Điều độ sản xuất động và đa mục tiêu trong môi trường job shop linh hoạt

i ĐẠI HỌC QUỐC GIA THÀNH PHỐ HỒ CHÍ MINH TRƯỜNG ĐẠI HỌC BÁCH KHOA -o0o - TRẦN VÕ THẢO HƯƠNG ĐIỀU ĐỘ SẢN XUẤT ĐỘNG VÀ ĐA MỤC TIÊU TRONG MÔI TRƯỜNG JOB SHOP LINH HOẠT Chuyên ngành: Kỹ thuật Công nghiệp Mã số: 8520117 LUẬN VĂN THẠC SĨ TP HỒ CHÍ MINH, tháng 01 năm 2020 ii VIETNAM NATIONAL UNIVERSITY - HO CHI MINH CITY HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY -o0o - TRẦN VÕ THẢO HƯƠNG MULTI-OBJECTIVE PREDICTIVE-REACTIVE PRODUCTION SCHEDULING IN DYNAMIC FLEXIBLE JOB SHOP ENVIRONMENT Major: Industrial Engineering Major Index: 8520117 MASTER’S THESIS HO CHI MINH CITY, January 2020 iii Cơng trình hồn thành tại: Trường Đại học Bách Khoa – ĐHQG-HCM Cán hướng dẫn khoa học: PGS.TS Lê Ngọc Quỳnh Lam Chữ ký: Cán chấm nhận xét 1: TS Đường Võ Hùng Chữ ký: Cán chấm nhận xét 2: TS Nguyễn Hữu Thọ Chữ ký: Luận văn thạc sĩ bảo vệ Trường Đại học Bách Khoa - Đại học Quốc gia Thành phố Hồ Chí Minh ngày 05 tháng 01 năm 2020 Thành phần Hội đồng đánh giá luận văn thạc sĩ gồm: Chủ tịch hội đồng: TS Nguyễn Vạng Phúc Nguyên Thư ký hội đồng: TS Nguyễn Văn Thành Ủy viên Phản biện 1: TS Đường Võ Hùng Ủy viên Phản biện 2: TS Nguyễn Hữu Thọ Ủy viên hội đồng: PGS.TS Đỗ Ngọc Hiền Xác nhận Chủ tịch Hội đồng đánh giá Luận văn Trưởng Khoa quản lý chuyên ngành sau luận văn sửa chữa CHỦ TỊCH HỘI ĐỒNG TRƯỞNG KHOA CƠ KHÍ TS Nguyễn Vạng Phúc Nguyên PGS.TS Nguyễn Hữu Lộc iv ĐẠI HỌC QUỐC GIA TP.HCM CỘNG HÒA XÃ HỘI CHỦ NGHĨA VIỆT NAM TRƯỜNG ĐẠI HỌC BÁCH KHOA Độc lập - Tự - Hạnh phúc NHIỆM VỤ LUẬN VĂN THẠC SĨ Họ tên học viên: TRẦN VÕ THẢO HƯƠNG Ngày, tháng, năm sinh: 02/08/1995 Chuyên ngành: Kỹ thuật Công nghiệp MSHV: 1870027 Nơi sinh: TP.HCM Mã số: 8520117 I TÊN ĐỀ TÀI: - Tiếng Việt: Điều độ sản xuất động đa mục tiêu môi trường Job shop linh hoạt - Tiếng Anh: Multi-Objective Predictive-Reactive Production Scheduling in Dynamic Flexible Job Shop Environment II NHIỆM VỤ VÀ NỘI DUNG: - Xây dựng mơ hình tốn điều độ sản xuất môi trường Job shop linh hoạt - Đề xuất giải pháp cho toán để thỏa mãn mục tiêu đặt ban đầu - Đánh giá giải pháp đề xuất III NGÀY GIAO NHIỆM VỤ: Ngày 11 tháng 02 năm 2019 IV NGÀY HOÀN THÀNH NHIỆM VỤ: Ngày 08 tháng 12 năm 2019 V CÁN BỘ HƯỚNG DẪN: PGS.TS LÊ NGỌC QUỲNH LAM Tp HCM, ngày 05 tháng 02 năm 2020 CÁN BỘ HƯỚNG DẪN CHỦ NHIỆM BỘ MÔN ĐÀO TẠO PGS.TS Lê Ngọc Quỳnh Lam PGS.TS Đỗ Ngọc Hiền TRƯỞNG KHOA CƠ KHÍ PGS.TS Nguyễn Hữu Lộc v ACKNOWLEDGEMENTS I would like to express my special deep and sincere gratitude to my research advisor Assoc.Prof PhD Le Ngoc Quynh Lam and my lecturer Assoc.Prof PhD Do Ngoc Hien, Head of Department of Industrial Systems Engineering, Ho Chi Minh city University of Technology for giving me the opportunity to pursue Master degree and providing in time and invaluable support throughout this research Their guidance and constructive suggestions during planning and development process helped me to go on the right direction toward completion of research I am also particularly grateful to all academic staff of Department of Industrial Systems Engineering and members of Faculty of Mechanical Engineering for their kind and understanding spirit and their encouragement in carrying out this research Last but not the least, my sincere thank goes to my family for supporting me spiritually throughout my academic path as well as my life TRẦN VÕ THẢO HƯƠNG vi TÓM TẮT Sự phát triển nhanh ngành công nghiệp sản xuất thời đại ngày đặc biệt ảnh hưởng Cách mạng Công nghiệp 4.0, dẫn đến việc tổ chức sản xuất doanh nghiệp ngày hệ thống, tinh gọn, hiệu trở thành yếu tố định để tạo lợi cạnh tranh thị trường Trong phát triển đó, điều độ sản xuất trở thành chức quan trọng giúp phân bổ hiệu nguồn lực tổ chức trình sản xuất đồng thời rút ngắn thời gian sản xuất, đảm bảo thời hạn cam kết với khách hàng Nghiên cứu đặt luận văn hướng đến việc đề giải pháp thích hợp để giải vấn đề điều độ sản xuất môi trường Job shop linh hoạt ảnh hưởng yếu tố ngẫu