Calibration of GA Parameters for Layout Design Optimization Problems Using Design of Experiments

Modrák, Vladimír; Sudhakarapandian, R.; Semanco, Pavol

doi:10.3390/app11156940

Cited by 6 publications

(5 citation statements)

References 50 publications

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“…The proposed approach shows promise in optimizing CF and layout design problems. Modrak et al [ 32 ] used the Taguchi method to determine the best configuration of parameters that affect the performance of GA in layout design and CF problems. They investigated the impact of noise variables, including balance weight factor, probability of mutation, and probability of crossover, on the optimal combination of genetic operators.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

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%

“…The underlying concept of GA is based on the principles of natural selection and genetics, which were initially proposed by Holland [37]. The following sequence of steps for GA can be applied to the setting of part-machine CF [32,33].…”

Section: Genetic Algorithmmentioning

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

View full text Add to dashboard Cite

show abstract

“…The test results demonstrated that the proposed strategy outperformed the competing algorithms. The primary objective of Modrak et al [31] was to discover how to solve the issues of Cell Formation and layout design. Changes to the GA parameter set resulting from research have an effect on the CMS issue.…”

Section: Ga and Hybrid Ga Approaches To Solve Cell Formation And Mach...mentioning

confidence: 99%

Cell Formation and Intra-Cell Optimal Machine Location in CMS: A Novel Genetic Algorithm (GA) Based on Machine Encoding

Wu,

Li,

Rizwan

et al. 2023

Applied Sciences

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Manufacturing industries are in a constant state of competition to attract customers in a variety of methods. Group Technology (GT) is a term used in the field of manufacturing for grouping similar elements based on their similarities in production and design. Cellular manufacturing (CM) is an application of Group Technology (GT) that has gained widespread traction in Small- and Medium-Sized Enterprises (SMEs) during the recent years in order to increase the production floor’s efficiency and output. A Cell Formation consists of grouping identical machinery and assigning them on similar functions. There are three main decisions involved in designing the Cellular Manufacturing System (CMS): Group Scheduling (GS), Group Layout (GL), and Cell Formation (CF). In this study, the primary challenge associated with the CMS is not only the formation of cells but also the optimal placement of machinery within each cell. This paper’s objectives are therefore twofold: the formation of cells and the optimal placement of machinery within cells. For the purpose of Cell Formation and the position of machinery within the cell, a Genetic Algorithm (GA) and Encoding Scheme are employed. In this study, a Genetic Algorithm is used to classify machines and parts, while MATLAB is used for the simulation and encoding scheme. To evaluate the developed objective function and GA, a layout problem of medium size is solved. Results indicate that the proposed strategy is effective for resolving CMS issues and increasing productivity by 8.85%.

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“…Modrak, Pandian, and Semanco [56] focussed on finding out how the solutions of the cell formation and layout design problem of CMs are affected by changing the set of GA parameters. On this basis, they used Takuji's approach to find the optimum combination of GA parameters that can improve its efficiency, and explore whether this optimum combination can be affected by noise factors, which are represented by the size of the matrix in that research.…”

Section: Ga To Solve Cell Machine Layout Problemsmentioning

confidence: 99%

Grouping technology and a hybrid genetic algorithm‐desirability function approach for optimum design of cellular manufacturing systems

Al‐Zuheri

Ketan

Vlachos

2022

IET Collab Intel Manufact

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Cell formation and machine layout in cellular manufacturing systems (CMs) design are considered as a crucial, yet hard and complex decision process. Owing to the nondeterministic polynomial time (NP) and combinatorial class of this problem, this paper presents an innovative heuristic approach to re-arrange machines enabling the minimisation of inter/intra-cellular movements as well as the cost of material handling between machines, therefore increasing group efficiency and efficacy. The heuristic approach, which is based on group technology, genetic algorithms, and desirability function, determines the optimal solution for flexible cell formation and machine layout within each cell. Flexibility refers to an explicit improvement using the desirability function to modify cell design by altering the ratio data; that is, the weight factor to meet demand flexibility. Specifically, the desirable function proposed here to provide the optimal setting of the weighting factor as a key factor which enables CMs design the flexibility to control the cell size. Promised results were obtained when the proposed approach was applied to a case study. Practical implications and recommendations are provided for use by decision makers in the design of CMs.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Calibration of GA Parameters for Layout Design Optimization Problems Using Design of Experiments

Cited by 6 publications

References 50 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

Cell Formation and Intra-Cell Optimal Machine Location in CMS: A Novel Genetic Algorithm (GA) Based on Machine Encoding

Grouping technology and a hybrid genetic algorithm‐desirability function approach for optimum design of cellular manufacturing systems

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