A Novel Genetic Algorithm for Solving Machine Part Cell Formation Problem considering alternative Process Plans

Sowmiya, N.; Valarmathi, B.; Gupta, Neha; Muthu, Pravin; Rajendran, Chandrasekharan

doi:10.1016/j.matpr.2018.02.353

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…The constructor technique and k-means algorithm were used to more precisely categorize the machine cells generated by the GA into part families. Sowmiya et al [ 21 ] applied GA to address CF issues while considering an alternative processing route. A ranking index was utilized to calculate the correlation value, which was then used to produce parent chromosomes with the same number of genes as the number of parts.…”

Section: Literature Reviewmentioning

confidence: 99%

Cell formation and layout design using genetic algorithm and TOPSIS: A case study of Hydraulic Industries State Company

Dhayef,

Al-Zubaidi,

Al-Kindi

et al. 2024

PLoS ONE

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Cell formation (CF) and machine cell layout are two critical issues in the design of a cellular manufacturing system (CMS). The complexity of the problem has an exponential impact on the time required to compute a solution, making it an NP-hard (complex and non-deterministic polynomial-time hard) problem. Therefore, it has been widely solved using effective meta-heuristics. The paper introduces a novel meta-heuristic strategy that utilizes the Genetic Algorithm (GA) and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) to identify the most favorable solution for both flexible CF and machine layout within each cell. GA is employed to identify machine cells and part families based on Grouping Efficiency (GE) as a fitness function. In contrast to previous research, which considered grouping efficiency with a weight factor (q = 0.5), this study utilizes various weight factor values (0.1, 0.3, 0.7, 0.5, and 0.9). The proposed solution suggests using the TOPSIS technique to determine the most suitable value for the weighting factor. This factor is critical in enabling CMS to design the necessary flexibility to control the cell size. The proposed approach aims to arrange machines to enhance GE, System Utilization (SU), and System Flexibility (SF) while minimizing the cost of material handling between machines as well as inter- and intracellular movements (TC). The results of the proposed approach presented here show either better or comparable performance to the benchmark instances collected from existing literature.

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Section: Literature Reviewmentioning

confidence: 99%

Cell formation and layout design using genetic algorithm and TOPSIS: A case study of Hydraulic Industries State Company

Dhayef,

Al-Zubaidi,

Al-Kindi

et al. 2024

PLoS ONE

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“…In the present study, the global reaction kinetics were modelled and optimized in a bubble column reactor via genetic algorithm (GA). Analysis of the kinetic models of catalytic reactions requires more accurate estimation of rate parameters and constants (20)(21)(22). The most commonly used methods in determining the optimal parameters model, are non-linear fitting algorithm like artificial neural networks and Marquardt algorithm.…”

Section: Introductionmentioning

confidence: 99%

Kinetic study of the regeneration of spent caustic via the genetic algorithm method

Karimi

Fatehifar

Alizadeh

et al. 2018

Environ Health Eng Manag

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Background: Spent caustic contains noxious components such as sulfide species and also high chemical oxygen demand content (COD). Oxidation of these materials to caustic and sulfate species is mostly the rate-controlling step within catalytic oxidation of spent caustic. Methods: In this study, the kinetics of catalytic oxidation of spent caustic and the regeneration methodology of the sulfidic spent caustic were investigated. The kinetics of catalytic oxidation of spent caustic was studied in the presence of a heterogeneous catalyst. The developed mathematical model was verified via the batch bubble column reactor. The elementary and non-elementary models based on the genetic algorithm were used to obtain the rate coefficient and kinetic order. Results: The experiments were carried out at various conditions. The results indicated that the error of objective function of the non-elementary and elementary models was 3.01% and 134.96%, respectively. Conclusion: According to the results, the non-elementary model had rational outcome compared to the elementary one. Also, non-elemental model is more concordance with experimental results.

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Cell formation with alternative process routing by euclidian distance matrix part assignment strategy EDPAS

Hakeem,

Al-Zubaidi,

Al-Kindi

2023

4th International Scientific Conference of Alkafeel University (Iscku 2022)

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A Novel Genetic Algorithm for Solving Machine Part Cell Formation Problem considering alternative Process Plans

Cited by 4 publications

References 25 publications

Cell formation and layout design using genetic algorithm and TOPSIS: A case study of Hydraulic Industries State Company

Cell formation and layout design using genetic algorithm and TOPSIS: A case study of Hydraulic Industries State Company

Kinetic study of the regeneration of spent caustic via the genetic algorithm method

Cell formation with alternative process routing by euclidian distance matrix part assignment strategy EDPAS

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