nhiên, bất định Hai mục tiêu giải pháp cần hướng đến đồng thời bao gồm cực tiểu thời gian hồn thành tất cơng việc (makespan) cực tiểu thời gian trễ lớn công việc Để giải vấn đề, phương pháp điều độ predictive-reactive sử dụng Đầu tiên điều độ tĩnh xem xét nhằm đưa lịch điều độ cụ thể với điều kiện đầu vào có sẵn Nếu dừng lại bước này, lịch điều độ tạo triển khai xưởng sản xuất bị gián đoạn trở nên khơng cịn khả thi với xuất yếu tố bất định biến thiên thời gian thực công đoạn hay kiện bất ngờ thêm công việc mới, hư hỏng máy, nguyên vật liệu cung cấp trễ Vì vậy, chiến lược tái điều độ đề xuất giúp làm giảm bớt ảnh hưởng yếu tố lên lịch điều độ, đồng thời phải giữ cho việc sản xuất ổn định, tránh thay đổi liên tục Các kết tính tốn cho thấy giải thuật điều độ tĩnh chiến lược tái điều độ đề xuất đáp ứng mục tiêu đề ra, có tính hiệu cao áp dụng cho doanh nghiệp thực tế Từ khóa: Điều độ sản xuất, Job shop linh hoạt động, đa mục tiêu, điều độ predictivereactive, tái điều độ theo kiện, tái điều độ theo chu kì vii ABSTRACT These days, production scheduling becomes one of the most crucial functions in the production organizations with its supports in effectively resource consuming as well as completion time minimization This thesis focuses mainly on finding out a predictivereactive solution for the flexible job shop problem in a dynamic manufacturing environment Two objectives considered simultaneously during the scheduling process are minimizing completion time and the maximal tardiness Firstly, a heuristic algorithm is proposed in order to generate a predictive schedule with using all the on-hand input information In the real-world manufacturing, when implementing the predictive schedule in the shop floor, uncertain elements and unexpected events are unavoidable, cause disruptions and impact on the schedule performance Therefore, a rescheduling strategy is mentioned in the reactive scheduling stage to help reduce the negative impacts and remain the shop floor stability The hybrid method combining between event-driven rescheduling and periodic rescheduling is used in order to deal with the critical problem in this stage that is defining the time to reschedule The computational results indicate that the heuristic algorithm proposed for predictive scheduling and the rescheduling strategy proposed for reactive process are satisfied with research objectives, gives the high effectiveness and can be applied to real organizations of production field Keywords: Production scheduling, flexible job shop environment, multi-objective problem, uncertainties, unexpected event, predictive-reactive scheduling, rescheduling viii REASSURANCE I hereby declare that this is my own research All the data and the results used in this research is honest and has not been published in other studies I will be totally responsible for my research if it is incorrect as mentioned above The research’s author TRẦN VÕ THẢO HƯƠNG ix TABLE OF CONTENTS ACKNOWLEDGEMENTS v TÓM TẮT .vi ABSTRACT vii TABLE OF CONTENTS .ix LIST OF TABLES xii LIST OF FIGURES xiii LIST OF NOTATIONS xiv CHAPTER INTRODUCTION 1.1 Background and Motivation 1.2 Objectives 1.3 Scope 1.4 Methodology 1.4.1 Formulating the problem model 1.4.2 Proposing the algorithm for the predictive scheduling problem 1.4.3 Proposing the algorithm for the reactive scheduling problem 1.4.4 Evaluation and revision 1.5 Contributions 1.6 Organization of the Thesis CHAPTER LITERATURE REVIEW 2.1 Production Scheduling Overview 2.2 Production Scheduling Problem Model 2.2.1 Production Environment 2.2.2 Processing Characteristics and Constraints 2.2.3 Objective Function 2.3 Deterministic scheduling algorithm 10 2.4 Predictive-reactive scheduling 11 x CHAPTER DETERMINISTIC PROBLEM FORMULATION 13 3.1 Problem description 13 3.2 Mathematical model formulation 15 3.2.1 Notations 15 3.2.2 Objective function 15 3.2.3 Decision variables 16 3.2.4 Constraints 17 3.2.5 Mathematical model 18 CHAPTER PREDICTIVE