Fields of action towards automated facility layout design and optimization in factory planning – A systematic literature review

Burggräf, Peter; Adlon, Tobias; Hahn, Viviane; Schulz-Isenbeck, Timm

doi:10.1016/j.cirpj.2021.09.013

Cited by 19 publications

(10 citation statements)

References 44 publications

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“…In addition, among this study's findings, evidence suggesting the applicability of the ALDEP method to the value-added wood products industry was present (Budianto et al, 2020). Burggräf et al (2021) stated that identifying action fields is a pre-requirement for developing a functional and integrated system for automatic layout design that could be used in practice. For this purpose, they conducted a systematic literature review.…”

Section: Introductionmentioning

confidence: 68%

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Facility layout planning through the ALDEP Method in the wooden cable reels industry

İnce,

Taşdemir

2024

Turkish Journal of Forestry | Türkiye Ormancılık Dergisi

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Facility layout planning plays a pivotal role in manufacturing system design, impacting vital metrics such as lead time, handling costs, and space optimization. While a significant portion of research has been invested in refining existing facility layouts, there is a noticeable research gap in devising optimized layouts for new establishments, especially in the value-added wood products domain. Addressing this lacuna, this research focused on designing an efficient department-level layout for a wooden cable drums manufacturing facility in an area of 4150 m2. This facility included both office and production areas. The investigative process was segmented into four distinct phases: Deciding the strategic positioning of the facility on the available plot, defining the functional and spatial requirements for each department, establishing the intricate relationship dynamics between these individual units, and rigorously documenting the most optimal department-level facility layout. For precision in layout creation, the ALDEP algorithm was employed, which was further visualized to offer a comprehensive three-dimensional representation. The final layout seamlessly organized seven departments within the 1st Floor Office Area, eight in the 2nd Floor Office Area, and thirteen within the Production Floor. Efficiency evaluation of these areas yielded scores of -811, 184, and -318, respectively. Conclusively, this research furnished actionable insights for manufacturers within the wood products sector and was expected to be an invaluable reference for academics delving into facility planning and value-added wood products manufacturing.

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Section: Introductionmentioning

confidence: 68%

“…For this purpose, they conducted a systematic literature review. They identified the need for actions in multicriteria optimization, the layout evaluation and selection process, the existing implemented algorithms, and the integration of the human planner (Burggräf et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Facility layout planning through the ALDEP Method in the wooden cable reels industry

İnce,

Taşdemir

2024

Turkish Journal of Forestry | Türkiye Ormancılık Dergisi

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“…It was subsequently enhanced as PRISMA 2020, which includes reporting guidance that advance sophisticated methodologies for identifying, selecting, assessing, and synthesising research [33]. A few examples of the PRISMA method in the manufacturing area are sustainable development [28], sustainable manufacturing [35], lean manual assembly [30], manufacturing data mining [7], factory planning [10], additive manufacturing [27], manufacturing methodology [19], augmented reality manufacturing [22] and Industry 4.0 [39]. As a result, this technique is suited for systematically describing the actions and methods for improving stamping die production via searching and identification, screening, eligibility, data abstraction, and analysis.…”

Section: Methodsmentioning

confidence: 99%

Improvement Activities in Stamping Die Manufacturing: A Systematic Literature Review

Rozeman¹,

Adesta²,

Sophian³

et al. 2023

IJEMM

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Die making is regarded as the mother of all industries. In the manufacturing world, this industry is thought to have existed for a long time. There is a need to address the improvement activities in this area in order to assess their relevance, results, and impacts on its latest industrial development. However, there were insufficient studies that systematically reviewed the existing literature related to stamping die manufacturing's latest improvement activities. As a result, the current article conducted a systematic literature review on stamping die manufacturing improvement activities. The present study applies the integrated multiple research design, and the review was based on the publication standard, namely the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This research is using two leading databases, namely Scopus and Science Direct, and five supporting databases, namely Emerald Insight, IEE Explore, Wiley Online, Taylor & Francis, and Google Scholar. Using thematic analysis, this review has six main themes: design, machining, finishing, trials, and overall improvements. These six major themes were subdivided into 20 sub-themes. The findings show that the researchers are covering most of the improvements in the main elements of die manufacturing processes. Based on this study, the contribution for practical purposes of stamping die manufacturing improvements was identified. The study contributes significant findings, such as detailed improvement activities that are specific to the targeted issues but have the potential to be adapted or imitated by other practitioners and future researchers.

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“…Meta-heuristics or heuristics, such as the Computerized Relative Allocation of Facilities Algorithm [18], are commonly used as approximating approaches. Multiple literature reviews have analyzed the use of meta-heuristics for the facility layout problem [8, [19][20][21]. They conclude that the majority of approaches are based on a GA, simulated annealing (SA), large adaptive neighborhood search, or TS.…”

Section: Factory Layout Planning Approachesmentioning

confidence: 99%

Multi objective factory layout planning using simulation-based reinforcement learning

Klar¹,

Schworm²,

Wu³

et al. 2023

Preprint

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Factory layout planning aims at finding an optimized layout configuration under consideration of varying influences such as the material flow characteristics. Manual layout planning can be characterized as a complex decision-making process due to a large number of possible placement options. Automated planning approaches aim at reducing the manual planning effort by generating optimized layout variants in the early stages of layout planning. Recent developments have introduced Reinforcement Learning (RL) based planning approaches that allow to optimize a layout under consideration of a single optimization criterion. However, within layout planning, multiple partially conflicting planning objectives have to be considered. Such multiple objectives are not considered by existing RL-based approaches. This paper addresses this research gap by presenting a novel RL-based layout planning approach that allows consideration of multiple objectives for optimization. Furthermore, existing RL-based planning approaches only consider analytically formulated objectives such as the transportation distance. Consequently, dynamic influences in the material flow are neglected which can result in higher operational costs of the future factory. To address this issue, a discrete event simulation module is developed that allows simulating manufacturing and material flow processes simultaneously for any layout configuration generated by the RL approach. Consequently, the presented approach considers material flow simulation results for multi-objective optimization. In order to investigate the capabilities of RL-based factory layout planning, different RL architectures are compared based on a simplified application scenario. In terms of optimization objectives, the throughput time, media supply, and clarity of the material flow are considered. The best performing architecture is then applied to an industrial planning scenario with 43 functional units to illustrate the approach. Furthermore, the performance of the RL approach is compared to the manually planned layout and to the results generated by a combined version of the genetic algorithm and tabu search. The results indicate that the RL approach is capable of improving the manually planned layout significantly. Furthermore, it reaches comparable results for the throughput time and better results for the clarity of the material flow compared to the combined version of a genetic algorithm and tabu search.

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Fields of action towards automated facility layout design and optimization in factory planning – A systematic literature review

Cited by 19 publications

References 44 publications

Facility layout planning through the ALDEP Method in the wooden cable reels industry

Facility layout planning through the ALDEP Method in the wooden cable reels industry

Improvement Activities in Stamping Die Manufacturing: A Systematic Literature Review

Multi objective factory layout planning using simulation-based reinforcement learning

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