Table Of Content

FEASIBILITY OF DISTRIBUTED MANUFACTURING OPERATIONS CONTROL By SERDAR KIRLI A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL F THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 1999 ACKNOWLEDGMENTS I would like to thank Dr. Sencer Yeralan for his support, encouragement and friendship. This study would not be possible without his vision and guidance. He has been not only an advisor but also a dear friend. I would like to extend my appreciations to my committee members Dr. Suleyman Tufekci, Dr. John F. Mahoney and Dr. John K. Schueller for their suggestions and comments throughout the study. ii TABLE OF CONTENTS £326 ABSTRACT vi CHAPTERS 1 INTRODUCTION „._ _ _ 1 1.1 Background 1 1.2 Trends in Technology 3 1.3 New Paradigm: Distributed Control 4 1.3.1 Manufacturing Environment: A Complex System 5 1.3.2 Distributed Manufacturing Operations Control 7 1.3.3 Technological Aspects 10 1.3.4 Related Work .._ 13 1.4 Motivation _ „._ 18 1.5 Approach ..„ 20 1.6 Organization 21 2 ELEMENTS OF DISTRIBUTED CONTROL 23 2.1 Embedded Data Processing 23 2.2 Network Communications 28 3 MODELING OF A DISTRIBUTED MANUFACTURING SYSTEM 33 3.1 Overview 33 3.2 Manufacturing Environment 35 3.2.1 Setup 35 3.2.2 Production Goal and Mode of Operation 36 3.2.3 Features of the Manufacturing System 37 3.3 Adaptive Control Policies 42 3.3.1 Control Policy Buffer_Size 43 3.3.2 Control Policy Delay_Time 51 4 SIMULATIONS ...„ _.. 57 4.1 Overview _ _ „ 57 4.2 Static Control Policies _ 58 4.3 Simulation Scenarios 59 4.3.1 Case 1: No Bottleneck (Balanced Line) „ 60 4.3.2 Case 2: Single Mild Bottleneck 61 4.3.3 Case 3: Single Severe Bottleneck 62 4.3.4 Case 4: Variable Bottleneck 62 iii 4.3.5 Case 5: Two Bottlenecks - — 64 4.3.6 Issues Involving Simulation Parameters 65 4.4 Simulations - - 68 4.4.1 Case 1: No Bottleneck (Balanced Line) 69 4.4.2 Case 2: Single Mild Bottleneck _ 75 4.4.3 Case 3: Single Severe Bottleneck 79 4.4.4 Case 4: Variable Bottleneck 83 4.4.5 Case 5: Two Bottlenecks - 87 4.5 E4.v4a.l6uaMtiixoendofCasSeimulation Results — 10902 4.6 Observations on Adaptive Behavior 102 4.6.1 An Example 103 4.6.2 Step Size „ 106 4.6.3 Oscillatory Behavior _ 113 5 DESIGN AND IMPLEMENTATION - 115 5.1 Technology Issues 115 5.1.1 Communication Protocol 116 5.1.2 Embedded Controllers - 119 5.2 Design Issues 120 5.2.1 Physical Components of Implementation 121 5.2.2 Scanner Module — 123 5.2.3 Time Base _ 125 5.2.4 CAN Messages 128 5.2.4.1 Initialization messages 130 5.2.4.2 Production messages 133 5.2.4.3 Data transfer messages 137 5.3 Implementation Issues _ ~ 138 5.3.1 CAN Data Transfer Rate 139 5.3.2 Time Base 139 5.3.3 CAN Message IDs 140 5.3.4 Communication Modes 143 5.3.5 Coding 147 5.4 Emulation Results „ 148 5.5 Observations and General Remarks _..151 5.6 Detailed Remarks on the Implementation of DMOC 152 5.6.1 Issues in Simulating Distributed Systems 152 5.6.2 Observations from the Physical Implementation 154 5.6.2.1 Communication rate 155 5.6.2.2 Time base 155 5.6.2.3 Synchronization 158 5.6.2.4 Embedded code development 162 6 SUMMARY AND CONCLUSIONS 165 APPENDICES A CONTROLLER AREA NETWORK (CAN) „. 168 B A TWO-STATION MARKOV MODEL WITH OUTPUT DELAY ..191 iv C ASSEMBLY AND C SOURCE CODE USED IN THE IMPLEMENTATION 204 REFERENCES ..„ _._.„ „.. „...„ 273 BIOGRAPHICAL SKETCH „ 281 V . Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy FEASIBILITY OF DISTRIBUTED MANUFACTURING OPERATIONS CONTROL By Serdar Kirli August 1999 Chairman: Dr. Sencer Yeralan Major Department: Industrial and Systems Engineering The last two decades have witnessed the emergence of new technologies in coitputer control and inter-computer communications. More recently, embedded controllers have redefined industrial automation through a new generation of "smart" machine tools (i.e., tools with local data processing capabilities ) New engineering technologies most often lead to new engineering approaches and, in turn, to new engineering practices. The focus of this dissertation is on the implications of these emerging technologies on manufacturing operations control. Generally speaking, current industrial engineering practice continue to rely heavily on traditional methods for production control. These methods may be characterized by two common points vi a unicentric view of the physical plant, and the treatment of the system components as passive elements, that is, uninvolved in the decision making process. This study is a trial for a new paradigm, oriented more towards