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Active Learning Methods for Dynamic Job Shop Scheduling Using Genetic Programming Under Uncertain Environment

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Active Learning Methods for Dynamic Job Shop Scheduling Using Genetic Programming Under Uncertain Environment Active Learning Methods for Dynamic Job Shop Scheduling using Genetic Programming under Uncertain Environment by Deepak Karunakaran A thesis submitted to the Victoria University of Wellington in fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science. Victoria University of Wellington 2019 Abstract Scheduling is an important problem in artificial intelligence and opera- tions research. In production processes, it deals with the problem of alloca- tion of resources to different tasks with the goal of optimizing one or more objectives. Job shop scheduling is a classic and very common scheduling problem. In the real world, shop environments dynamically change due to events such as the arrival of new jobs and machine breakdown. In such manufacturing environments, uncertainty in shop parameters is typical. It is of vital importance to develop methods for effective scheduling in such practical settings. Scheduling using heuristics like dispatching rules is very popular and suitable for such environments due to their low computational cost and ease of implementation. For a dynamic manufacturing environment with varying shop scenarios, using a universal dispatching rule is not very ef- fective. But manual development of effective dispatching rules is difficult, time consuming and requires expertise. Genetic programming is an evo- lutionary approach which is suitable for automatically designing effective dispatching rules. Since the genetic programming approach searches in the space of heuristics (dispatching rules) instead of building up a sched- ule, it is considered a hyper-heuristic approach. Genetic programming like many other evolutionary approaches is com- putationally expensive. Therefore, it is of vital importance to present the genetic programming based hyper-heuristic (GPHH) system with schedul- ing problem instances which capture the complex shop scenarios captur- ing the difficulty in scheduling. Active learning is a related concept from machine learning which concerns with effective sampling of those training instances to promote the accuracy of the learned model. The overall goal of this thesis is to develop effective and efficient ge- netic programming based hyper-heuristic approaches using active learn- ing techniques for dynamic job shop scheduling problems with one or more objectives. This thesis develops new representations for genetic programming en- abling it to incorporate the uncertainty information about processing times of the jobs. Furthermore, a cooperative co-evolutionary approach is devel- oped for GPHH which evolves a pair of dispatching rules for bottleneck and non-bottleneck machines in the dynamic environment with uncer- tainty in processing times arising due to varying machine characteristics. The results show that the new representations and training approaches are able to significantly improve the performance of evolved dispatching rules. This thesis develops a new GPHH framework in order to incorpo- rate active learning methods toward sampling DJSS instances which pro- mote the evolution of more effective rules. Using this framework, two new active sampling methods were developed to identify those schedul- ing problem instances which promoted evolution of effective dispatching rules. The results show the advantages of using active learning methods for scheduling under the purview of GPHH. This thesis investigates a coarse-grained model of parallel evolution- ary approach for multi-objective dynamic job shop scheduling problems using GPHH. The outcome of the investigation was utilized to extend the coarse-grained model and incorporate an active sampling heuristic to- ward identifying those scheduling problem instances which capture the conflict between the objectives. The results show significant improvement in the quality of the evolved Pareto set of dispatching rules. Through this thesis, the following contributions have been made. (1) New representations and training approaches for GPHH to incorporate uncertainty information about processing times of jobs into dispatching rules to make them more effective in a practical shop environment. (2) A new GPHH framework which enables active sampling of scheduling prob- lem instances toward evolving dispatching rules effective across complex shop scenarios. (3) A new active sampling heuristic based on a coarse- grained model of parallel evolutionary approach for GPHH for multi- objective scheduling problems. iv List of Publications 1. Karunakaran, Deepak, Yi Mei, Gang Chen, and Mengjie Zhang. “Dy- namic job shop scheduling under uncertainty using genetic program- ming.” In Proceedings of the Intelligent and Evolutionary Systems, Springer, 2017, pp. 195-210, Canberra. 