Founded 1905 OPTIMIZATION FOR PROCESS PLANNING AND SCHEDULING IN PARTS MANUFACTURING WANG YIFA (B.Eng., M.Eng., HUST) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF MECHANICAL ENGINEERING NATIONAL UNIVERSITY OF SINGPAORE 2010 ACKNOWLEDGEMENTS I would like to sincerely thank my supervisors Professor Jerry Fuh Ying Hsi and Associate Professor Zhang Yunfeng, from the Department of Mechanical Engineering at the National University of Singapore, for their knowledge, guidance, and help throughout my doctoral studies. My gratitude has far exceeded what words can express. I would also like to thank the National University of Singapore for providing the research scholarship to support my doctoral studies. I also wish to thank Associate Professor A. Senthil Kumar and Assistant Professor Subramaniam Velusamy for their comments and suggestions during my qualifying exams. My gratitude also goes to all the fellows in LCEL for their encouragement and creating a pleasant research environment. I also want to thank all the friends for their support and care. Last, but not least, I would like to express my hearty gratitude to my family, for their love and constant support that sustained me through this critical stage of career. i TABLE OF CONTENTS ACKNOWLEDGEMENTS . i TABLE OF CONTENTS ii SUMMARY . vi LIST OF TABLES . ix LIST OF FIGURES . xi LIST OF ABBREVIATIONS xiii CHAPTER INTRODUCTION . 1.1 Computer-aided Process Planning (CAPP) 1.2 Scheduling 1.3 Rescheduling 1.4 Integration of CAPP and Scheduling . 1.5 Research Motivation 1.6 Research Objectives . 10 1.7 Organization of the Thesis . 11 CHAPTER A PSO-BASED OPTIMIZATION ALGORITHM FOR CAPP . 13 2.1 Background 13 2.2 Literature Review . 14 2.3 Problem Modelling . 20 2.3.1 Problem description 20 2.3.2 Objective function . 23 2.4 A PSO-based Optimization Algorithm 24 ii 2.4.1 Solution representation . 25 2.4.2 Population initialization 27 2.4.3 Fitness function . 28 2.4.4 The search algorithms . 29 2.4.5 PSO parameter settings . 33 2.4.6 PSO based algorithm for process planning problem . 34 2.5 Numerical Experiment and Comparisons 34 2.5.1 Process planning case study 36 2.5.2 Comparison between PSO-LSII and an exact search algorithm . 38 2.5.3 PSO-LSI vs. PSO-LSII vs. PSO vs. SA . 39 2.6 Summary 41 CHAPTER A PSO ALGORITHM TO MINIMIZE THE TOTAL TARDINESS FOR FELIXIBLE JOB SHOP SCHEDULING 43 3.1 Problem Statement . 43 3.1.1 Problem formulation . 43 3.1.2 Disjunctive graph model . 44 3.2 Related Works 47 3.3 The PSO-based Algorithm for FJSP 50 3.3.1 Solution representation . 51 3.3.2 Solution decoding and transformation 53 3.3.3 Initialization and fitness function 55 3.3.4 Local search 56 3.3.5 An integrated PSO algorithm for the FJSP . 61 3.4 Computational Results . 61 3.5 Summary 68 iii CHAPTER AN INTEGRATED PROCESS PLANNING AND SCHEDULING SYSTEM…………………………………………………………………………………70 4.1 Related works . 71 4.1.1 The iterative approach . 72 4.1.2 The simultaneous approach . 73 4.1.3 Discussion . 73 4.2 System Overview . 74 4.3 System Implementation 77 4.4 Summary 80 CHAPTER REDUCING JOB TARDINESS THROUGH THE INTEGRATED SYSTEM…………………………………………………………………………………82 5.1 Problem Definition . 83 5.2 Heuristic based Algorithms for Constraint Generation 84 5.3 Discussion 88 5.4 An Application Example 88 5.5 Numerical Experiments and Comparisons . 92 5.6 Summary 94 CHAPTER JOB RESCHEDULING BY EXPLORING THE SOLUTION SPACE OF PROCESS PLANNING AND SCHEDULING . 95 6.1 Introduction 96 6.2 Problem Definition . 100 6.3 The Re-process Planning and Re-scheduling Systems 102 6.3.1 Re-process planning for ARJS 103 6.3.2 Re-scheduling for ARJS . 104 6.4 Overview of the Rescheduling System 109 6.5 Experimental Results 110 iv 6.5.1 Machine breakdown 111 6.5.2 New job arrival . 116 6.5.3 A comparative study . 123 6.6 Summary 128 CHAPTER A PSO-BASED MULTI-OBJECTIVE OPTIMIZATION APPROACH TO THE IPPSP …………………………………………………………………………130 7.1 Introduction 131 7.2 Basic Concepts in Multi-objective Optimization . 133 7.3 PSO-based Multi-objective Optimization for the IPPSP . 134 7.3.1 Solution representation . 136 7.3.2 Population initialization 137 7.3.3 An external archive . 138 7.3.4 Updating the personal best and global best solutions . 139 7.3.5 Pruning the external archive . 140 7.3.6 Local search exploitation 141 7.3.7 Crossover algorithm 142 7.3.8 A PSO-based algorithm for multi-objective IPPSP 144 7.4 Case Study and Discussion 145 7.5 Summary 148 CHAPTER CONCLUSIONS AND FUTURE WORK . 149 8.1 Conclusions 150 8.2 Future Work . 155 REFERENCES . 