A Framework for Automated Multiobjective Factory Layout Planning using Reinforcement Learning

Klar, Matthias; Langlotz, Pascal; Aurich, Jan C.

doi:10.1016/j.procir.2022.09.099

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…Menurut Klar et al (2022), perencanaan tata letak fasilitas merupakan elemen sentral dalam proses perencanaan pabrik. Pernyataan ini mendukung temuan Stephen & Meyers (2013) yang menyatakan bahwa perancangan fasilitas harus dipertimbangkan untuk memaksimalkan keuntungan dan efisiensi bisnis, serta mengurangi biaya transportasi dan logistik.…”

Section: Tinjauan Pustakaunclassified

Perancangan Tata Letak Pabrik Mie Lethek UD Garuda dengan Metode DBD, ALDEP, CORELAP dan MST

Sonja,

Radeva,

Dianswari

et al. 2023

JIME

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Penelitian ini melakukan perancangan layout fasilitas pada UMKM pembuatan mie lethek tradisional UD Garuda. UD Garuda saat ini belum memiliki pengetahuan dan pertimbangan yang cukup mengenai tata letak fasilitas. Oleh karena itu, penelitian ini bertujuan untuk mencari layout optimal bagi UD Garuda. Perancangan layout dilakukan dengan menggunakan Dimensionless Block Diagram (DBD), Automated Layout Design Program (ALDEP), Computerized Relationship Planning (CORELAP), dan Minimum Spanning Tree (MST). Metode yang paling efektif untuk merancang layout di UD Garuda adalah CORELAP, karena menghasilkan total proximity score yang lebih tinggi sebesar 705 dibandingkan dengan metode DBD (697) dan ALDEP (569). Perancangan layout ini membantu dalam mengoptimalkan hubungan kedekatan antar departemen, mengurangi material handling cost, dan meningkatkan efisiensi produksi secara keseluruhan.

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Section: Tinjauan Pustakaunclassified

Perancangan Tata Letak Pabrik Mie Lethek UD Garuda dengan Metode DBD, ALDEP, CORELAP dan MST

Sonja,

Radeva,

Dianswari

et al. 2023

JIME

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A Holistic Framework for Factory Planning Using Reinforcement Learning

Klar¹,

Mertes²,

Glatt³

et al. 2023

Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes

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The generation of an optimized factory layout is a central element of the factory planning process. The generated factory layout predefines multiple characteristics of the future factory, such as the operational costs and proper resource allocations. However, manual layout planning is often time and resource-consuming and involves creative processes. In order to reduce the manual planning effort, automated, computer-aided planning approaches can support the factory planner to deal with this complexity by generating valuable solutions in the early phase of factory layout planning. Novel approaches have introduced Reinforcement Learning based planning schemes to generate optimized factory layouts. However, the existing research mainly focuses on the technical feasibility and does not highlight how a Reinforcement Learning based planning approach can be integrated into the factory planning process. Furthermore, it is unclear which information is required for its application. This paper addresses this research gap by presenting a holistic framework for Reinforcement Learning based factory layout planning that can be applied at the initial planning (greenfield planning) stages as well as in the restructuring (brownfield planning) of a factory layout. The framework consists of five steps: the initialization of the layout planning problem, the initialization of the algorithm, the execution of multiple training sets, the evaluation of the training results, and a final manual planning step for a selected layout variant. Each step consists of multiple sub-steps that are interlinked by an information flow. The framework describes the necessary and optional information for each sub-step and further provides guidance for future developments.

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A survey on applications of reinforcement learning in spatial resource allocation

Zhang,

Wang,

Mango

et al. 2024

Comput.Urban Sci.

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The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life. As the scale of real-world issues continues to expand and demands for real-time solutions increase, traditional algorithms face significant computational pressures, struggling to achieve optimal efficiency and real-time capabilities. In recent years, with the escalating computational power of computers, the remarkable achievements of reinforcement learning in domains like Go and robotics have demonstrated its robust learning and sequential decision-making capabilities. Given these advancements, there has been a surge in novel methods employing reinforcement learning to tackle spatial resource allocation problems. These methods exhibit advantages such as rapid solution convergence and strong model generalization abilities, offering a new perspective on resolving spatial resource allocation problems. Despite the progress, reinforcement learning still faces hurdles when it comes to spatial resource allocation. There remains a gap in its ability to fully grasp the diversity and intricacy of real-world resources. The environmental models used in reinforcement learning may not always capture the spatial dynamics accurately. Moreover, in situations laden with strict and numerous constraints, reinforcement learning can sometimes fall short in offering feasible strategies. Consequently, this paper is dedicated to summarizing and reviewing current theoretical approaches and practical research that utilize reinforcement learning to address issues pertaining to spatial resource allocation. In addition, the paper accentuates several unresolved challenges that urgently necessitate future focus and exploration within this realm and proposes viable approaches for these challenges. This research furnishes valuable insights that may assist scholars in gaining a more nuanced understanding of the problems, opportunities, and potential directions concerning the application of reinforcement learning in spatial resource allocation.

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A Framework for Automated Multiobjective Factory Layout Planning using Reinforcement Learning

Cited by 3 publications

References 20 publications

Perancangan Tata Letak Pabrik Mie Lethek UD Garuda dengan Metode DBD, ALDEP, CORELAP dan MST

Perancangan Tata Letak Pabrik Mie Lethek UD Garuda dengan Metode DBD, ALDEP, CORELAP dan MST

A Holistic Framework for Factory Planning Using Reinforcement Learning

A survey on applications of reinforcement learning in spatial resource allocation

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