Layout Design for Efficient Material Flow Path

Leno, I. Jerin; Saravanasankar, S.; Ponnambalam, S. G.

doi:10.1016/j.proeng.2012.06.110

Cited by 12 publications

(4 citation statements)

References 11 publications

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“…Heavy traffic congestion, in particular, causes low utilization of machines, delays in production schedules, and inconsistent product quality, to name a few. Because transportation and storage of materials within a plant account for approximately 70 percent of the operating costs [7], managing OHT traffic is a major challenge in a large-scale fab.…”

Section: Introductionmentioning

confidence: 99%

Dynamic OHT Routing Using Travel Time Approximation Based on Deep Neural Network

Choi,

Yu,

Choi

2024

IEEE Access

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This study proposes an effective dynamic OHT routing approach to handle a substantial volume of wafer transport in a modern large semiconductor fabrication plant. The proposed approach aims to overcome challenges faced by previous approaches whose applicability is limited in conditions where the underlying distribution of traffic conditions varies over a short period. The approach comprises two models to explicitly approximate the congestion-aware travel times of different parts of the candidate route based on the current rail conditions, to evaluate the traffic conditions when routing. First, the local path approximation model heuristically evaluates the travel time of paths within a short range. Second, the global path approximation model evaluates the travel time of a distant range using a deep neural network. The simulation experiments show that the proposed approach outperforms the benchmark algorithms regarding delivery time and throughput, exhibiting 11.34% lower delivery time compared to a reinforcement-learningbased benchmark model. The proposed approach successfully integrates environmental information to evaluate congestion in a complex fab and optimize the routes of a large fleet of OHTs while balancing the traffic throughout a dynamic system.INDEX TERMS congestion-aware, deep neural network, dynamic vehicle routing, large scale rail network, overhead hoist transport, semiconductor manufacturing

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Section: Introductionmentioning

confidence: 99%

Dynamic OHT Routing Using Travel Time Approximation Based on Deep Neural Network

Choi,

Yu,

Choi

2024

IEEE Access

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“…By adopting a holistic approach to manufacturing cycle analysis, these studies enable the identification of areas where time and resources are squandered, thereby facilitating the implementation of measures to bolster efficiency. Furthermore, investigations by [42][43][44] indicate that the MCE method can be leveraged to improve shipyard layouts by scrutinizing material flow and production activities and identifying areas requiring layout modifications or enhancements to optimize workflow and mitigate conflicts. The application of the MCE method also yields benefits in the identification of quality issues and bolsters the effectiveness of quality management.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Manufacturing Cycles to Improve Production: Application in the Traditional Shipyard Industry

Jebbor,

Benmamoun,

Hachimi

2023

Processes

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This article explores the important role of traditional shipyards in the global maritime industry, covering aspects of construction, repair, and maintenance. With the advent of faster manufacturing techniques, traditional shipyards face important challenges, such as planning errors, coordination problems, delivery delays, and underutilization of technology, which results in high costs, reduced productivity, and prolonged projects. The application of Manufacturing Cycle Efficiency (MCE) emerged as an important solution to significantly increase production efficiency. MCE empowers shipyards to deal effectively with waste, bottlenecks, and disruptions, thereby increasing performance, competitiveness, and profitability. Using a comprehensive approach that uses both qualitative and quantitative methods, including field surveys, and in-depth interviews in the traditional shipyard industry, this research identifies Nonvalue-Added (NVA) processes, conducts process mapping, and calculates MCE. The findings reported in this article underscore the significant wastage in the production process, indicating an urgent need for improvement, given the current average MCE value of 67.08%, indicating considerable room for improvement. This article provides innovative perspectives on optimizing the traditional shipyard industry through production cycle efficiencies while offering actionable recommendations. Key focus areas include integrating management systems, adopting advanced technologies, and implementing sustainable strategies to improve MCE, especially by reducing nonvalue-added time wastage, such as inspection and storage. By implementing strategies that optimize production, minimize waste, and overcome the challenges of global competition, this research contributes to improving MCE. In conclusion, this study is an invaluable guide for industry stakeholders, enabling them to enhance their competitiveness and adapt effectively to a dynamic business environment.

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“…Furthermore, investigations by Centobelli et al, (2016), Jerin Leno et al, (2012) and Purnamasari et al, (2018) indicate that the MCE method can be leveraged to improve shipyard layouts by scrutinizing material flow and production activities and identifying areas requiring layout modifications or enhancements to optimize workflow and mitigate conflicts. The application of the MCE method also yields benefits in the identification of quality issues and bolsters the effectiveness of quality management.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Traditional Shipyard Industry: Enhancing Manufacturing Cycle Efficiency for Enhanced Production Process Performance

Fitriadi,

Ayob

2023

IJIETOM

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The traditional shipbuilding industry is crucial in the global maritime sector, contributing to ship construction, repair, and maintenance. However, it faces challenges in improving production process efficiency. This article explores strategies to optimize the industry's manufacturing cycle. A comprehensive literature review identified key challenges, including production planning errors, coordination issues, material delivery delays, and limited technology integration. These challenges result in higher costs, reduced productivity, and project delays. To address these issues, the article proposes integrating efficient management systems like Lean Manufacturing and Total Quality Management to enhance the production cycle's efficiency. Implementing these systems eliminates non-value-added activities, simplifies processes, and reduces wait times. Additionally, the Manufacturing Cycle Efficiency method is valuable for analyzing production processes, identifying waste, bottlenecks, and interruptions, and optimizing workflows. In conclusion, this article provides insights and strategies to optimize the traditional shipbuilding industry. By integrating efficient management systems, utilizing the MCE method, and embracing sustainability practices, shipyards can enhance production cycle efficiency, increase productivity, and gain a competitive advantage, supporting industry growth and paving the way for a sustainable future.

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Layout Design for Efficient Material Flow Path

Cited by 12 publications

References 11 publications

Dynamic OHT Routing Using Travel Time Approximation Based on Deep Neural Network

Dynamic OHT Routing Using Travel Time Approximation Based on Deep Neural Network

Optimizing Manufacturing Cycles to Improve Production: Application in the Traditional Shipyard Industry

Optimizing Traditional Shipyard Industry: Enhancing Manufacturing Cycle Efficiency for Enhanced Production Process Performance

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