New criteria for configuration of cellular manufacturing considering product mix variation

Bootaki, Behrang; Mahdavi, Iraj; Paydar, Mohammad Mahdi

doi:10.1016/j.cie.2016.06.021

Cited by 15 publications

(8 citation statements)

References 40 publications

(59 reference statements)

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“…AUGMECON has been employed in various applications and systems, including supply chain management (Torabi et al 2013;Bootaki et al 2014Bootaki et al , 2016Canales-Bustos et al 2017;Musavi and Bozorgi-Amiri 2017;Ravat et al 2017;Vieira et al 2017;Sazvar et al 2018;Ehrenstein et al 2019;Oiu et al 2019;Shekarjan et al 2019;Xin et al 2019), energy planning (Hombach and Walther 2015;Tartibu et al 2015;Arancibia et al 2016;Mohammadkhani et al 2018;Rabbani et al 2018;Sedighizadeh et al 2018;Razm et al 2019), waste management Mavrotas et al 2015a, b;Inghels et al 2016), portfolio analysis (Xidonas et al 2010(Xidonas et al , 2011 Mavrotas and Florios (2013) further extended this algorithm in AUGMECON 2, by introducing a bypass coefficient as well as a type of lexicographic optimisation to all objective functions, the order of which was insignificant in AUGMECON:…”

Section: A Brief Overview Of the Augmented ε-Constraint Methodsmentioning

confidence: 99%

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

et al. 2020

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Systems can be unstructured, uncertain and complex, and their optimisation often requires operational research techniques. In this study, we introduce AUGMECON-R, a robust variant of the augmented ε-constraint algorithm, for solving multiobjective linear programming problems, by drawing from the weaknesses of AUG-MECON 2, one of the most widely used improvements of the ε-constraint method. These weaknesses can be summarised in the ineffective handling of the true nadir points of the objective functions and, most notably, in the significant amount of time required to apply it as more objective functions are added to a problem. We subsequently apply AUGMECON-R in comparison with its predecessor, in both a set of reference problems from the literature and a series of significantly more complex problems of four to six objective functions. Our findings suggest that the proposed method greatly outperforms its predecessor, by solving significantly less models in emphatically less time and allowing easy and timely solution of hard or practically impossible, in terms of time and processing requirements, problems of numerous objective functions. AUGMECON-R, furthermore, solves the limitation of unknown nadir points, by using very low or zero-value lower bounds without surging the time and resources required.

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Section: A Brief Overview Of the Augmented ε-Constraint Methodsmentioning

confidence: 99%

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

et al. 2020

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“…The nature of cellular manufacturing systems in manufacturing products in mid-variety and mid-volume, the product mix variation is not too far-fetched. Bootaki et al [7] studied about configuring manufacturing cells when product mix variation occurs. The part-machine incidence matrix should be changed because of product mix variation.…”

Section: Introductionmentioning

confidence: 99%

“…The part-machine incidence matrix should be changed because of product mix variation. They formulated the problem with two different criteria which considered to workers' relation together and each worker experts [7]. Sakhaii et al [27] considered decreasing the machinery moving costs, lack costs and worker cost.…”

Section: Introductionmentioning

confidence: 99%

Nested Bi-level metaheuristic algorithms for cellular manufacturing systems considering workers’ interest

et al. 2021

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Due to the competitive nature of the market and the various products production requirements with short life cycles, cellular manufacturing systems have found a special role in manufacturing environments. Creativity and innovation in products are the results of the mental effort of the workforces in addition to machinery and parts allocation. Assignment of the workforce to cells based on the interest and ability indices is a tactical decision while the cell formation is a strategic decision. To make the correct decision, these two problems should be solved separately while considering their impacts on each other classically. For this reason, a novel bi-level model is designed to make decentralized decisions. Because of the importance of minimizing voids and exceptional element in the cellular manufacturing system, it is considered as a leader at the first level and the assignment of human resources is considered as a follower at the second level. To achieve product innovation and synergy among staff in the objective function at the second level, increasing the worker’s interest in order to cooperate with each other is considered too. Given the NP-Hard nature of cell formation and bi-level programming, nested bi-level genetic algorithm and particle swarm optimization are developed to solve the mathematical model. Various test problems have been solved by applying these two methods and validated results have been shown the efficiency of the proposed model. Also, real experimental comparisons have been presented. These results in contrast with previous works have been shown the minimum amount of computational time, cell load variation, total intercellular movements, and total intracellular movements of this new method. These effects have an important role in order to the improvement of cellular manufacturing behavior.

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“…Group Technology (GT) that was introduced by Mitrofanov [1] is a theory of management based on the principle that similar things should be done similarly [2]. Cellular manufacturing (CM), which is the implementation of group technology, is an important modern manufacturing alternative to achieve mid-volume and high-variety production [3]. CM is a hybrid system, which takes the advantage of flexibility of job shops and efficiency of flow shops [3] [4].…”

Section: Introductionmentioning

confidence: 99%

“…Cellular manufacturing (CM), which is the implementation of group technology, is an important modern manufacturing alternative to achieve mid-volume and high-variety production [3]. CM is a hybrid system, which takes the advantage of flexibility of job shops and efficiency of flow shops [3] [4]. Design of CM systems is a three-step process that consists of cell formation, intracellular layout and cell layout [5].…”

Section: Introductionmentioning

confidence: 99%

İmalat Hücresi Oluşturulması İçin Farklı Kümeleme Yöntemlerinin Performans Karşılaştırması

Kiriş

Tüysüz

2017

SAÜ Fen Bilimleri Enstitüsü Dergisi

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This study refers to cell formation, which is the fundamental and important stage of cellular manufacturing system design. Three widely used methods that are K-means clustering algorithm, average-linkage clustering algorithm and fuzzy clustering using expectation maximization algorithm for cell formation problem are studied. A real life application of these methods for the design of cylinder department of a construction equipment manufacturer is performed. The performance of each applied algorithm is evaluated according to intracellular voids, intracellular processing intensity and intercellular transportation measures. The application results indicate that K-means clustering algorithm, which is the most widely applied and most known one of classical clustering algorithms, is still an effective method for cell formation.

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New criteria for configuration of cellular manufacturing considering product mix variation

Cited by 15 publications

References 40 publications

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

Nested Bi-level metaheuristic algorithms for cellular manufacturing systems considering workers’ interest

İmalat Hücresi Oluşturulması İçin Farklı Kümeleme Yöntemlerinin Performans Karşılaştırması

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