Hybrid genetic algorithm for coordinated production and transportation planning problem

Omar, Mohamed K.; Angusamy, Ajitha

doi:10.1109/beiac.2012.6226081

Cited by 1 publication

(2 citation statements)

References 27 publications

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“…Thus, the use of GA is important to find an optimal solution for a relatively short time [66]. The components needed to implement GA are the parameters, initial population, evaluation of fitness function, selection process and genetic operators such as crossover and mutation [67]. GA was implemented on the coordinated production and transportation problem in as a mixed-integer linear programming problem [67].…”

Section: Vehicle Routing Problem (Vrp)mentioning

confidence: 99%

“…The components needed to implement GA are the parameters, initial population, evaluation of fitness function, selection process and genetic operators such as crossover and mutation [67]. GA was implemented on the coordinated production and transportation problem in as a mixed-integer linear programming problem [67]. GA are procedures that resemble the evolutionary process (selection, crossover, and mutation processes).…”

Section: Vehicle Routing Problem (Vrp)mentioning

confidence: 99%

See 1 more Smart Citation

A Review of Genetic Algorithm: Operations and Applications

Faizatulhaida Md Isa,

Wan Nor Munirah Ariffin,

Muhammad Shahar Jusoh

et al. 2024

ARASET

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This article presents a review of the Genetic Algorithm (GA), a prominent optimization technique inspired by natural selection and genetics. In the context of rapidly evolving computational methodologies, GA have gained considerable attention for their efficacy in solving complex optimization problems across various domains. The background highlights the growing significance of optimization techniques in addressing real-world challenges. However, the inherent complexity and diversity of problems necessitate versatile approaches like GA. The problem statement underscores the need to explore the underlying operations and applications of GA to provide a nuanced understanding of their capabilities and limitations. The objectives of this review encompass delving into the fundamental genetic operators, such as selection, crossover, and mutation, while examining their role in maintaining diversity and converging toward optimal solutions. Methodology-wise, a systematic analysis of existing literature is undertaken to distil key insights and trends in GA applications. The main findings show the adaptability of GA in tackling problems spanning engineering, economics, bioinformatics, and beyond. By facilitating the discovery of optimal or near-optimal solutions within large solution spaces, GA proves its mettle in scenarios where traditional methods fall short. The conclusion underscores the enduring relevance of GA in the optimization landscape, emphasizing their potential to remain a pivotal tool for addressing intricate real-world challenges, provided their parameters are fine-tuned judiciously to balance exploration and exploitation.

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Section: Vehicle Routing Problem (Vrp)mentioning

confidence: 99%

Section: Vehicle Routing Problem (Vrp)mentioning

confidence: 99%