2. Karunakaran, Deepak, Yi Mei, Gang Chen, and Mengjie Zhang. “Evolv- ing dispatching rules for dynamic Job shop scheduling with uncer- tain processing times.” In Proceedings of IEEE Congress on Evolu- tionary Computation (CEC), IEEE, 2017, pp. 364-371, San Sebastian. 3. Karunakaran, Deepak, Yi Mei, Gang Chen, and Mengjie Zhang. “To- ward evolving dispatching rules for dynamic job shop scheduling under uncertainty.” In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), ACM, 2017, pp. 282-289, Berlin. 4. Karunakaran, Deepak, Gang Chen, and Mengjie Zhang. “Parallel multi-objective job shop scheduling using genetic programming.” In Proceedings of Australasian Conference on Artificial Life and Com- putational Intelligence (ACALCI), Springer, 2016, pp. 234-245, Can- berra. 5. Karunakaran, Deepak, Yi Mei, Gang Chen, and Mengjie Zhang. “Sam- pling Heuristics for Multi-objective Dynamic Job Shop Scheduling Using Island Based Parallel Genetic Programming.” In Proceedings v vi of International Conference on Parallel Problem Solving from Nature (PPSN), Springer, 2018, pp. 347-359, Coimbra. 6. “Active Learning Methods for Dynamic Job Shop Scheduling under Uncertainty Using Genetic Programming based Hyper-heuristics” (Journal paper in preparation). Acknowledgments I would like to thank my parents and wife for their continuous support and sacrifices throughout this journey. I am grateful to Victoria University of Wellington for the financial sup- port through the Victoria Doctoral Scholarship and Victoria Doctoral Sub- mission Scholarship. I am also grateful to the administrative staff at the Engineering and Computer Science school for their support throughout my study. I would like to acknowledge my gratitude to my supervisors Prof.Mengjie Zhang, Dr.Aaron Chen and Dr.Yi Mei for their support and supervision to- ward the completion of this PhD thesis. vii viii Contents List of Publications iv Acknowledgement vii List of Figures xii List of Tables xv 1 Introduction1 1.1 Problem Statement........................1 1.2 Motivations............................4 1.3 Research Goals..........................9 1.4 Major Contributions....................... 13 1.5 Organization of thesis...................... 15 2 Literature Review 17 2.1 Scheduling............................. 18 2.1.1 Job Shop Scheduling Problem.............. 19 2.1.2 Uncertainty in Scheduling................ 23 2.1.3 Dynamics in Scheduling................. 24 2.2 Machine Learning and Hyper-heuristics............ 25 2.2.1 Machine Learning.................... 25 2.2.2 Heuristics, Meta-heuristics and Hyper-heuristics... 28 2.3 Evolutionary Computation................... 30 ix x CONTENTS 2.3.1 Evolutionary Algorithms................ 30 2.3.2 Swarm Intelligence.................... 31 2.4 Active Learning.......................... 32 2.4.1 Active Learning in Evolutionary Algorithms..... 34 2.5 Parallel Evolutionary Algorithms................ 35 2.6 Genetic Programming...................... 39 2.6.1 Multi-objective GP (MOGP)............... 44 2.6.2 Genetic Programming based Hyper-heuristics (GPHH) 46 2.7 Related Work to Job Shop Scheduling............. 46 2.7.1 Solution Approaches for Static JSS........... 46 2.7.2 Solution Approaches for Dynamic JSS......... 51 2.7.3 GPHH for scheduling.................. 53 2.7.4 Cooperative Co-evolution for JSS............ 56 2.7.5 Difference in Solution Approaches to Static and Dy- namic JSS Problems................... 57 2.7.6 Approaches for Dealing with Uncertainty in JSS... 57 2.8 Parallel Hyper-heuristics and MOEAs............. 59 2.9 Summary of Literature Survey................. 60 3 Genetic Programming based Hyper-Heuristics for Dynamic Job Shop Scheduling under Uncertainty 63 3.1 Introduction............................ 63 3.1.1 Chapter Goals....................... 66 3.1.2 Chapter Organization.................. 66 3.2 New representations....................... 67 3.2.1 Simulation Model with Uncertainty.......... 67 3.2.2 Exponential Moving Average (EMA) Terminal.... 69 3.2.3 Ex-post and Ex-ante Optimization........... 70 3.2.4 Experiment Design.................... 75 3.2.5 Results and Discussions................. 79 3.2.6 Analysis.......................... 85 CONTENTS xi 3.2.7 Section Summary..................... 87 3.3 Cooperative Co-evolutionary method............. 88 3.3.1 Experiment Design.................... 98 3.3.2 Results and Discussions................. 100 3.3.3 Analysis.......................... 105 3.3.4 Section Summary..................... 106 3.4 Further Discussion........................ 107 3.5 Chapter Summary......................... 107 4 Active Sampling Methods for Dynamic Job Shop Scheduling un- der Uncertainty 110 4.1 Introduction............................ 110 4.1.1 Shop Scenarios in Dynamic Environment....... 111 4.1.2 Multiple Dispatching
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