PESIGN OF AN INTERACTIVE SIMULATION TOOL FOR AUTOMATIC GENERATION AND EXECUTION OF A SIMULATION PROGRAM USING SIMAN/' ,, A Thesis Presented to The Faculty of the College of Engineering and Technology Ohio University In Partial Fulfillment of the Requirements for the Degree Master of Science Iyer Krishnan Jyothi Lakshmi - / March 1993 LIST OF ILLUSTRATIONS FIGURE PAGE 2.1 Expert System in Simulation Modeling 08 5.1 Block diagram of the conceptualized system 39 5.2 Building Blocks for typical manufacturing systems 41 5.3 Single server, single queue 44 5.4 Multiple servers, single queue per server 44 5.5 Single station with parallel servers,single queue 45 5.6 Multiple stations, parallel servers, single queue 45 5.7 Operation sequence for system with no inspection 46 5.8 Operation sequence for inherent inspection 47 5.9 Operation sequence for independent inspection 48 6.1 Structure Charts for SIMTRAN (a), (b), (c) 5 1 6.1 Structure Charts for SIMTRAN (d) (e) 52 6.2 General Flow Chart for program 53 6.3 Specific Flow Chart of developed software 57 6.4 Facility Layout for sample problem 62 7.1 Single server,single queue system for verification 66 8.1 Bar Chart the overall time taken per subject 76 8.2 Bar chart indicating the average time taken for 77 each task by all subjects 8.3 Bar Chart indicating the subject ratings for the software 78 ACKNOWLEDGMENTS I wish to express my deepest appreciation and gratitude to Dr. H.T Zwahlen for willing to be my advisor and for the help and constructive criticisms provided by him towards its successful completion. My sincere acknowledgements also go to Dr.Nur Ozdemirel who provided me with the opportunity to begin on this research with her and guided me through its conceptualization. I also wish to thank Dr. C.M Parks and Dr. C. Vassiliadis for their co-operation and for having consented to be on the thesis committee. A special thanks to Dinesh Dhamija for promptly providing the computer and lab facilities. I would like to express my heartfelt gratitude to Murlidar Ramakrishnan and my brother Venkatesh for the timely support and encouragement. A special thanks to all friends, especially Kay Reeves, and all faculty who have made these past three years so memorable. Finally, I express my deepest gratitude to my parents whose patience and continuous support has been the cornerstone for all my achievements in academia or otherwise. ABSTRACT Manufacturing simulation used in the modeling and experimentation of a prototype requires a high level of expertise to achieve the desired task. This research proposes a method of configuring existing generic simulation models in manufacturing through an user-friendly and interactive interface between the non-expert user and the simulation language. Such a system could be a powerful, cost effective tool that provides the non-specialist access to simulation and the specialist increased productivity. TABLE OF CONTENTS CHAPTER PAGE Acknowledgments Abstract List of Illustrations List of Tables 1. INTRODUCTION 2. LITERATURE REVIEW 2.1 Simulation Software Development 2.2 Towards "Integrated Simulation Environments" 2.3 Various Approaches to K.B Simulation 2.4 Ergonomics of Interactive Interfaces 2.5 Summary 3. SYSTEM REQUIREMENTS AND ASSUMPTIONS 3.1 Simulation Model Assumptions 3.2 Hardware and Software Requirements 3.2.1 Requirements for a user 3.2.2 Requirements for developer 3.3 User Requirements 3.4 Input Requirements 3.5 Output Requirements 3.6 Other Requirements 4. DESIGN ALTERNATIVES 4.2 User Levels 4.4 Simulation Languages 4.4.2 Simscript 11.5 4.5 Programming Languages 5. SYSTEM APPROACH 5.1 Research Goal 5.2 Approach Outline 5.2.1 Environment for Simulation Model Generation 5.2.2 Conceptualized System for Model Generation 5.2.3 Deriving a Simple Manufacturing Subset 5.2.4 Implementing the System 5.3 System Configuration Approach 5.3.1 Station Configuration 5.3.2 Operation Sequence Configuration 6. SOFTWARE DESIGN 6.1 Software Design Criteria 6.2 Software Organization 6.2.1 Create Module 6.2.2 Edit Module 6.2.3 Generate Module 6.2.4 Simulate Module 6.3 Software Ergonomics Used in Design 6.3.1 Input Error Handling 6.3.2 Simulation Error Handling 6.3.3 Use of Color Codes in Input Screens 6.4 Sample Problem 7. TESTING AND EVALUATION 7.1 Verification of the software 7.1.1 Verification of generic model 7.1.2 Verification of the translator module 7.1.3 Verification of user interface 7.1.4 Verification of the integrated package 7.1.5 Verification of simulation results 7.2 Validation of the software 8. RESULTS AND DISCUSSION 8.1 Equivalence of Results 8.1.1 Results for single server problem 8.2 Analysis of Results 9. CONCLUSIONS AND RECOMMENDATIONS BIBLIOGRAPHY ...... APPENDIX A. User's Guide B. IS0 Recommendations for color codes in Displays C. Validation Data sheets, Instructions, Questionnaire D. Verification Data using SIMAN & SIMTRAN E. Program Source Code F. Raw Data Sheets from subject tests CHAPTER ONE INTRODUCTION Schriber defined computer simulation as the modeling of a process or a system that mimics the response of an actual system to events that