intelligent model generator automatically. The block diagram of the simulation models are shown in Fig. 2 and Fig. 3. The simulation models to be generated have to possess the capabilities to perform the following functions. (1) To manage and control, by a time table, events and activities which are generated in the simulation system. (2) To provide various priority control policies. (3) To provide a variety of system performance evaluation measures and schedule policies for user to choose. (4) To initialize the simulation program. (5) To create a part flow in an identical time interval which is specified by the user via a menu selection. (6) To simulate a machine tool which is available to operate a process of a part. (7) To simulate a machine tool which is setting up to work on a part. (8) To offer a storage for a part which is waiting in a queue for an appropriate
The intelligent model generator presented here implements the automation of the discrete simulation model construction processes. The specification acquisition is performed interactively through an input menu which is contained in the simulation software environment. Once the modelling specification has been defined, the model can be created automatically by the model generator. In general, to conduct an experimental study of the behavior of FMS simulation models requires the knowledge of the FMS under study, the knowledge of how to handle the statistical out221
puts, the knowledge of how the simulation and the real FMS behave, as well as the knowledge of the extended GPSS - F simulation language. The intelligent model generator requires the user to know a little of the above. In building a simulation model of the FMS, all the user needs to do is to follow the instructions, provided by the input menu, and answer questions or make selections. So that it is not necessarily for users to know modelling processes of the system. The menu interface for modelling specification acquisition eliminates the need for a long interactive dialog. This advantage is achieved due to that the necessary information given through the interactive dialogs or menu selections is sufficient for modelling specification. In addition to this, the menu interface for the acquisition of the modelling specification is simple and flexible to manipulate, and easy to understand. It is a user -friendly interface.
search and match mechanizm, a model framewark is created. Then the model framework combines an experiment framework to form a modelling prescription. The experiment framework is obtained through a interpret. It implements a transformation of the experimental specification contained in the input information into an internal program code. The prescription is then converted into an appropriate extended GPSS - F structure by a converter, that means the converter selects appropriate extended GPSS - F construction blocks from the model base, and links them together in a right order corresponding to the prescription, so that a full FMS simulation model is built automatically. In fact, the converter plays a role for managing specification data and the development of the complete simulation model of the FMS. THE SIMULATION SOFTWARE The intelligent model generator is concluded in a simulation software environment. The simulation software is used for studying and analyzing the behavior of the FMS, evaluating the system performance, providing an optimal control and schedule policy and a schedule plan dynamically.
MODEL CONSTRUCTION PROCESS The model generator consists of three parts: part one is a model base, part two is a IFTHEN rule base and part three is a control and search mechanism. The model base contains 77 construction blocks which are used to construct different simulation models of the FMS according to the part processes specification and the FMS structure and configuration.
The simulation software consists of five units (see Fig. 4), i. e. an input menu, an output unit and an animation unit except the automatic model generator and the FMS simulation model which were presented already.
The rules encluded in the rule base of the knowledge- based model generator are derivated from simulation modelling and the extended GPSS- F knowledge. The process of generating an FMS simulation model framework can be viewed as a series of operations of array (G,Z). The operations of the array can be formularized as follows: (G(i-1) ,Z(i-1) ,Ni) = => (G(i-1)+gi ,Z(i-1)+zi),
The input menu provides interactive dialogs for collecting input data or acquirring modelling specification and making selections of system performance evaluation criteria and control and schedule policies. The output unit provides the simulation results which are shown in different ways: numericalIy, graphically such as in bar charts and GANTT diagram, and in a form of tables. It also gives an explanation about the optimal result obtained.
