Simulation and scheduling implementation study of TFT-LCD Cell plants using Drum–Buffer–Rope system

Wu, Horng-Huei; Chen, Ching‐Piao; Tsai, Chih‐Hung; Yang, Chun-Jheng

doi:10.1016/j.eswa.2010.05.075

Cited by 16 publications

(7 citation statements)

References 10 publications

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“…The time-related factor most frequently included in the DBR application literature is the cycle time (Atwater and Chakravorty 2002;Gupta, Ko, and Min 2002;Steele et al 2005;Sirikrai and Yenradee 2006;Patti, Watson, and Blackstone 2008;Wu et al 2010;Georgiadis and Politou 2013;Qiao and Wu 2013). The cycle time is described as the elapsed time between releasing a job at the beginning of routing until the job reaches the end of the process.…”

Section: Toc Production Applications and Operational Performance Measmentioning

confidence: 99%

Implementation of S-DBR in four manufacturing SMEs: a research case study

Benavides

Landeghem

2015

Production Planning & Control

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Production planning and control systems (PPCs) are tools that have a major effect on the performance of manufacturing companies. An inappropriate determination of the applicability of PPCs could seriously jeopardise the ability to achieve competitive priorities. This mistake can be expensive for any company, but proper implementation is particularly critical for small and medium enterprises (SMEs), which are characterised by limited access to financial resources. Simplified Drum-Buffer-Rope (S-DBR) is a PPC control approach that is characterised by minimum detail in the planning stage and a primary emphasis on the control of execution. Therefore, this approach is a suitable choice for the highly variable context of SMEs. This study aims to explore the practical issues related to S-DBR implementation in four Ecuadorian SMEs through case study research. The case analysis within this study first identifies the choices made in the implementation process design within the four companies according to process and product characteristics. We then conduct a cross-case analysis to explore the effects of the S-DBR implementation process designs on a group of performance measures. Our research findings provide new insights into the S-DBR implementation process in the context of SMEs, and the effects of this approach on performance measures.

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Section: Toc Production Applications and Operational Performance Measmentioning

confidence: 99%

Implementation of S-DBR in four manufacturing SMEs: a research case study

Benavides

Landeghem

2015

Production Planning & Control

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“…Chen et al [6] proposed the iterative simulation-based release policy for color filter fabs based on iterative simulation to significantly reduce idle time and increase throughput. Wu et al [39] implemented drum-buffer-rope (DBR) for TFT-LCD cell plants to generate efficient schedule. Lin et al [25] analyzed the effect of lot release time and dispatching rule to the TFT-LCD cell process performance, which is the setup time for rubbing machine and cycle time.…”

Section: Literature Reviewmentioning

confidence: 99%

Capacity planning in thin film transistor – Liquid crystal display cell process

Chen

Pratama

et al. 2016

Journal of Manufacturing Systems

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“…DBR can be used here to improve the organization performance when it is limited by capacity constraint resources (CCR) or bottleneck i.e. identification and exploitation of CCR/bottleneck processes to maximize their utilization ( [5] and [7]). CCR/bottleneck utilization limits the organization throughput, it needs to maximize in order to maintain on time deliveries, minimum WIP level and production cost, maximum profit and quality.…”

Section: ) Go To Step 1 If Any System's Constraint Is Brokenmentioning

confidence: 99%

“…As lack of material at CCR or underutilization of CCR can have devastating effects on throughput. In summary, the main aim of DBR is to schedule the material flow through the production line to produce according to customer demand by keeping lead time, WIP and production cost as lower as possible [7]. From current research's perspective dynamic market demand can be seen as one of the causes of variability (in terms of processing times, setup times and routings followed by different work types) in the system, as in high variety/low volume manufacturing variable product demand can cause more often setups without an optimal schedule.…”

Section: ) Go To Step 1 If Any System's Constraint Is Brokenmentioning

confidence: 99%

A Multi-Objective Optimization Approach Using Genetic Algorithms for Quick Response to Effects of Variability in Flow Manufacturing

Khalil¹,

Stockton²,

Kang³

et al. 2012

IJACSA

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Abstract-This paper exemplifies a framework for development of multi-objective genetic algorithm based job sequencing method by taking account of multiple resource constraints. Along this, Theory of Constraints based Drum-Buffer-Rope methodology has been combined with genetic algorithm to exploit the system constraints. This paper introduces the Drum-Buffer-Rope to exploit the system constraints, which may affect the lead times, throughput and higher inventory holding costs. Multi-Objective genetic algorithm is introduced for job sequence optimization to minimize the lead times and total inventory holding cost, which includes problem encoding, chromosome representation, selection, genetic operators and fitness measurements, where Queuing times and Throughput are used as fitness measures. Along this, paper provides a brief comparison of proposed approach with other optimisation approaches. The algorithm generates a sequence to maximize the throughput and minimize the queuing time on bottleneck/Capacity Constraint Resource (CCR). Finally, Results are analysed to show the improvement by using current research framework.

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Simulation and scheduling implementation study of TFT-LCD Cell plants using Drum–Buffer–Rope system

Cited by 16 publications

References 10 publications

Implementation of S-DBR in four manufacturing SMEs: a research case study

Implementation of S-DBR in four manufacturing SMEs: a research case study

Capacity planning in thin film transistor – Liquid crystal display cell process

A Multi-Objective Optimization Approach Using Genetic Algorithms for Quick Response to Effects of Variability in Flow Manufacturing

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