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Cloud-based dynamic distributed optimisation of integrated process planning and scheduling in smart factories

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A presentation of the paper developed in the SAFIRE project titled "Cloud-based dynamic distributed optimisation of integrated process planning and scheduling in smart factories", delivered at the Genetic and Evolutionary Computation Conference (GECCO) at Prague, The Czech Republic in July 2019.

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Cloud-based dynamic distributed optimisation of integrated process planning and scheduling in smart factories

  1. 1. Cloud-based Dynamic Distributed Optimisation of Integrated Process Planning and Scheduling in Smart Factories Shuai Zhao1, Piotr Dziurzanski1, Michal Przewozniczek1, Marcin Komarnicki, Leandro Soares Indrusiak1 1University of York, UK 2Wroclaw University of Technology, Poland GECCO 2019 Prague, July 13th-17th 2019 1
  2. 2.  SAFIRE project  System architecture  Problem description  Cloud deployment – Kubernetes cluster  Dynamic number of islands  Experimental results  Conclusion GECCO 2019 Prague, July 13th-17th 2019 2 Agenda
  3. 3.  Funded under: H2020 - EU.2.1.1. - Industrial Leadership  Reconfiguration-as-a- Service for dynamic smart factories and manufactured smart products.  Exploits cloud-based services to continuously optimise the performance of production systems and products. GECCO 2019 Prague, July 13th-17th 2019 3 SAFIRE project
  4. 4. GECCO 2019 Prague, July 13th-17th 2019 4 SAFIRE architecture SAFIRE Manufacturer / Factory Optimisation & Reconfiguration Engine Situation Determination Services Predictive Analytics Engine Reconfiguration Quality Evaluation Services Reconfiguration Interfaces Secure SAFIRE infrastructure Connected Product Network &Event-driven Data Ingestion Situation Monitoring Services
  5. 5. GECCO 2019 Prague, July 13th-17th 2019 5 SAFIRE components Situation Determination (SD) Reconfiguration & Optimisation Engine (OE) Predictive Analytics (PA) Reconfiguration Quality Evaluation Services based on Digital Twin time A 0 100 B Interval algebra Smart Factory
  6. 6. GECCO 2019 Prague, July 13th-17th 2019 6 Application model - example allocation decision A B  Two dependent manufacturing processes, two machines  Process A can be assigned to machine M1  Process B can be assigned to machine M1 or M2  M2 is 2* faster than M1 Process A Process B M1 time0 20 40 60 80 M2 Process A M1 time0 20 40 60 80 Process B M2 Or:
  7. 7. GECCO 2019 Prague, July 13th-17th 2019 7 Application model 53 2 9 41 9 5 3 6 4 7 8 5 9 9 5 6 7 8 initialisation termination evaluate fitness select the fittest breed pairs / crossover mutations replace old population Input: manufacturing processes, plant situation (e.g. past allocations, machine availability etc.) Output: resource allocations, resource modes, time slots
  8. 8. GECCO 2019 Prague, July 13th-17th 2019 8 Chromosome encoding Target machine Mode Priority Target machine Mode Priority Process A Process B ...
  9. 9. GECCO 2019 Prague, July 13th-17th 2019 9 Kubernetes (K8s) cluster Kubernetes cluster Load balancer Pod Services Horizontal Pod Autoscaler PodAddon CoreDNS
  10. 10. GECCO 2019 Prague, July 13th-17th 2019 10 Execution stages Pod - Master ... 1st stage 2nd stage ... Kubernetes cluster Pod - OE Pod - OE Pod - OE ... Pod - OE Pod - OE Pod - OE Pod - Master Pod - Master
  11. 11.  Executing islands for i iterations (=stage)  Adding non-dominated solutions returned from all islands to (common) PF  Performing migrations  Deciding on the number of nodes GECCO 2019 Prague, July 13th-17th 2019 11 Master node functionality
  12. 12.  One K8s working node can host one or more OE (islands)  Number of islands – decided by the Master node  Number of working nodes – decided by K8s Horizontal Pod Autoscaler based on the K8s metrics (memory usage) GECCO 2019 Prague, July 13th-17th 2019 12 Dynamic number of islands / K8s working nodes
  13. 13.  Static – baseline  Classic  If CI of PF @ stage s <= CI of PF @ stage s-1 ● Delete islands that meet island deletion criteria; ● Create one island with randomly generated individuals  Active  If CI of PF @ stage s <= CI of PF @ stage s-1 ● Delete all islands that do not provide new solutions to PF  If CI of PF @ stage s <= CI of PF @ stage s-1 ● Create one island with randomly generated individuals GECCO 2019 Prague, July 13th-17th 2019 13 Number of islands - strategies