take place over time. Since complex systems can be both objective and subjective, different analyses of the same process can conceptualize it into different systems and environment. It is therefore possible, for several models to exist simultaneously. However, this requires : definition of an explicit mathematical or logical model, an efficient and effective computer program, and, an experimental procedure to obtain results which can predict system behavior under various conditions. Thus, simulation modeling must be considered as both an experimental and applied methodolow. Simulation plays an important role in the design and management of the manufacturing and/or service industry. It allows for visual modeling of a prototype on a computer, well before the implementation of an actual system. The modeler has to identify the simulation objects, the various entities, resources, and their data structures to form the conceptualized system. Although a demanding task, simulation has proven to be highly cost effective in industries. However, in order to use simulation techniques correctly and proficiently, the user must have expertise in several fields viz., modeling, computer programming, probability, statistics and heuristic methods in addition to real world practical experience. Consequently, simulation has received low acceptance in industry. Simulation models can be either symbolic or iconic. Symbolic models are represented by either higher level languages (viz., FORTRAN,PASCAL, BASIC) or general purpose simulation languages (viz., GPSS, GASP, SIMAN, 2 SIMSCRIPT). Iconic models are represented by integrated packages called simulators (viz.,SIMFACTORY, PROMODEL, WITNESS, SLAM). Although simulation has grown in popularity in manufacturing, it is still being under utilized to either modify existing facilities under extreme conditions of loss or emergency. Ideally it should be used in the design and planning of a new facility before its actual implementation in order to eliminate bottlenecks and to optimize the use of system resources. This would help in proofing out plans for long term profitability through simulation, for a very small percentage of the cost, as opposed to altering existing facilities. However, the fact that simulation has not yet been universally adopted is indicative of certain limitations that may be present in currently used simulation approaches. Two primary reasons that have been attributed towards this lack of acceptability are: 1) the considerable level of expertise required for its use and, 2) the prohibitively long lead times where decisions have to be made promptly. Although simulators allow simulation models to be developed using only menus and graphs, with little or no programming, they are still application-specific due to constraints in their system configuration. Hence the need for a simulation approach which has the simplicity of a simulator and flexibility of a simulation language, allowing the design of reusable models instead of "throw away" ones. With the advent of artificial intelligence(A1) in the 70's, research in this direction dramatically increased. A1 has in turn opened new disciplines of which expert systems (ES) is the most successful of them all. On examining the various taxonomy for combining ES and simulation (O'Keefe, 1986), three significant applications were recognized. These were, the development of: (1) simulation tools for combining existing simulation and knowledge-based methods. (2) systems for giving advice to inexperienced modelers in the area of experimentation and analysis. (3) intelligent user interfaces for existing simulation packages. 3 Recently, with the recognition of similarity between simulation and expert systems and the increasing ability of computers in symbolic processing, simulation languages and packages are being replaced with the concept of a "simulation environment". A "simulation environment" is a collection of tools, that are well- integrated and interacting synergistically in support of all phases of the modeling process (Reilly, Jones, Dey, 1989). Its original framework was introduced by Henrikson (1983) using distributed processing architecture and consisting of a: builder, record keeper, results analyzer and model executor. Attempts to apply A1 techniques to the complex area of simulation would provide both the non- specialist and the specialist with access to simulation. The Intelligent Simulation Interface (ISI) software combines the concepts of a simulation environment with effective interactive graphics for the construction and running of discrete event simulation models. It currently provides user interface to SIMAN through a user-specified graphical model which is later used for model definition, code generation and post-processing of results. Yet another application is ISIM (Intelligent Simulation) developed by Bozenhardt, Eads and Etra in 1990. ISIM provides the ability for any process engineer to draw a process flow diagram and specify parameters to begin dynamic simulation without any knowledge of computer or programming skills.