SCHEDULING 19 4.1 Proposed heuristic algorithm 19 4.1.1 The main procedure of proposed algorithm 20 4.1.2 Operation selection rule 21 4.1.3 Machine selection rule 23 4.2 Comparison of dispatching rules 23 4.3 The detailed steps of proposed algorithm 27 4.4 Numerical example 29 CHAPTER REACTIVE SCHEDULING 33 5.1 Overall 33 5.2 Rescheduling strategy 34 5.2.1 Rescheduling policy 34 5.2.2 Rescheduling method 35 5.3 Event evaluation 37 5.3.1 Defining the latest completion time of operation 37 5.3.2 Event classification method 41 5.3.3 Example on each type of event 42 CHAPTER COMPUTATIONAL RESULTS 50 6.1 Design of experiments 50 6.2 Experiment results 52 52 According to that, 15 experiments are generated with the detail information of those experiments is shown in Appendix C This study evaluates the proposed reactive scheduling based on these experiments Some parameters for the experiments are set as follows: − The rescheduling period is set at T = 48 This can be explained that a time unit in the experiments can be considered as one hour In the real world, a shop floor is often operated under constant period like one day, one week or two week or a month And the working time of an organization is commonly days per week and hours per day Hence, T = 48 corresponds to one-week period in reality − The average tightness among 100 initial jobs is calculated at 2.30 − The accepted maximum tardiness is set at 32 Hence, β value is defined by the deviation between accepted value and the current maximum tardiness value − Because the makespan can be changed due to job addition or job cancellation (change of load), there is no constant accepted makespan and it will be updated after two above events Hence, α value is constantly set at 10 6.2 Experiment results The result gained from hybrid policy application is compared to those gained by applying periodic policy and event-driven policy − With periodic policy, at the beginning of each scheduling period, the rescheduling process will be implemented if there is at least one event in group or group occured before Otherwise, the fixed-sequence method is applied − With event-driven policy, once an event in group or group occurs, the rescheduling process will be implemented immediately Otherwise, the fixedsequence method is applied The efficiency of algorithm is evaluated via two following criteria: − The objective function value (F = 0.5Cmax + 0.5Tmax ): RPD (Relative Performance Deviation in Equation 4.6) parameter is also used in this case to compare the effectivenesses of these approaches The value illustrates that that policy made the best (the smallest) objective function value among three policies 53 − The number of rescheduling times (nre ) A small nre decribes that the shop floor is more stable The results are shown in Table 6.2 Table 6.2 The results of experiments Event-driven Case Periodic Hybrid 𝐅 𝐑𝐏𝐃 𝐧𝐫𝐞 𝐅 𝐑𝐏𝐃 𝐧𝐫𝐞 𝐅 𝐑𝐏𝐃 𝐧𝐫𝐞 E01 227.5 2.02 228.0 2.24 223.0 0.00 E02 241.0 0.00 246.5 2.28 245.5 1.87 E03 247.5 0.00 250.5 1.21 251.5 1.62 E04 231.0 5.00 223.0 1.36 220.0 0.00 E05 234.0 3.31 11 226.5 0.00 227.0 0.22 E06 233.0 0.00 241.0 3.43 240.0 3.00 E07 242.0 2.33 11 246.0 4.02 236.5 0.00 11 E08 239.5 1.91 235.0 0.00 235.0 0.00 E09 239.5 5.97 226.0 0.00 232.0 2.65 E10 238.5 0.00 242.0 1.47 242.0 1.47 E11 246.0 5.35 240.0 2.78 233.5 0.00 E12 237.5 0.00 241.0 1.47 239.5 0.84 E13 235.0 0.00 241.5 2.77 235.0 0.00 E14 227.0 0.00 233.0 2.64 237.5 4.63 E15 242.5 1.25 244.5 2.09 239.5 0.00 Average 1.810 1.851 1.087 Figure 6.1 shows the RPD value among three policies applying on 15 experimetns, while Figure 6.2Figure 6.2 The number of rescheduling times comparison between policies compares the number of rescheduling times between them It is illustrated that although there were 7/15 experiments event-driven policy gave the best solution with the smallest objective function value F, the high average value of RPD shows that its solutions are so far from the best solution in the other