  14. 14. GECCO 2019 Prague, July 13th-17th 2019 14 Process Manufacturing Example
  15. 15.  Metrics  Key objective metrics ● Makespan (minimise) ● Surplus (minimise)  Controlled metrics ● Production line ● Recipes applied  Observable metrics ● Actual process time per batch ● Energy consumption 15 Optimisation Prague, July 13th-17th 2019GECCO 2019
  16. 16. GECCO 2019 Prague, July 13th-17th 2019 16 Recipes Paint name Recipe Compatible resources Amount produced Execution time Std Weiss A Mixer 1 - Mixer 5 5 t 90 min. B Mixer 6, Mixer 7 10 t 60 min. C Mixer 8, Mixer 9 10 t 45 min. D Mixer 8, Mixer 9 10 t 40 min. Weiss Matt A Mixer 1 - Mixer 5 5 t 90 min. B Mixer 6, Mixer 7 10 t 60 min. C Mixer 8, Mixer 9 10 t 45 min. D Mixer 8, Mixer 9 10 t 40 min. W Super Glanz A Mixer 1 - Mixer 5 4 t 120 min. B Mixer 6, Mixer 7 8 t 90 min. C Mixer 8, Mixer 9 8 t 60 min. D Mixer 8, Mixer 9 8 t 55 min. Weiss Basis A Mixer 1 - Mixer 5 6 t 60 min. B Mixer 6, Mixer 7 12 t 45 min. C Mixer 8, Mixer 9 12 t 30 min. D Mixer 8, Mixer 9 12 t 25 min.
  17. 17. Strategy Island Executed Island Created Island Deleted Static 200 5 0 Active 192 29 19 Classic 137 25 23 GECCO 2019 Prague, July 13th-17th 2019 17 Experimental results
  18. 18.  DCI: (0,0,1)  Amazon Elastic Container Service for Kubernetes (Amazon EKS) run on 4-cores instances m5 in the AWS London zone: Static- 15 USD, Classic – 11 USD, Active - 12.1 USD GECCO 2019 Prague, July 13th-17th 2019 18 Process manufacturing optimisation results by all managers
  19. 19. 19 Discrete Manufacturing Example Prague, July 13th-17th 2019GECCO 2019
  20. 20.  Metrics  Key objective metrics ● Makespan (minimize) ● Cost/part (minimize)  Controlled metrics: ● Wire  Type  Diameter ● Machine Eco-mode ● Machine model  Observable metrics ● Actual process time per job/part ● Actual wire consumption ● Energy consumption ● Actual Wire cost. 20 Optimisation Prague, July 13th-17th 2019GECCO 2019
  21. 21. GECCO 2019 Prague, July 13th-17th 2019 21 Order – 16 metal parts Part name Size/ machin e MW PL (mm) Speed (mm/mi n) Cutting time (min) Wire Consum ption speed (kg/h) Wire Consum ption per part (kg) Wire cost per kg (€) Wire Cost per part (€) Machin e cost per hour (€) Machin e cost per part (€) Total cost per part (€) P1 Small 1 2400 2.85 842.1 0.250 3.5 8.0 28.1 10 140.4 168.4 Medium 1 2400 2.85 842.1 0.250 3.5 8.0 28.1 15 210.5 238.6 Large 1 2400 2.85 842.1 0.250 3.5 8.0 28.1 35 491.2 519.3 Small 2 2400 2.47 971.4 0.233 3.8 8.0 30.2 10 161.9 192.1 Medium 2 2400 2.47 971.4 0.233 3.8 8.0 30.2 15 242.9 273.1 Large 2 2400 2.47 971.4 0.233 3.8 8.0 30.2 35 566.7 596.9 Small 3 2400 3.17 756.2 0.250 3.2 13.0 41.0 10 126.0 167.0 Medium 3 2400 3.17 756.2 0.250 3.2 13.0 41.0 15 189.0 230.0 Large 3 2400 3.17 756.2 0.250 3.2 13.0 41.0 35 441.1 482.1 Small 4 2400 3.27 733.5 0.258 3.2 17.0 53.7 10 122.3 175.9 Medium 4 2400 3.27 733.5 0.258 3.2 17.0 53.7 15 183.4 237.1 Large 4 2400 3.27 733.5 0.258 3.2 17.0 53.7 35 427.9 481.6 . . .
  22. 22.  DCI test:  ManagerStatic: 0.852  ManagerActive: 1.0  ManagerClassic: 0.926  The total optimisation cost < 0.5USD for any strategy. GECCO 2019 Prague, July 13th-17th 2019 22 Cost per part vs Makespan
  23. 23.  The three managers have been used to optimise 30 randomly generated manufacturing orders.  28 out of 30: ManagerActive was the best.  ManagerClassicacted better than ManagerStatic in 27 cases. GECCO 2019 Prague, July 13th-17th 2019 23 Scaling the problem size
  24. 24.  Two genetic algorithms for multi-objective optimisation using a dynamic number of islands have been proposed.  The software implementation of these algorithms has been deployed to a cloud and applied to an integrated process planning and scheduling for two real-world smart factories representing the process and discrete manufacturing branches.  The presented experimental results have confirmed the superiority of the proposed method over the typical approach using a static number of islands in terms of solution quality and computation time. GECCO 2019 Prague, July 13th-17th 2019 24 Conclusion
  25. 25. GECCO 2019 Prague, July 13th-17th 2019 25 Asynchronous architecture SD Kubernetes cluster Load balancer Pod OE Services Horizontal Pod Autoscaler Pod Redis Addon CoreDNS
  26. 26. Thank you! Questions ? GECCO 2019 Prague, July 13th-17th 2019 26

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