157 RELEVANT PUBLICATION LIST 170 v SUMMARY This thesis studies the optimization for an integrated process planning and scheduling system in the job shop batch manufacturing. The objective is to generate a satisfactory plans/schedule solution such that the tardiness of the schedule is minimized and the cost of process plans is maintained at a near minimum level. On the other hand, two types of commonly occurred disruptions including machine breakdown and new order arrival are also investigated and accommodated through the developed approach. Firstly, in process planning, two optimization algorithms are proposed to automatically generate the optimal process plan with minimum machining cost. The process planning problem for manufacturing prismatic parts is defined as to simultaneously consider operation methods selection and sequencing. A feasible solution representation scheme to enable the continuous particle swarm optimization (PSO) in this discrete problem is proposed. Moreover, the strategy to enhance the search quality is also investigated. Numerical experiments and a comparative study are conducted to validate the efficiency and effectiveness of the proposed algorithms. Secondly, a search algorithm is proposed to find the optimal schedule for a flexible job shop scheduling problem to minimize the total tardiness. A disjunctive graph model is used to represent and analyze the problem. For adapting the PSO in this scheduling problem, a unique solution representation scheme is proposed. Furthermore, a tabu search algorithm is developed and integrated with the PSO to perform the exploitation search so as to avoid entrapment into a local optimum. In the tabu search, vi effective neighbourhoods are defined and a variant length of tabu list is utilized. Experimental results are conducted to validate the effectiveness, efficiency, and robustness of the proposed algorithms. Thirdly, the problem of integrating process planning and scheduling is addressed. The objective is to find a good trade-off plans/schedule solution in terms of minimum total tardiness and total machining cost. Two optimization approaches are proposed to solve this problem. The first one is based on the idea of linking the process planning and scheduling with an integrator module. Iterative improvement is then performed between these two functions by intelligently modifying the process plan solution space of the tardy jobs and re-generating the respective process plans. The solution space of process plans for the tardy jobs are thus explored to achieve a better plans/schedule solution. The second one is to develop a multi-objective optimization algorithm to perform an exploration search on the solution space of process planning and scheduling by incorporating various optimization techniques. Solutions obtained by these two approaches are then compared with each other. Fourthly, a new rescheduling approach to accommodate the disruptions of machine breakdown and new job arrival is developed. The rescheduling problem is modelled by considering the status of jobs at the point of disruption. Subsequently, the reprocess planning and re-scheduling algorithms are respectively developed. Several application examples as well as the comparative studies are performed to demonstrate the effectiveness of this rescheduling approach. vii Finally, an integrated process planning and scheduling system incorporating the proposed algorithms is developed based on multi-tier system architecture, taking the advantage of flexibility, scalability, reusability, and interoperability. viii LIST OF TABLES Table 1.1 Disruption types Table 2.1 Available machines and tools . 36 Table 2.2 The PP problem information of Part32 37 Table 2.3 An optimal solution to the Part32 . 37 Table 2.4 Performance comparison between PSO-LSII and ESSA 39 Table 2.5 Performance comparison between PSO-LSII , PSO-LSI, PSO, and SA . 41 Table 3.1 A solution representation for a schedule with jobs and machines . 52 Table 3.2 Data set for the (8×8) case with partial flexibility 62 Table 3.3 Data set for the (10x10) case with full flexibility . 63 Table 3.4 The equation of priority calculation for a list of dispatching rules . 64 Table 3.5 The results for the (8×8) case with different tightness factors . 67 Table 3.6 The results for the (10×10) case with different tightness factors . 67 Table 5.1 Available machines and cutters 88 Table 5.2 Job information . 89 Table 5.3 Solution space modification of Job7 . 89 Table 5.4 Numerical experimental results 93 Table 6.1 Job information . 111 Table 6.2 Status of jobs and OPTs after M2 breaks down 112 Table 6.3 Completion time and tardiness of jobs in the 0-level solutions 112 Table 6.4 Modification for Cases I and II in the machine breakdown . 113 ix Chapter Conclusions and recommendations scalability, reusability, and interoperability. With this system, users in geographically dispersed departments are able to cooperate with each other in a distributed, transactional, and portable environment. 