experiments Besides, event-driven policy rescheduled many times, that may make the shop floor unstable Periodic policy almost makes a least number of rescheduling times but the relative performance about objective function is less than Hybrid policy has the number of rescheduling times in the medium 54 level but its average relative performance deviation of objective value is the smallest In almost experiments, hybrid policy gave the solution that is better than or nearer the best solution than others Figure 6.1 RPD comparison between three rescheduling policies Figure 6.2 The number of rescheduling times comparison between policies From the results on the sample instance, the proposed approach for predictive-reactive scheduling in this study shows that it is effective with the objective of minimizing both makespan and maximum tardiness in flexible job shop scheduling 55 CHAPTER CONCLUSIONS AND RECOMMENDATIONS 7.1 Conclusions In this research, the problem considered is production scheduling in the dynamic flexible job shop environment with two objectives that are minimzing the makespan and the maximum tardiness The predictive-reactive scheduling method was applied to deal with the problem With predictive scheduling, previous research on deterministic flexible job shop scheduling often skipped the release time values of jobs and the initial available time values of machines, that made the rescheduling process difficult to deal with updating the shop floor condition This research begun with proposing a heuristic algorithm using ODD dispatching rule for operation selection process and (WINQ+RPT+PT)×PT rule for machine selection process Its outperformed effectiveness was proven through comparing with some other dispatching rules and the short computational time After that, a rescheduling strategy was proposed for reactive process in order to cope with uncertainties and unexpected events during the schedule application on shop floor Each occuring event is classified into one in three groups based on its impact on the initial schedule, then a proper reaction will be applied corresponding with each group The result on some experiments illustrated that the proposed rescheduling strategy is more effective than periodic method and event-driven method based on performance deviation and stability of the schedule (evaluating via the number of rescheduling times) 7.2 Recommendations This research can be applied effectively in real-world production due to its high reality and ease to operate For the further research, the objective can be changed by considering about operation cost and punish cost for late order In addition, it is necessary to make some efforts to define the proper rescheduling point, that could be right at the point when the significant event occurs or when it is known or another one The time for the scheduler to make a new schedule also need to be considered in the rescheduling problem REFERENCES [1] M L Pinedo, Planning and Scheduling in Manufacturing and Services 2nd edition, New York: Springer, 2009 [2] M L Pinedo, Scheduling Theory, Algorithm, and Systems 5th edition, New York: Springer, 2012 [3] P Kaweegitbundit and T Eguchi, "Flexible job shop scheduling using genetic algorithm and heuristic rules," Journal of Advanced Mechanical Design, Systems, and Manufacturing, vol 10, no 1, 2016 [4] W P Syam and I M Al-Harkan, "Comparison of three meta heuristics to optimize hybrid flow shop scheduling with parallel machines," International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, vol 4, no 267-271, 2010 [5] P Zheng, J Wang, J Zhang, C Yang and Y Jin, "An adaptive CGAN/IRFbased rescheduling strategy for aircraft parts remanufacturing system under dynamic environment," Robotics and Computer Integrated Manufacturing, vol 58, pp 230-238, 2019 [6] G E Vieira, J W Herrmann and E Lin, "Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods," Journal of Scheduling, vol 6, no 1, pp 39-62, 