Scheduling Tardiness Constrained Flow Shop with Simultaneously Loaded Stations Using Genetic Algorithm
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This paper describes an approach for solving a tardiness constrained flow shop with simultaneously loaded stations using a Genetic Algorithm (GA). This industrial based problem is modeled from a filter basket production line and is generally solved using deterministic algorithms. An evolutionary approach is utilized in this paper to improve the tardiness and illustrate better consistent results. A total of 120 different problem instances in six test cases are randomly generated to mimic conditions, which occur at industrial practice and solved using 22 different GA scenarios. These results are compared with four standard benchmark priority rule based algorithms of First in First Out (FIFO), Raghu and Rajendran (RR), Shortest Processing Time (SPT) and Slack. From all the obtained results, GA was found to consistently outperform all compared algorithms for all the problem instances.
Davendra, D., Hermann, F., & Bialic-Davendra, M. (2020). Scheduling Tardiness Constrained Flow Shop with Simultaneously Loaded Stations Using Genetic Algorithm. ISMSI '20: Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 93-98. https://doi.org/10.1145/3396474.3396475
ISMSI '20: Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence
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