Chaotic Flower Pollination Algorithm for scheduling tardiness-constrained flow shop with simultaneously loaded stations

Document Type

Article

Department or Administrative Unit

Computer Science

Publication Date

11-18-2022

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.

Comments

This article was originally published in Neural Computing and Applications. The full-text article from the publisher can be found here.

Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.

Journal

Neural Computing and Applications

Rights

Copyright © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

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