genetic algorithm (production scheduling)
TRANSCRIPT
Improving the ADACOR2 Supervisor Holon Scheduling Mechanism with Genetic Algorithm
by
(José Barbosa, Paulo Leitão, Emmanuel Adam and Damien Trentesaux)
on PRODUCTION SCHEDULING
presented by
Ay ob a mi A to la g b e (2015 434 4)20 DECEMBER 2016
• Abstract• Introduction• Heuristic algorithm as scheduling
techniques• An evolution manufacturing control
architecture• Experiment & Result• Conclusion
Keywords to note
• Genetic Algorithm: is a method for solving both constrained and unconstrained optimization problems based on a natural selection process that mimics biological evolution.
• Scheduling• Manufacturing Control
Case Problem• Use of GA techniques to improve the
existing fast, non-optimal scheduling techniques and improvement of the overall system processing execution
Bio-Inspired Algorithms
Ant colony optimization
algorithm (Ant)
Particle Swarm Optimization (Birds & Fish)
Genetic Optimization
(Natural Evolution)
SELECTIONMUTATION
CROSSOVER
Heuristic Algorithm As Scheduling Techniques
An Evolution Manufacture Control ArchitechADACOR & ADACOR2
• PRODUCT HOLONPH • TASK HOLONTH • OPERATION HOLONOH • SUPERVISOR
HOLONSH
ADACOR2 + GA based Algorithm
(Barbosa, J., Leitão, P., Adam, E., & Trentesaux, D. 2015)
Experiment Test on AIP-PRIMECA Flexible Manufacturing System
(FMS)• Composed of a Shuttle Transport Conveyor System
& 7 work station.• Categorised into Loading/Unloading Station,
Automated Inspection Unit, Skilled operation and Manual Recovery
• Use of different batch sizes (ranging from A0 to F0)
Experiment • A value of 6 was used (Batch sizes)• 6 initially scheduling solution were generated• Algorithm runs iteratively 6 times
Result
(Barbosa, J., Leitão, P., Adam, E., & Trentesaux, D. 2015)
Considering TIME and the OUTPUT RESULT
• GA OVERALL TIME improves the “old” by 24.81%
• Possible increase based on BATCH ORDER increases, GA improvements also rise, giving 34.77% for scenario F0
Conclusion• Experimental results have shown that even with a simple version of the
GA it is still possible to increase deeply the actual scheduling algorithm.
• Future work will be devoted to incorporate a dedicated scheduling tool in the SH. Tools such IBM ILOG or the Choco API are good candidates for this integration, being the last one fully compliant with Java. With these it is expected to greatly improve the GA calculation speed and the GA results.
THANK YOU