the discovery and evaluation of previously untested ideas. Specifically, it investigates the feasibility of distributed production line operations control where the information processing is locally carried out by networked (communicating) embedded station controllers. The decision making process relies on rule-based local learning from real-time process data. In accordance with the orientation of the study, the decision making process is kept intentionally simple to retain the focus on paradigm development rather than on seeking streamlined policies. Particularly, a minimal set of rules is selected to characterize the behavior of stations and a straightforward learning mechanism is chosen. The work includes a physical implementation and a computer simulation, whose findings are compared. The controller area network (CAN) is selected as the cost-effective communication protocol. A production line comprised of four stations is used as a testbed for the purposes of this study. Evidence is revealed implying that even with simple rule- based learning, a distributed embedded approach achieves production line control performances comparable to the traditional techniques. Moreover, it is demonstrated that such a control system may be implemented using low-cost embedded vii control. The study discovers empirical aspects of distributed production control that present profound justification for future research. Perhaps most importantly, the distributed embedded approach shows promise in cases where system scalability requirements and environmental dynamics are more demanding. viii . CHAPTER 1 INTRODUCTION 1.1 Backgroiond The control of manufacturing systems has been a fundamental field of work in industrial engineering [15,41]. In the last few decades many approaches have been developed to determine the optimum operating parameters of manufacturing systems [23,75]. Reviewing the pertinent industrial engineering literature, one observes a gradual transition from more qualitative approaches to manufacturing operations control (MOC) toward more quantitative and analytical approaches [67] Recent quantitative approaches to MOC have lead to optimization models that are most often solved on a computer in an algorithmic fashion. Paralleling the improvements in data processing technologies, the scale and scope of such models have been increasing. However, there may be limits to such improvements. Although it is possible with today's computers to develop models for large-scale manufacturing systems with hundreds of elements, as a whole, industry tends to use the more simplistic techniques, such as push, pull (kanban), constant work-in-process (CONWIP), and so on. This stems partially from the difficulties in modeling and in verifying the analytical solutions, and the simplifying assumptions inherent in most 1 2 analytical models, such as linearity, time-homogeneity, and deterministic operating disciplines. However, the use of such simplistic approaches does not always allow the manufacturing system to fulfill its potential in efficiency and effectiveness. Presently, production control mainly relies on non-adaptive control strategies such as material requirements planning (MRP) [34], kanban [86], and CONWIP [71]. These have all been developed and have become popular over the last few decades. MRP is a push system where raw materials are pushed into the production line periodically based on demand. Once in the system parts flow toward the output without any control of the downstream stations possibly causing inventory build-ups in front of bottleneck stations. Kanban and CONWIP on the other hand are pull systems, which are geared toward reducing the work-in-process (WIP) inventory. Contrary to the push systems, in pull systems the flow of parts is controlled by the downstream stations. CONWIP is based on the principle that the total inventory in the system remains constant at all times. Hence, a new part can enter the system only after a finished part is removed from the finished goods inventory. To accomplish that a fixed number of cards are assigned to the production line that accompany parts through the system. Similar to CONWIP the material flow in kanban is also regulated with cards. However, in kanban, cards are allocated to each station rather than the entire line. Cards are attached to parts and removed when they move from one station to another. Therefore, the number of cards at a station determines the maximum WIP level for that station.