@inproceedings{DavendraHerrmannBialicDavendra, author = {Davendra, Donald and Herrmann, Frank and Bialic-Davendra, Magdalena}, title = {Scheduling tardiness constrained flow shop with simultaneously loaded stations using Genetic Algorithm}, series = {Proceedings of the 4th International Conference on Intelligent Systems, Metaheuristics \& Swarm Intelligence (ISMSI 2020): Thimphu, Kingdom of Bhutan during March 21-22, 2020}, booktitle = {Proceedings of the 4th International Conference on Intelligent Systems, Metaheuristics \& Swarm Intelligence (ISMSI 2020): Thimphu, Kingdom of Bhutan during March 21-22, 2020}, publisher = {ACM}, doi = {10.1145/3396474.3396475}, abstract = {In this study, a real world flow shop with a transportation restriction is regarded. This restriction reduces the set of feasible schedules even more than the no-buffer restrictions discussed in the literature in the case of limited storage. Still this problem is NP-hard. Since this scheduling problem is integrated in the usual hierarchical planning, the tardiness is minimised. Compared to even specific priority rule for this class of problems the suggested genetic algorithm delivers significant better results. The specific structure of this class of problems complicates the calculation of the performance criteria. This is solved by a simulation algorithm.}, subject = {Ablaufplanung}, language = {en} } @article{DavendraHerrmannBialicDavendra, author = {Davendra, Donald and Herrmann, Frank and Bialic-Davendra, Magdalena}, title = {Chaotic Flower Pollination Algorithm for scheduling tardiness-constrained flow shop with simultaneously loaded stations}, series = {Neural computing \& applications}, journal = {Neural computing \& applications}, publisher = {Springer Nature}, doi = {10.1007/s00521-022-08044-0}, abstract = {This paper introduces a novel chaotic flower pollination algorithm (CFPA) to solve a tardiness-constrained flow-shop scheduling problem with simultaneously loaded stations. This industrial manufacturing problem is modeled from a filter basket production line in Germany and has been generally solved using standard deterministic algorithms. This research develops a metaheuristic approach based on the highly efficient flower pollination algorithm coupled with different chaos maps for stochasticity. The objective function targeted is the tardiness constraint of the due dates. Fifteen different experiments with thirty scenarios are generated to mimic industrial conditions. The results are compared with the genetic algorithm and with the four standard benchmark priority rule-based deterministic algorithms of First In First Out, Raghu and Rajendran, Shortest Processing Time and Slack. From the obtained results and analysis of the relative difference, percentage relative difference and t tests, CFPA was found to be significantly better performing than the deterministic heuristics and the GA algorithm.}, language = {en} } @inproceedings{MetlickaDavendraHerrmannetal., author = {Metlicka, Magdalena and Davendra, Donald and Herrmann, Frank and Meier, Markus and Amann, Matthias}, title = {GPU accelerated NEH algorithm}, series = {Proceedings of the 2014 IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS), Orlando, FL, 09.-12. Dec. 2014}, booktitle = {Proceedings of the 2014 IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS), Orlando, FL, 09.-12. Dec. 2014}, doi = {10.1109/CIPLS.2014.7007169}, pages = {114 -- 119}, abstract = {This research aims to develop a CUDA accelerated NEH algorithm for the permutative flowshop scheduling problem with makespan criterion. NEH has been shown in the literature as the best constructive heuristic for this particular problem. The CUDA based NEH aims to speed up the processing time by utilising the GPU cores for parallel evaluation. In order to show the versatility and scalability of the CUDA based NEH, four new higher dimensional Taillard sets are generated. The experiments are conducted on the CPU and GPU and pairwise compared. Percentage relative difference and paired t-test both confirm that the GPU based NEH significantly improves on the execution time compared to the sequential CPU version for all the high dimensional problem instances.}, subject = {Flow-shop-Problem}, language = {en} }