8.2 Future Work Several limitations might exist in this research and the future work is suggested as follows: (1) The current integrated process planning and scheduling system developed can only handle the disruptions of machine breakdown and rush order. This, however, may not be sufficient in practice, since production shop floor is highly complex and filled with different kinds of disruptions, which would greatly disturb the production. Therefore, to make the developed system more useful in the real manufacturing, more disruptions may need to be investigated in future work. However, due to the adaptability and extendibility of the developed system, it is believed that only minor work is needed on modifying the “pre-processing” part and adding a new rule in the integrator module in the rescheduling system. In addition, with the requirement of fast response to the occurred disruptions, the technique to reduce the algorithm computational time will be expected. (2) Since the proposed search algorithms based on particle swarm optimization and local search have fully utilized the capability of the exploration and exploitation search, much computational resource is needed to find a satisfactory solution, thereby causing its slow computational speed even with the high performance 155 Chapter Conclusions and recommendations workstations. Future research on parallel distributed computing should be studied to shorten the computational time. (3) The scheduling model in our problem incorporates several assumptions, which could limit its practical use in real-time applications, although they are all typical assumptions in the literature. To enhance the applicability, future research can attempt to relax some assumptions. For example, the setup time and transferring time between two subsequent operations are taken into consideration. (4) Due to the complex solution space of integrated process planning and scheduling problem, the efficiency and quality to achieve the final solution using the simultaneous multi-objective optimization algorithm still need further improvement. (5) This study mainly focuses on improving the production through the integration of process planning and scheduling. In reality, the design process and shop floor control, which are respectively performed before the process planning and after the scheduling, have also a significant impact on the quality of obtained process plans and schedule. Effective interaction and communication among these functions could lead to a better product design and achieve higher production efficiency. 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Comput. Eng. Networks Lab. (TIK), Swiss Federal Institute Technology (ETH), Zurich, Switzerland, Technical Report 103. 169 RELEVANT PUBLICATION LIST Journals Wang J., Zhang Y.F., Nee A.Y.C., Wang Y.F. and Fuh J.Y.H., 2009. Reducing tardy jobs by integrating process planning and scheduling functions, International Journal of Production Research, 47(21), 6069 - 6084. Wang Y.F., Zhang Y.F., and Fuh J.Y.H., Job rescheduling by exploring the solution space of process planning for machine breakdown/arrival problems, Proceedings of the Institute of Mechanical Engineering, Part B, Journal of Manufacturing Engineering. (Accepted for publication) Wang Y.F., Zhang Y.F., and Fuh J.Y.H., A hybrid particle warm based method for process planning optimization, International Journal of Production Research. (Accepted for publication) Wang Y.F., Zhang Y.F., and Fuh J.Y.H., A multi-agent process planning and scheduling system, IEEE Transaction on Automation Science and Engineering. (Under review) Conferences Wang Y.F., Zhang Y.F., Fuh J.Y.H., Zhou Z.D., Lou P. and Xue L.G., 2008. An integrated approach to reactive scheduling subject to the machine breakdown, Proceedings of the IEEE Int. Conf. on Automation and Logistics, 542-547. Wang Y.F., Zhang Y.F., Fuh J.Y.H., Zhou Z.D., Xue L.G., and Lou P., 2008. A web-based integrated process planning and scheduling system, IEEE Int. Conf. on Automation Science and Engineering, Washington DC, USA, 662-667. Wang Y.F., Zhang Y.F., and Fuh J.Y.H., 2009. Using hybrid particle swarm optimization for process planning problem, IEEE Int. Conf. on Computational Science and Optimization, 204-308. Wang Y.F., Zhang Y.F., and Fuh J.Y.H., 2009. An agent-based distributed process planning and scheduling system, 2nd Int. Symposium on Digital Manufacturing, Sep 10-11, Wuhan, China. Wang Y.F., Zhang Y.F., and Fuh J.Y.H., 2010. A PSO-based Multi-objective Optimization Approach to the Integration of Process Planning and Scheduling, IEEE Int. Conf. on Control & Automation, 614-619. 