Cellular manufacturing design 1996–2021: a review and introduction to applications of Industry 4.0

YounesSinaki, Roohollah; Sadeghi, Azadeh; Mosadegh, Hadi; Almasarwah, Najat; Süer, Gürsel A.

doi:10.1080/00207543.2022.2105763

Cited by 12 publications

(4 citation statements)

References 471 publications

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“…First, our study adds a layer of "product variant" which are combinations of multiple parts, and we examine the demand uncertainty of product variants (but not parts directly). We consider that the demand uncertainty of product variants is closer to the actual market information associated with product variety [11] and supply chain management [6]. Second, our study aims to evaluate which product variants (subject to demand uncertainty) tend to affect the layout's performance more (i.e., the performance's sensitivity to demand changes).…”

Section: Literature Review 21 Comparison Studies Of Functional and Ce...mentioning

confidence: 99%

“…Industry 4.0, as an important technological trend, refers to a range of interconnected technologies such as cloud computing, the Internet of Things, and cyber-physical systems, which can support production systems in coping with market challenges [4]. Its potential has been discussed in the contexts of product customization [5], cellular manufacturing [6], mass personalization production [7], and sustainable manufacturing [8]. To deploy new technologies of Industry 4.0, decision-making methods have been proposed to support the selection of digital technologies [9] and sustainable machining process [10].…”

Section: Introductionmentioning

confidence: 99%

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Simulation-Based Study of the Resilience of Flexible Manufacturing Layouts Subject to Uncertain Demands of Product Variants

Li,

Eshragh,

Ebufegha

2023

Sustainability

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Due to market competition, manufacturers typically produce their products with different customized features, leading to the production of product variants (or a product family). Since the market trend can change swiftly, the demands of individual product variants can be difficult to predict. Two flexible manufacturing layouts are commonly considered: functional and cellular layouts. While the functional layout is more resilient to demand changes due to better resource pooling, the cellular layout can be more productive on some occasions due to better routing efficiency. In this context, the purpose of this paper is to quantify and study the criticality of product variants. The criticality score of a product variant can estimate and rank which product variants can sensitively cause bottlenecks in the functional and cellular layouts. The proposed criticality analysis starts with the estimation of bottleneck machines. Through the dependency information of machines and parts, we can estimate the criticality of product variants. The criticality analysis is demonstrated and examined through a simulation study with a study case involving the production of five furniture products with 16 unique parts using 11 machines. The simulation results show that the productions with more critical product variants tend to deteriorate the completion time of the cellular layout more severely. In practice, manufacturers can use the proposed criticality analysis to evaluate the criticality of product variants and support their facility layout decision. For example, if more demand for critical products is expected, the layout should support more resource pooling (e.g., functional layouts).

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Section: Literature Review 21 Comparison Studies Of Functional and Ce...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simulation-Based Study of the Resilience of Flexible Manufacturing Layouts Subject to Uncertain Demands of Product Variants

Li,

Eshragh,

Ebufegha

2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…According to the findings, compared with the sequential design approach, decisions about Cell Formation, inter-and intracellular layouts, and part routing result in a significant decrease in overall material handling costs. In order to reduce the overall cost of part relocations, as well as cell reconfigurations, a bi-objective optimization model is created to incorporate the Cell Formation and inter/intracell layouts in continuous space while taking fuzzy conditions into consideration [35]. In order to identify concurrent machine cells, the layout of facilities (machines, workstations, and cells), processing routes, and processing speed of operations while minimizing electric energy consumption and material handling costs, Forghani et al [36] proposed a hybrid solution approach that combines GAs and SA (Simulated Annealing).…”

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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“…In PMS, a group of similar machines is grouped in a certain area to serve one or more of several objectives such as minimizing the total tardiness, number of tardy jobs, maximum tardiness, and flow time (YounesSinaki, et al, 2022). Various methods including metaheuristic, mathematical model, heuristic algorithms and others were proposed in the literature to schedule (n) jobs into (m) identical parallel machines given different objectives such as minimization of the total tardiness, number of tardy jobs, or maximum tardiness and makespan (Almasarwah & Süer, 2021;Kim & Kim 2021;Verma et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

An Accurate and Robust Genetic Algorithm to Minimize the Total Tardiness in Parallel Machine Scheduling Problems

RAMADAN,

ALMASARWAH,

ABDELALL

et al. 2023

Management and Production Engineering Review

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This paper uses a Genetic Algorithm (GA) to reduce total tardiness in an identical parallel machine scheduling problem. The proposed GA is a crossover-free (vegetative reproduction) GA but used for four types of mutations (Two Genes Exchange mutation, Number of Jobs mutation, Flip Ends mutation, and Flip Middle mutation) to make the required balance between the exploration and exploitation functions of the crossover and mutation operators. The results showed that use of these strategies positively affects the accuracy and robustness of the proposed GA in minimizing the total tardiness. The results of the proposed GA are compared to the mathematical model in terms of the time required to tackle the proposed problem. The findings illustrate the ability of the propounded GA to acquire the results in a short time compared to the mathematical model. On the other hand, increasing the number of machines degraded the performance of the proposed GA.

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Cellular manufacturing design 1996–2021: a review and introduction to applications of Industry 4.0

Cited by 12 publications

References 471 publications

Simulation-Based Study of the Resilience of Flexible Manufacturing Layouts Subject to Uncertain Demands of Product Variants

Simulation-Based Study of the Resilience of Flexible Manufacturing Layouts Subject to Uncertain Demands of Product Variants

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

An Accurate and Robust Genetic Algorithm to Minimize the Total Tardiness in Parallel Machine Scheduling Problems

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