2003 [7] K M M A Bukkur, M I Shukki and O M Elmardi, "A review for dynamic scheduling in manufacturing," The Global Journal of Researches in Engineering, vol 18, no 5, pp 25-37, 2018 [8] I Y Kim and O L de Weck, "Adaptive weighted sum method for multi objective optimization: a new method for Pareto front generation," Structural and Multidisciplinary Optimization, vol 31, pp 105-116, 2006 [9] D Behnke and M J Geiger, "Test Instances for the Flexible Job Shop Scheduling Problem with Work Centers," Germany, 2012 [10] T C E Cheng, "Analytical determination of optimal TWK due-dates in a job shop," International Journal of Systems Science, vol 16, no 6, pp 777-787, 1985 [11] F Geyik and I H Cedimoglu, "The strategies and parameters of tabu search for job-shop scheduling," Journal of Intelligent Manufacturing, vol 15, no 4, pp 439-448, 2004 [12] F Koblasa, "Single swap local search for classical Job Shop and Flexible Job Shop scheduling Problem," in Mezinárodní Baťovy konference pro doktorandy a mladé vědecké pracovníky, Zlín, 2010 [13] A Baykasoğlu, L özbakir and A İ Sönmez, "Using multiple objective tabu search and grammars to model and solve multi-objective flexible job shop scheduling problems," Journal of Intelligent, vol 15, no 6, pp 777-785, 2004 [14] F Glover and E Taillard, "A User’s Guide to Tabu Search," Annals of Operations Research, vol 41, no 1, pp 1-28, 1993 [15] T V T Huong, L N Q Lam and V Q Nhi, "Flexible Job shop scheduling with batch and discrete processing machines," in MMMS, Danang, 2018 [16] N B Ho and J C Tay, "GENACE: an efficient cultural algorithm for solving the flexible job-shop problem," in The congress on Evolutionary Computation, Portland USA, 2004 [17] J C Tay and N B Ho, "Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems," Computers & Industrial Engineering, vol 54, no 3, pp 453-473, 2008 [18] I Kacem, S Hammadi and P Borne, "Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems," IEEE Transactions on Systems, Man, and Cybernetics, vol 32, no 1, pp 1-13, 2002 [19] N A Masruroh, "Adaptive Scheduling Systems: A decision-theoretic approach," National university of Singapore, Singapore, 2009 A1 APPENDIX A CPlex Code for the proposed mathematical model in Chapter tuple Job { int jobId; int rel; int due; }; tuple Mch{ int MchId; int Avail; }; tuple Operation { int opId; int jobId; int pos; }; tuple Mode { int opId; int mch; int pt; }; {Job} Jobs = ; {Mch} Machine = ; {Operation} Ops = ; {Mode} Modes = ; int jlast[j in Jobs] = max(o in Ops: o.jobId == j.jobId) o.pos; //Variables dvar boolean x[Ops][Machine]; dvar boolean y[Ops][Ops]; dvar int s[Ops]; dexpr int c[o in Ops]=(s[o] + (sum(i in Machine, m in Modes: m.opId==o.opId && m.mch==i.MchId) (x[o][i]*m.pt))); dexpr int com[j in Jobs]=max(o in Ops: o.jobId == j.jobId && o.pos==jlast[j]) c[o]; dexpr int T[j in Jobs]= maxl(0, (com[j]-j.due)); dexpr int Cmax = max(j in Jobs, o in Ops: o.pos==jlast[j]) c[o]; dexpr int Tmax = (max(j in Jobs) T[j]); //Object minimize 0.5*Cmax + 0.5*Tmax; //Constraints subject to{ forall(o in Ops) sum(i in Machine,m in Modes:m.opId==o.opId && m.mch==i.MchId) x[o][i]==1; forall(o in Ops) sum(i in Machine) x[o][i]==1; forall(o in Ops) s[o] >= sum(i in Machine) (i.Avail * x[o][i]); forall(o in Ops, j in Jobs: o.jobId==j.jobId) s[o] >= j.rel; forall(o1 in Ops, o2 in Ops: o1.jobId==o2.jobId && o2.pos==o1.pos+1) s[o2]>=c[o1]; forall(a in Ops, b in Ops: a.opId >= b.opId) y[a][b]==0; forall(a in Ops, b in Ops, i in Machine: a.opId < b.opId) (s[a] >= c[b] (2 - x[a][i]-x[b][i]+y[a][b])*10000); forall(a in Ops, b in Ops, i in Machine: a.opId < b.opId) (s[b] >= c[a] (3 - x[a][i]-x[b][i]-y[a][b])*10000); }; B1 APPENDIX B The following is the added information for jobs and machines of the test instance used to evaluate the proposed algorithm in Chapter The used instance is the B60 instance which is gained from the study of Behnke and Geiger [9] with 100 jobs and 60 machines (𝐫: release time; 𝐝: due time; 