Makespan Minimization using Hybrid Heuristic Metaheuristic Genetic Algorithm

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Bari Prasad; Karande Prasad

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Journal Paper

Paper Information

Journal: International Journal of Industrial Engineering & Production Research Year:2023 | Volume:34 | Issue:2 Page(s): 1-16

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Information Journal Paper

Title

Makespan Minimization using Hybrid Heuristic Metaheuristic Genetic Algorithm

Author(s)

Bari Prasad | Karande Prasad | Issue Writer Certificate

Keywords

Makespan

Scheduling

Heuristic

Metaheuristic

Genetic algorithm

Lower bound

Abstract

This paper presents a model for minimizing the Makespan in the flow shop Scheduling problem. Due to the impact of increased workloads, flow shops are becoming more popular and widely used in industries. To solve the challenge of minimizing Makespan, a Hybrid-Heuristic-MetaHeuristic-Genetic-Algorithm (HHMGA) is proposed. The proposed HHMGA algorithm is tested using the simulation software and demonstrated with steel industry data. The results are compared with those of the best available flow shop problem algorithms such as Palmer’s slope index, Campbell-Dudek-Smith (CDS), Nawaz-Enscore-Ham (NEH), Genetic algorithm (GA) and particle swarm optimization (PSO). According to empirical results and relative differences from the Lower bound, the proposed technique outperforms the three Heuristics and two metaHeuristics algorithms in three of six cases, while the remaining three produce the same results as the NEH Heuristic. In comparison to the steel industry's regular job Scheduling technique, the simulation model based on HHMGA can save 4642 hours. It was discovered that the suggested model enhanced the job sequence based on the Makespan requirements.

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