(1)
i=1,2,3,4,5;G(i-1), Z(i-1), Ni, gi, zi are integers. G (j - 1) and Z (j - 1) are known and represent the quantities of different kind of blocks chosen from the model base, which are concerned with the amount of processing sequences to be operated for (i -1) types of parts, Ni represents the number of processing sequences of the i - th part type, gi and zi are to be found and related to Ni. To perform the above function IF - THEN rules are used. When a rule in the rule base is fired through a
The animation unit can be used as a simulation aid to assist the user better understanding of the behavior of the system concerned. It displays changes which occure in the state of entities in the simulation system with the advance of the simulation clock when the system runs. The animation unit can also be used to plan a system layout.
222
FEATURES OF THE SIMULATION SOFTWARE
ing to the real system. ACKNOWLEDGMENTS
The features of the FMS simulation software described above can be summarized briefly as the followings. (1) Simulation models of the FMS under study are generated automatically through the intelligent model generator. (2) The simulation software can be used for studying and anaIyzing the behavior of the FMS under various control and schedule policies and evaluating the system performance with different performance criteria. (3) It has a user friendly interface. including input menu selections and result explanations. The manipulation of the input menu is simple. flexible and easy to understand. (4) It is capable of simulating a machine tool breakdown in the FMS and providing a dynamic schedule plan. (5) It is able to offer an optimal result concernc."
This work was supported financially by the Academy of Electronic Science and Research in China. REFERENCES Joseph. M. Mellichamp and A. R. Venkatkchalam (1990). An interactive debugging expert system for GPSS/H simulation models. Simulation.81. 13-19. Liu. Fuyan and Shaoyi Lu (1989). Views on expert simulation systems. Systems Simulation. 8. 19-22. Liu. Fuyan and Shaoyi Lu (1991). A software package: GFTCP for simulation modelling of FMS. In S. Hu and S. Jiang (Ed.). in Proceedings of International Conference on Information and Systems. Vo!. 1. International Academic Publishers. BEIJING. pp. 311-319. Lu. Shaoyi and Fuyan Liu (1990). Knowledge representation and its application in manufacturing. Modular Machine Tool &. Automatic Manufacturing .194.38 - 42. Lu. Shaoyi and Fuyan Liu (1991). An automatic generator for modelling an FMS. In S. Hu and S. Jiang (Ed.). in Proceedings of International Conference on Information and Systems. Vo!. 3. International Academic Publishers. BEIJING. pp. 1237 -1240. Forsyth. Richard (1984). Expert systems principles and case studies. Chapman and Hall. LONDON. O·keefe. Robert (987). Simulation and expert systems - A taxonomy and some examples. Simulation.~ 10-16. Schmidt. B. (1980). GPSS - FORTRAN. J. Wiley and Sons. N. Y. Shannon. Robert. E. Ricgard Alayer and Heimo H. Adelsberger (1985). Exper systems and simulation. Simulation. 44. 275-284.
CONCLUSION As a summary. a knowledge - based simulation model generator has been developed in this paper. It is used to create simulation models of FMS under study automatically. Both the simulation model construction process and the functions of the FMS simulation models have been described. The knowledge- based model generator is surrounded by a larger simulation software environment including an input menu interface which provides information about modelling specification to the model generator. The function of each unit envolved in the software and the features of the simulation software are also presented. In the end. it should be pointed out that the software needs to be improved. For example. system performance measures all are single - objectives. but multi - objectives are more useful in practice; the schedule control of tools in the tool base is rather simplified compar223
INITIALIZE THE SYS TEM
INTERACTIVE D1ALOGS
RESUL T OUTPUT
Y
GOAL SELECTOR SIMULATION MODEL A PART FLOW
Fig. I. ~ription of the model generator.
GENERATOR IDENTIFY APART
INPUT DATA
~
START FROM THE LAST RUN?
PRIORITY CONTROL Y
WAITING IN A QUEUE THE SYSTEM
MAIN BODY OF
READ IN DATA
A PART LEA YES
THE MODEL
THE SYSTEM STATISTICAL
DATA FILES
STOP Fig. 2. Block diagram of the simulation model. Fig. 3. Block diagram of the main body of the model.
ANIMATION UNIT
Fig. 4. The simulation software structure.
224