𝐚: available time) Job 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 r 132 108 100 117 138 199 122 113 123 50 169 97 34 23 123 138 119 91 145 13 182 78 132 Machine a Machine a Machine a Machine a d 325 325 338 377 385 405 386 347 350 287 398 323 241 306 277 356 350 369 309 361 250 431 310 376 215 16 17 31 19 46 19 17 64 32 47 47 20 Job 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 18 94 33 17 48 r 25 177 200 137 79 177 45 65 122 137 19 32 198 42 104 189 67 94 150 104 166 43 23 143 15 19 34 91 49 67 30 20 33 35 67 50 37 d 253 440 441 379 301 404 284 302 345 357 227 276 436 222 328 405 288 317 389 335 404 286 227 394 270 22 21 27 36 48 51 Job 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 31 22 20 37 52 67 17 23 14 38 39 53 12 r 17 27 31 20 52 47 37 106 184 185 106 183 109 127 174 156 63 199 160 127 34 130 168 25 49 24 51 39 17 54 28 d 308 242 279 245 269 339 281 339 417 424 337 416 336 333 426 401 268 242 425 403 331 293 351 401 300 10 55 25 39 40 73 55 37 11 12 26 84 41 12 56 49 Job 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 12 40 27 94 42 74 57 14 r 156 172 190 58 46 80 177 96 192 21 14 33 184 151 37 42 116 95 75 144 13 12 28 87 43 41 58 37 d 265 427 401 264 440 269 284 388 367 238 343 407 242 222 214 286 420 240 355 245 314 348 310 298 327 14 86 29 20 44 59 48 15 30 58 45 82 60 34 C1 APPENDIX C The information of three new job used to generate cases in experiments in Chapter Job Operration 101 102 103 (Doable machine – Processing time) (01-15), (02-23), (03-20), (04-22) (05-21), (06-25), (07-21), (08-12), (09-23), (10-19), (11-10), (12-14), (13-19), (14-30), (15-13), (16-17), (17-29), (18-19), (19-12), (20-17), (29-12), (30-13), (31-27), (32-25), (37-28), (38-19), (39-30), (40-30), (41-29), (42-17), (43-23), (44-17), (53-24), (54-11), (55-12), (56-15) (21-18), (22-23), (23-27), (24-14), (25-25), (26-28), (27-28), (28-13), (29-18), (30-11), (31-15), (32-23), (33-15), (34-27), (35-15), (36-14), (37-30), (38-21), (39-24), (40-14), (41-25), (42-18), (43-16), (44-21), (45-19), (46-18), (47-26), (48-20), (49-24), (50-21), (51-24), (52-28), (53-18), (54-18), (55-26), (57-17) (5-12), (6-30), (7-11), (8-18), (9-21), (10-19), (11-19), (12-15), (1323), (14-15), (15-25), (16-11), (17-21), (18-18), (19-14), (20-16), (2120), (22-26), (23-23), (24-26), (29-11), (30-23), (31-23), (32-14), (3717), (38-24), (39-25), (40-14), (41-20), (42-25), (43-19), (44-28), (4510), (46-12), (47-23), (48-18), (49-24), (50-29), (51-14), (52-15), (5320), (54-20), (55-30), (56-30) (57-29), (58-30), (59-18), (60-23) (1-17), (2-16), (3-18), (4-11) (21-30), (22-23), (23-30), (24-15) (21-22), (22-19), (23-18), (24-23), (33-26), (34-24), (35-24), (36-16), (41-21), (42-13), (43-13), (44-20), (53-25), (54-26), (55-14), (56-27) (5-30), (6-21), (7-18), (8-16), (13-29), (14-18), (15-30), (16-23), (2124), (22-14), (23-26), (24-28), (29-28), (30-11), (31-21), (32-22), (3311), (34-19), (35-12), (36-11), (37-17), (38-27), (39-25), (40-23), (4529), (46-30), (47-13), (48-10) (57-26), (58-13), (59-30), (60-27) (1-27), (2-11), (3-24), (4-10) (37-20), (38-12), (39-16), (40-15), (49-26), (50-13), (51-17), (52-20) (9-20), (10-29), (11-10), (12-14), (13-25), (14-17), (15-22), (16-14), (17-23), (18-15), (19-11), (20-11), (21-30), (22-25), (23-21), (24-16), (25-19), (26-10), (27-20), (28-24), (29-29), (30-16), (31-10), (32-24), (33-15), (34-19), (35-12), (36-29), (37-17), (38-29), (39-26), (40-28), (45-29), (46-16), (47-15), (48-20), (49-11), (50-11), (51-30), (52-30), (53-10), (54-22), (55-10), (56-26) (9-25), (10-12), (11-17), (12-13), (13-23), (14-16), (15-26), (16-10), (17-13), (18-17), (19-22), (20-26), (25-12), (26-11), (27-13), (28-20), (33-11), (34-23), (35-30), (36-21), (37-23), (38-25), (39-19), (40-25), (49-29), (50-13), (51-28), (52-26) (57-28), (58-27), (59-14), (60-30) The notation meaning of attributes of each type of events describe as follows C2 Type Note Machine breakdown Delay in job arrival Delay in