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Pattern-Recognition Scheduling PATTERN-RECOGNITION SCHEDULING A Thesis presented to The Faculty of the Fritz J. and Dolores H. Russ College of Engineering and Technology Ohio University In Partial Fulfillment of the Requirements of the Degree Master of Science -, . ? X.' by 1 ; , 5.2: Xiaoqiang Yao November, 1996 Table of Contents Abstract Chapter 1 Introduction 1.1 Overview 1.2 Research Objective and Development 1.3 Thesis Organization Chapter 2 Literature Review 2.1 Mathematical Programing and Analytical Models 2.2 Heuristics, Dispatching Rules and Digital Computer Simulation 2.3 Artificial Intelligence Based Methodologies 2.3.1 Knowledge-based Expert Systems 2.3.2 FuqLogic 2.3.3 Machine Learning 2.3.4 Genetic Algorithms 2.3.5 Artificial Neural Networks 2.4 Pattern Recognition and Neural Networks Chapter 3 System Architecture 3.1 System Overview 3.1.1 Assessment Module 3.1.2 Data Preprocessing Module 3.1.3 Pattern Recognition/Decision MakingIOptimization Module 3.2 Dispatching Rules and Performance Measures 3.2.1 Definition of Symbols and Terms 3.2.2 Definition of Selected Dispatching Rules 3.2.3 Definition of Selected Performance Measures 3.3 Backpropagation Paradigm 3.4 Genetic Algorithms as Schedule Optimizers 3.5 Summary Chapter 4 Case Study 1: Single-machine Scheduling Problem 4.1 Description of the Problem 4.2 Data Acquisition and Analysis 4.2.1 Job Data 4.2.2 Performance Measures 4.2.3 Scheduling Rules 4.3 Data Preparation for Pattern recognition Neural Networks 4.4 Implementation of the Expert neural Network Rule Selector 4.5 Optimization with Genetic Algorithms 4.6 Analysis of the Results Chapter 5 Case Study 2: Multiple-machine Scheduling Problem 5.1 Description of the Problem 5.2 Data Acquisition and Analysis 5.2.1 Job Data 5.2.2 Performance Measures 5.2.3 Scheduling Rules 5.3 Data Preprocess for Neural Networks 5.4 Implementation of the Network Scheduler 5.5 Analysis of the Results Chapter 6 Conclusions 6.1 Conclusions 6.1 Issues and Future Works References Appendix A Program Lists Appendix B Sample Test Data for Single-machine problem Appendix C Sample Test Data for Multiple-machine problem List of Tables Table 3- 1 GA - population initialization 42 Table 3-2 GA - reproduction of superior strings 43 Table 3-3 GA - New population after one generation 44 Table 3-4 GA - reproduction of superior strings 44 Table 3-5 GA - GA - results after two generations 4 5 Table 4- 1 Process plans 49 Table 4-2 Setup time matrix 50 Table 4-3 A sample job batch 50 Table 4-4 Performance measure groups 52 Table 4-5 Neural network configuration and training specilications 56 Table 4-6 Simulation results of 15 scheduling rules for the example 57 Table 4-7 Results of 7 neural network rule selectors for the example 57 Table 4-8 Results of the genetic optimizer for the example 60 Table 4-9 Comparison of the overall performance among scheduling rules, 61 neural networks, and genetic algorithm for 100 samples Table 5- 1 Process plans for multiple-machine system 6 6 Table 5-2 A sample job batch 69 Table 5-3 Neural network configuration 77 Table 5-4 Simulation results of 9 rules and neural network selection 77 Table 5-5 Performance of scheduling rules and neural networks 7 8 List of Figures Figure 3- 1 The general codguration of the intelligent scheduling system 25 Figure 3-2 Module for objective definition and data acquisition 26 Figure 3-3 Data preprocessing module 2 7 Figure 3-4 Module for scheduling pattern-recognition 29 Figure 3-5 A typical feedforward neural network 37 Figure 3-6 Crossover process 43 Figure 4- 1 A single-machine system 4 7 Figure 4-2 Linear representation of (pt + st) in SPT order 54 Figure 4-2 Linear representation of (pt + st) in FIFO order 55 Figure 5- 1 A 10-machine FMS system 64 Figure 5-2 Performance of rules for MFT 79 Figure 5-2 Performance of rules for MTD 79 Abstract The interest in the use of artificial neural networks (ANNs) to sohre engineering optimization problems have been growing at a substantial pace in recent years. This mainly owes to ANNs' ability to mimic human intelligence and hence making ANNs a more robust technique in the decision making in a dynamic environment. The emphasis in this study is try to find an approach that is intelligent and flexible enough to handle the real- time scheduling requirements in a dynamic manufacturing environment, with shorter response time. An artificial neural network based pattern-recognition approach for real- time scheduling of production system is studied and a scheduling system which integrates artscial neural networks, dispatching rules, real-time simulation and genetic algorithms has been developed. In this system, art5cial neural networks, with their ability of learning and generalization, are used to make a predictive selection of a small set of candidate scheduling policies from a larger set of heuristics dynamically at a decision point without searching through the solution space exhaustively. Genetic algorithms are then applied to take this selected set of rules as part of the "seed" rules to generate a single &a1 "best" schedule, this schedule may be totally different from any of the root schedules. The approach has been applied, with some variance, in two cases: (1) single-machine scheduling problem with sequence dependent setup times; (2) a multiple-machine scheduling problem. The simulation results in both cases for different performance measures demonstrated that the neural network based integrated system performed better than any dispatching rule alone. Artificial neural networks, when appropriately built, do possess promising potentials in solving real-time production scheduling problems at an intelligent level, which traditional scheduling theories and techniques have not been able to :provide. Chapter 1 Introduction 1.1 Overview In a manufacturing system, the construction of a good schedule is often the primary means of achieving major system performance goals such as reducing costs and increasing productivity. Unfortunately, scheduling of manufacturing systems is a problem of well known complexity. In recent years, flexible manufacturing systems (FMS) have played an increasingly important role in manufacturing environments. Featured by flexibility and adaptability, FMS's have presented new challenges to the scheduling of manufacturing systems. They require that the scheduling system must operate in real-time with a short response time and be intelligent enough to handle the dynamic requirement changes and reflect both the flexibility and adaptability of these manufacturing systems. Over the years, many scheduling theories and techniques have been studied and developed. Among the traditional techniques, dispatching rules are used the most in scheduling manufacturing systems, and the selection of the most suitable rule for a given situation is mainly carried out by computer simulation. However, the performance of different scheduling rules are very dependent on the criteria selected as well as on the current state of the production system, and the selection of a suitable rule using computer simulation presents enormous practical difllculties to their real-time application because of the tremendous computational efforts involved and the time needed, especially in large problems and when there are many rules to search (Conway 1965, Baker 1974, Graves 1981, Blackstone 1982, Shaw 1990, Doctor 1993, Matruura 1993, etc.). Recently, the developments of artificial neural networks have provided some innovative alternatives to 3 attack the scheduling problems for dynamic manufacturing systems with certain new potential which traditional techniques have been unable to provide (Wu 1985, Arizono 1992, Chryssolouries 1992, Yih and Jones 1992, Rabelo 1992, 1993, etc.). The focus of this research is on the application of artificial neural networks and pattern recognition technique, as well as the implementation of the concept of a integrated system for the scheduling of dynamic manufacturing systems. Artificial neural networks are in essence the mapping of a set of inputs to a set of outputs based on certain mapping relationships encoded in its structure. They tend to capture in a black box the general relationships between inputs and outputs that are difficult or impossible to be represented by any analytical model (Chryssolouries et al., 1992). Zn many researches on artificial neural networks and their application to various engineering optimization problems, artificial neural networks have shown several major advantages over traditional methods: The ability to learn fiom past experience and generalize the knowledge learned, through closed-loop interaction with the system and its environment. This gives neural network the ability to derive good results even there exists certain level of noise in the input data. Faster than simulation in terms of execution speed. Do not need exhaustive search to reach satisfactory result. Do not need to represent mathematically the general relationship between the inputs and outputs. These unique features make artificial neural networks an appealing technique because they are intrinsically parallel and could in principle be used to explore solutions for large, complicated combinatorial engineering optimization problems. This discovery has raised great interests in the potential application of these techniques in the scheduling of dynamic manufacturing systems by developing intelligent, robust, real-time schedulers. 4 Among the many neural network paradigms, backpropagation neural networks have been successfblly applied in the fields of pattern recognition and classification, and have been considered for solving a variety of scheduling problems. They can be utilized to recognize patterns of different scheduling situations and make the decision of choosing a suitable scheduling policy (dispatching rule) based on the pattern recognized fiom a larger set of available such rules. Although the training of such networks could take longer time to finish, once a network is properly constructed and trained, it can provide robust performance and more globally optimized results.
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