Data mining–based disturbances prediction for job shop scheduling

In real production manufacturing process, there are many disturbances (e.g. machine fault, shortage of materials, tool damage) which can greatly interfere the original scheduling. These interventions will cost production managers extra time to schedule orders, which increase much workload and cost of maintenance. On account of this phenomenon, a novel system of data mining–based disturbances prediction for job shop scheduling is proposed. It consists of three modules: data mining module, disturbances prediction module, and manufacturing process module. First, in data mining module, historical data and new data are acquired by radio frequency identification or cable from database, and a hybrid algorithm is used to build a disturbance tree which is utilized as a classifier of disturbances happened before manufacturing. Then, in the disturbances prediction module, a disturbances pattern is built and a decision making will be determined according to the similarity between testing data attributes and mined pattern. Finally, in the manufacturing process module, scheduling will be arranged in advance to avoid the disturbances according to the results of decision making. Besides, an experiment is conducted at the end of this article to show the prediction process and demonstrate the feasibility of the proposed method.

[1]  Nasser Mebarki,et al.  Data mining based job dispatching using hybrid simulation-optimization approach for shop scheduling problem , 2012, Eng. Appl. Artif. Intell..

[2]  Jaejin Jang,et al.  Production rescheduling for machine breakdown at a job shop , 2012 .

[3]  Ray Y. Zhong,et al.  RFID-enabled real-time advanced planning and scheduling shell for production decision making , 2013, Int. J. Comput. Integr. Manuf..

[4]  Moneer Helu,et al.  The Current State of Sensing, Health Management, and Control for Small-To-Medium-Sized Manufacturers. , 2016, Proceedings of the ASME International Conference on Manufacturing Science and Engineering. ASME International Conference on Manufacturing Science and Engineering.

[5]  Michal Jakubczyk,et al.  A framework for sensitivity analysis of decision trees , 2017, Central European Journal of Operations Research.

[6]  Mingzhou Liu,et al.  Dynamic Rescheduling Optimization of Job-shop under Uncertain Conditions , 2009 .

[7]  Takahiro Hara,et al.  A Multi-Objective Optimization Scheduling Method Based on the Ant Colony Algorithm in Cloud Computing , 2015, IEEE Access.

[8]  Pingyu Jiang,et al.  Manifold learning based rescheduling decision mechanism for recessive disturbances in RFID-driven job shops , 2016, Journal of Intelligent Manufacturing.

[9]  You-Shyang Chen,et al.  Extracting performance rules of suppliers in the manufacturing industry: an empirical study , 2011, Journal of Intelligent Manufacturing.

[10]  Ashkan Hafezalkotob,et al.  Maintenance scheduling using data mining techniques and time series models , 2018 .

[11]  Hua Yang,et al.  Supply chain production and delivery scheduling based on data mining , 2018, Cluster Computing.

[12]  Henri Pierreval,et al.  Real time selection of scheduling rules and knowledge extraction via dynamically controlled data mining , 2010 .

[13]  Jinjiang Yuan,et al.  Pareto optimization of rescheduling with release dates to minimize makespan and total sequence disruption , 2013, J. Sched..

[14]  Wei Zhang,et al.  Data mining based multi-level aggregate service planning for cloud manufacturing , 2018, J. Intell. Manuf..

[15]  Ray Y. Zhong,et al.  A big data approach for logistics trajectory discovery from RFID-enabled production data , 2015 .

[16]  P. Gu,et al.  Low-carbon scheduling and estimating for a flexible job shop based on carbon footprint and carbon efficiency of multi-job processing , 2015 .

[17]  Yan-hong Wang,et al.  Data Mining Based Approach for Jobshop Scheduling , 2014 .

[18]  Pingyu Jiang,et al.  An RFID-Driven Graphical Formalized Deduction for Describing the Time-Sensitive State and Position Changes of Work-in-Progress Material Flows in a Job-Shop Floor , 2013 .

[19]  Farouk Yalaoui,et al.  Data Mining Approaches for the Methods to Minimize Total Tardiness in Parallel Machine Scheduling Problem , 2016 .

[20]  Fei Tao,et al.  Big Data in product lifecycle management , 2015, The International Journal of Advanced Manufacturing Technology.

[21]  Zhixin Liu,et al.  Rescheduling for machine disruption to minimize makespan and maximum lateness , 2014, J. Sched..

[22]  Kevin P. Scheibe,et al.  Supply chain disruption propagation: a systemic risk and normal accident theory perspective , 2018, Int. J. Prod. Res..

[23]  De-Li Yang,et al.  Solving single machine scheduling under disruption with discounted costs by quantum-inspired hybrid heuristics , 2013 .

[24]  M. A. Adibi,et al.  A clustering-based modified variable neighborhood search algorithm for a dynamic job shop scheduling problem , 2013, The International Journal of Advanced Manufacturing Technology.

[25]  Remzi Seker,et al.  Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook , 2016, Comput. Ind..

[26]  Fei Qiao,et al.  A fuzzy Petri net-based reasoning method for rescheduling , 2011 .

[27]  Hong Zhou,et al.  On the identical parallel-machine rescheduling with job rework disruption , 2013, Comput. Ind. Eng..

[28]  Gregory Piatetsky-Shapiro,et al.  Knowledge Discovery in Databases: An Overview , 1992, AI Mag..

[29]  Rubén Ruiz,et al.  Flow shop rescheduling under different types of disruption , 2013 .

[30]  Latifur Khan,et al.  FSBD: A Framework for Scheduling of Big Data Mining in Cloud Computing , 2014, 2014 IEEE International Congress on Big Data.

[31]  Lihong Qiao,et al.  Process planning and scheduling integration with optimal rescheduling strategies , 2014, Int. J. Comput. Integr. Manuf..

[32]  Nhu Binh Ho,et al.  Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems , 2008, Comput. Ind. Eng..

[33]  Abraham Kandel,et al.  Using data mining techniques for optimizing traffic signal plans at an urban intersection , 2011, Int. J. Intell. Syst..

[34]  Benjamin Lindemann,et al.  Cloud-based Control Approach in Discrete Manufacturing Using a Self-Learning Architecture , 2018 .

[35]  Da Ruan,et al.  Neighborhood rough sets for dynamic data mining , 2012, Int. J. Intell. Syst..

[36]  Lihui Wang,et al.  Big data analytics based fault prediction for shop floor scheduling , 2017 .

[37]  Klaus-Dieter Thoben,et al.  "Industrie 4.0" and Smart Manufacturing - A Review of Research Issues and Application Examples , 2017, Int. J. Autom. Technol..

[38]  Xinyu Li,et al.  An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem , 2016 .

[39]  Li Zhang,et al.  Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks , 2014, Expert Syst. Appl..

[40]  Tom Page,et al.  A hybrid discrete firefly algorithm for solving multi-objective flexible job shop scheduling problems , 2015, Int. J. Bio Inspired Comput..