machine available time Processing time variation Tightening due time Job cancellation Job addition A Notation B Notation C Notation D Notation E Notation F Notation G Notation Occuration time Occuration time Occuration time Occuration time Occuration time Occuration time Occuration time Attribute Index Machine Repair index time Job Delay index time Machine Delay index time Job Operation index index Job Deviation index Job / index New job Release index time / / / Deviation / / Due time Attribute Index Attribute Index Type No Type No The information of generated cases in Chapter 13 14 15 16 17 18 19 20 21 22 23 24 F D D A D D D D G A E D F1 D7 D6 A2 D2 D4 D8 D15 G1 A1 E1 D10 115 127 202 208 222 237 238 238 266 290 294 313 85 39 85 29 34 65 41 102 60 57 20 5 4 280 14 -12 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 D D G E D D A G A D D D D D D D12 D8 G2 E1 D13 D9 A1 G1 A2 D6 D4 D5 D1 D11 D2 157 162 166 169 184 198 205 208 210 217 235 267 277 332 361 58 102 98 62 74 51 103 16 49 94 70 59 59 80 186 -11 45 247 41 2 4 D01 10 11 12 F B B D D D D E D D D D F2 B1 B2 D12 D11 D9 D13 E2 D3 D1 D5 D14 13 16 17 41 55 66 76 79 87 88 88 102 84 38 90 15 51 60 67 90 75 39 50 99 12 18 4 -15 3 15 16 17 18 13 13 15 13 13 14 10 585 13 D02 10 11 12 13 14 15 16 C B C C D C C C B D D B B F E E C3 B1 C1 C5 D7 C6 C4 C2 B4 D10 D3 B3 B2 F1 E3 E2 14 14 16 22 33 38 41 66 88 92 121 125 140 142 15 93 22 90 38 36 12 56 90 48 24 35 26 23 100 41 40 26 30 32 12 35 30 5 18 17 -13 -21 11 433 453 5 Attribute Index Type Attribute Index No Type No C3 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 D G D D A E D A D G D D D D D D10 G2 D6 D1 A2 E1 D7 A1 D13 G1 D17 D12 D16 D8 D14 149 158 179 180 184 214 236 242 282 282 287 292 295 313 367 98 101 35 40 26 59 29 80 102 18 87 73 69 158 37 -6 11 323 5 11 12 13 14 15 16 17 18 19 20 E B D D D F A D E G E2 B2 D4 D5 D2 F1 A1 D6 E1 G1 83 90 113 113 121 128 135 149 217 248 10 70 82 75 63 76 34 71 84 103 -18 44 3 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 D E B F D D E A D D D D A D D G D D D D D2 E1 B4 F3 D17 D8 E2 A2 D11 D4 D12 D19 A1 D18 D6 G1 D14 D16 D5 D10 115 123 134 137 145 169 180 192 198 206 210 214 216 217 225 265 275 284 307 347 75 38 63 19 49 75 29 100 66 22 28 58 103 87 16 84 77 D03 10 11 12 13 14 15 C F C D D D D D D B D B D E D C1 F1 C2 D18 D5 D4 D3 D2 D15 B2 D11 B1 D19 E2 D9 11 14 29 33 38 50 54 72 78 81 100 106 136 141 143 41 45 54 21 89 53 82 51 35 23 73 10 80 75 26 18 23 -23 2 6 376 4 635 5 D04 10 C C D B B B B B F D C2 C1 D3 B3 B4 B5 B6 B1 F2 D1 23 28 28 31 40 43 55 60 66 75 58 13 32 55 67 67 42 30 88 44 11 21 41 28 14 21 5 38 -11 272 507 D05 10 11 12 13 14 15 16 17 18 19 20 21 C C C C B D B C C D B D B F B D B F D B D C4 C2 C1 C5 B5 D3 B8 C6 C3 D13 B3 D7 B6 F1 B1 D9 B2 F2 D15 B7 D1 12 13 30 35 35 40 41 45 53 56 57 66 70 78 79 83 103 107 114 115 11 20 42 26 32 10 52 68 30 76 63 79 61 14 78 91 66 82 21 46 15 27 29 30 46 41 26 21 21 38 4 -8 19 1 -12 42 5 5 28 5 300 4 2 2 2 564 5 Attribute Index Type Attribute Index No Type No C4 16 17 18 19 20 21 22 23 24 25 26 27 28 29 E F D E D A A D D G A G D D E2 F1 D11 E1 D1 A3 A1 D7 D3 G2 A2 G1 D4 D6 118 121 136 172 177 186 189 236 270 275 291 292 347 350 95 44 96 26 55 84 35 102 32 101 87 31 -6 13 15 310 33 324 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 D B D A G A B G G D D A D D D D D D3 B8 D9 A3 G3 A2 B6 G2 G1 D8 D6 A1 D2 D7 D10 D4 D1 74 81 88 125 130 146 151 162 188 198 217 235 236 249 251 265 278 81 97 47 16 102 42 65 101 103 19 56 97 80 60 77 24 32 13 170 37 10 204 228 39 2 1 14 15 16 17 18 19 20 21 22 23 24 25 A G F D D G D A E D D A A1 G1 F2 D9 D8 G2 D6 A2 E1 D5 D4 A3 185 186 194 199 205 216 236 239 254 268 270 273 31 102 92 45 35 103 29 25 35 84 65 39 44 195 D06 10 11 12 13 14 15 B B D B B C D B B B D D D B B B4 B9 D8 B1 B3 C1 D10 B6 B8 B2 D2 D9 D5 B7 B5 16 18 30 49 51 53 61 66 84 86 87 98 103 113 90 54 89 95 19 52 51 97 16 57 15 39 80 29 33 23 37 24 38 28 19 2 42 44 4 -13 3 566 565 D07 10 11 12 13 