Chaotic Flower Pollination Algorithm for scheduling tardiness-constrained flow shop with simultaneously loaded stations
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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.
Davendra, D., Herrmann, F., & Bialic-Davendra, M. (2022). Chaotic Flower Pollination Algorithm for scheduling tardiness-constrained flow shop with simultaneously loaded stations. Neural Computing and Applications, ahead of print. https://doi.org/10.1007/s00521-022-08044-0
Neural Computing and Applications
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