170 [...]... propose optimization algorithms for the process planning based on a realistic process planning model (2) To propose an efficient scheduling algorithm for the flexible job shop scheduling with the objective of minimizing the total tardiness (3) To develop an integrated process planning and scheduling approach to improve the tardiness of the generated schedule, while maintaining the lower machining cost for. .. IPPSP Integrated Process Planning and Scheduling Problem xiii IPPS Integrated Process Planning and Scheduling approach JNI Java Native Interface JSSP Job Shop Scheduling Problem JVM Java Virtual Machines LS Local Search M Machine MC Machine Cost MCC Machine Change Cost MCCI Machine Change Cost Index MCI Machine Cost Index MOO Multi-Objective Optimization MVC Model-View-Controller NLPP Non-Linear Process. .. integrating the process planning and scheduling owns the following characteristics Firstly, as both process planning and scheduling are NP-hard combinatorial optimization problems, the integrated problem by combing the solution space of these two functions will own a substantially large search space, thus significantly increasing the problem complexity Secondly, the objectives in the process planning. .. sets for an in- processing job 104 Figure 6.4 Improve the tardiness by reducing the OPM waiting time 105 xi Figure 6.5 The overview of the rescheduling process 110 Figure 6.6 Tardiness after each iteration in Case I in the machine breakdown 114 Figure 6.7 Tardiness after each iteration in Case II in the machine breakdown 115 Figure 6.8 Total machining cost for Cases I and II in the machine... 2007 Jain et al 1997, Subramaniam et al 2005 Integration of CAPP and Scheduling In parts manufacturing, CAPP acts as a bridge between computer-aided design (CAD) and computer-aided manufacturing (CAM) CAD is used for generating the 3D part design and the parts specification information, which serve as the input for CAPP Subsequently, CAPP is invoked to generate a process plan composed of determining the... the process planning and scheduling The results obtained by multi-objective optimization algorithm are compared with those obtained by the integrated process planning and scheduling approach Chapter 8 draws the conclusion by highlighting the contributions of the work and providing some recommendations for the future work 12 CHAPTER 2 A PSO-BASED OPTIMIZATION ALGORITHM FOR CAPP An effective process planning. .. in this work is to model a realistic process planning problem and then design an effective optimization algorithm to find a high-quality plan 2.1 Background In process planning, a part is usually described with a set of features, having geometric forms with machining meanings, such as holes, slots, and bosses Given a part and a set of manufacturing resources in the shop, an effective process planning. .. and scheduling are not necessary in line, which are both important to a manufacturing enterprise and thus should be considered simultaneously During the last two decades, the optimization method for the integrated process planning and scheduling problem has received significant research attention and thus resulted in a large number of reported integration systems (Tan and Khoshnevis 2000) These efforts... algorithms in the process planning and scheduling functions should be separately addressed At the National University of Singapore, an integrated approach for process planning and scheduling has been developed to effectively balance the machine utilization for the generated schedule (Zhang et al., 2003) In this study, the work will 9 Chapter 1 Introduction focus on developing an effective integration... machine breakdown), leading to the existing schedule not applicable It is becoming increasingly realized that the predominant scheduling activity in the real world is reactive scheduling (Raheja and Subramaniam 2002) Therefore, an effective scheduling system must be able to react quickly to accommodate these disturbances and revise the existing schedule in a cost-effective manner Rescheduling is the process . Graphic User Interface IMOEA Incrementing MO Evolutionary Algorithm IPPSP Integrated Process Planning and Scheduling Problem xiv IPPS Integrated Process Planning and Scheduling approach. 100 6.3 The Re -process Planning and Re -scheduling Systems 102 6.3.1 Re -process planning for ARJS 103 6.3.2 Re -scheduling for ARJS 104 6.4 Overview of the Rescheduling System 109 . competitive market. This chapter introduces the CAPP and scheduling functions in discrete parts manufacturing. The issues on rescheduling and integration of CAPP and scheduling are also highlighted.