TY - CHAP A1 - Davendra, Donald A1 - Herrmann, Frank A1 - Bialic-Davendra, Magdalena T1 - Scheduling tardiness constrained flow shop with simultaneously loaded stations using Genetic Algorithm T2 - Proceedings of the 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (ISMSI 2020): Thimphu, Kingdom of Bhutan during March 21-22, 2020 N2 - 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. KW - simulation of restrictions KW - scheduling, KW - flow-shop KW - no-buffer (blocking) KW - no-wait KW - genetic algorithm KW - real world application KW - Ablaufplanung KW - Hierarchische Produktionsplanung KW - Flow-shop-Problem KW - Reihenfolgeproblem KW - Genetischer Algorithmus Y1 - 2020 U6 - https://doi.org/10.1145/3396474.3396475 PB - ACM ER - TY - JOUR A1 - Davendra, Donald A1 - Herrmann, Frank A1 - Bialic-Davendra, Magdalena T1 - Chaotic Flower Pollination Algorithm for scheduling tardiness-constrained flow shop with simultaneously loaded stations JF - Neural computing & applications N2 - 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. KW - Flower pollination algorithm KW - Chaos maps KW - Flow shop with tardiness Y1 - 2022 U6 - https://doi.org/10.1007/s00521-022-08044-0 PB - Springer Nature ER -