14 15 16 17 B C C B F B B B B C D C C C B B D B2 C1 C4 B10 F1 B7 B3 B5 B1 C3 D11 C5 C2 C6 B9 B4 D5 3 5 11 14 14 17 25 25 28 31 34 60 62 64 76 48 51 85 15 89 88 54 88 54 25 24 60 99 88 22 35 22 11 20 37 35 41 37 24 10 478 521 491 7 D08 10 11 12 13 C D C D F C B D D B D D D C1 D11 C3 D1 F1 C2 B2 D10 D12 B1 D2 D7 D3 16 24 24 24 26 46 59 60 74 127 154 161 48 90 54 48 62 21 42 39 25 34 33 14 18 28 42 21 41 1 2 2 2 258 21 -13 30 454 499 Attribute Index Type Attribute Index No Type No C5 13 14 15 16 17 18 19 20 21 22 23 D A E D D D E A G G D D4 A2 E1 D8 D10 D9 E2 A1 G2 G1 D5 136 139 140 148 170 205 209 211 265 322 346 64 38 20 71 63 36 102 101 87 10 -16 5 -21 31 273 332 10 11 12 13 14 15 16 17 18 D D E D D A D G D D3 D7 E1 D5 D6 A1 D1 G1 D4 137 150 155 215 221 245 329 337 345 57 12 67 66 27 38 101 87 -16 30 387 15 16 17 18 19 20 21 22 23 24 25 26 27 28 D D A D E B D D D E D D A A D13 D12 A3 D8 E1 B4 D4 D7 D1 E2 D10 D11 A1 A2 128 132 148 155 168 174 174 205 223 264 268 277 299 339 37 71 20 28 31 61 40 69 73 80 44 42 23 -18 -11 1 21 32 12 13 14 15 16 17 18 19 20 21 22 B E A E A G D D D G A B1 E3 A1 E2 A3 G1 D4 D2 D5 G2 A2 103 108 115 139 157 176 186 187 189 248 263 13 89 59 102 49 45 103 42 37 -18 -7 29 199 3 290 36 D09 10 11 12 C C C D B D D F F D E D C2 C1 C3 D1 B1 D3 D7 F2 F1 D2 E3 D6 28 43 45 73 77 88 89 104 112 119 125 46 55 35 26 83 50 99 54 51 15 47 23 45 32 25 -18 5 4 489 611 D10 C C F C B E D D D C1 C2 F1 C3 B1 E2 D9 D8 D2 17 22 45 48 87 93 102 109 110 54 12 81 55 91 51 33 35 10 11 12 13 14 C C C C F D B D B D C D D B C1 C2 C5 C4 F1 D3 B2 D6 B3 D9 C3 D2 D5 B1 12 20 24 42 43 44 56 83 87 108 111 15 46 13 86 48 83 48 32 79 18 53 12 46 24 21 13 45 10 11 C C C C D C D E B D F C1 C5 C4 C2 D1 C3 D3 E1 B2 D6 F1 14 19 28 28 30 62 78 84 90 93 41 60 58 13 48 25 35 14 65 32 22 33 19 14 -16 23 42 11 -16 4 4 5 683 D11 44 43 17 22 4 12 5 D12 2 489 539 Attribute Index Type Attribute Index No Type No C6 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 B D D D B D D D D D D A D A D B7 D16 D15 D10 B1 D13 D2 D6 D5 D14 D9 A1 D7 A2 D4 126 135 153 169 170 173 178 204 214 216 220 261 270 318 331 29 19 14 35 59 94 19 16 16 63 22 14 59 20 60 20 30 2 29 26 11 12 13 14 15 16 17 18 19 20 D G A B E D E D A A D1 G1 A2 B5 E1 D4 E2 D2 A3 A1 143 148 151 157 163 201 236 266 280 312 72 102 57 92 31 80 38 62 32 29 187 37 42 -9 -12 46 45 14 15 16 17 18 19 20 21 22 23 24 25 B B F D A B E D D D G D B7 B6 F1 D1 A1 B2 E1 D4 D5 D3 G1 D2 131 136 145 156 167 182 197 213 232 239 255 280 17 59 71 75 92 23 27 34 66 101 46 12 10 D13 10 11 12 13 14 15 B B C B D D B D B D F F B D B B6 B5 C1 B4 D1 D12 B9 D3 B8 D11 F1 F2 B3 D8 B2 18 21 26 39 67 80 86 87 92 92 107 111 115 79 13 48 13 54 82 17 10 87 40 31 91 100 35 26 25 41 20 32 5 29 2 4 4 D14 10 B B C B D F B F B B B4 B6 C1 B7 D3 F2 B3 F1 B1 B2 28 31 45 62 72 95 100 120 121 131 55 96 56 50 40 40 15 28 74 39 11 15 12 25 501 D15 10 11 12 13 B B B B C B C C C C B B D B1 B4 B5 B9 C5 B10 C3 C1 C4 C2 B3 B8 D6 11 13 14 16 20 30 33 50 68 87 87 85 68 54 89 57 15 47 20 24 52 99 19 36 21 26 18 31 29 22 42 43 10 37 36 21 42 -16 294 6 578 PHẦN LÝ LỊCH TRÍCH NGANG Họ tên: TRẦN VÕ THẢO HƯƠNG Ngày, tháng, năm sinh: 02/08/1995 Nơi sinh: Thành phố Hồ Chí Minh Địa liên lạc: 213 lơ F cư xá Thanh Đa, Phường 27, Quận Bình Thạnh, Thành phố Hồ Chí Minh Q TRÌNH ĐÀO TẠO: • 2013-2018: Sinh viên Đại học Chính qui ngành Kỹ thuật Hệ thống Cơng nghiệp Khoa Cơ Khí, Trường Đại học Bách Khoa – ĐHQG-HCM • 2018-2020: Học viên Cao học (Thạc sĩ) ngành Kỹ thuật Công nghiệp Trường Đại học Bách Khoa – ĐHQG-HCM Q TRÌNH CƠNG TÁC: • 06/2017-10/2017: Nhân viên Cơng ty TNHH Lập Phúc • 06/2018: Giảng viên Bộ môn Kỹ thuật Hệ thống Cơng nghiệp, Khoa Cơ Khí, Trường Đại học Bách Khoa – ĐHQG-HCM ... dụng cho doanh nghiệp thực tế Từ khóa: Điều độ sản xuất, Job shop linh hoạt động, đa mục tiêu, điều độ predictivereactive, tái điều độ theo kiện, tái điều độ theo chu kì vii ABSTRACT These days,... 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