Standalone closed-form formula for the throughput rate of asynchronous normally distributed serial flow lines

Aboutaleb, Adam; Kang, Parminder Singh; Hamzaoui, Raouf; Duffy, Alistair

doi:10.1016/j.jmsy.2016.12.006

Cited by 4 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Addressing furnace limitations is a key step to enable both production lines to run closer to their ideal speeds, and the study results inform the business case for redesign of the furnace or capital investment for new technology. Also found to be significant in this study are human factors, such as production scheduling, learning curve, unambitious target setting, crew differences and machine breakdowns from improper maintenance [2,3,22,23]. This finding indicates that sizeable improvements in speed loss can be made by applying lean and TPM approaches, such as the speed loss method applied by Nakajima [1] and the 5 Whys analysis used by Benjamin et al [8].…”

Section: Discussionmentioning

confidence: 57%

Why slow down? Factors affecting speed loss in process manufacturing

Trattner

Hvam

Haug

2019

Int J Adv Manuf Technol

View full text Add to dashboard Cite

 Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

show abstract

Section: Discussionmentioning

confidence: 57%

Why slow down? Factors affecting speed loss in process manufacturing

Trattner

Hvam

Haug

2019

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…(2013 and2014) proposed a mixed integer linear programming model combined with dual encoding and smart-decoding based genetic algorithms framework to address the makespan minimization problem for a hybrid flow shop with a parallel batching system. Aboutaleb et al (2017) used simulation modeling and data mining approach to develop the standalone closed-loop formula for a throughput rate of normally distributed asynchronous human-dependent serial flow lines. Pfeiffer et al (2016) used a combination of simulation-based approach and statistical learning methods to improve the to develop a multimodel prediction of manufacturing lead times.…”

Section: Introductionmentioning

confidence: 99%

Continuous process improvement implementation framework using multi-objective genetic algorithms and discrete event simulation

Kang

Bhatti

2018

BPMJ

View full text Add to dashboard Cite

Purpose Continuous process improvement is a hard problem, especially in high variety/low volume environments due to the complex interrelationships between processes. The purpose of this paper is to address the process improvement issues by simultaneously investigating the job sequencing and buffer size optimization problems. Design/methodology/approach This paper proposes a continuous process improvement implementation framework using a modified genetic algorithm (GA) and discrete event simulation to achieve multi-objective optimization. The proposed combinatorial optimization module combines the problem of job sequencing and buffer size optimization under a generic process improvement framework, where lead time and total inventory holding cost are used as two combinatorial optimization objectives. The proposed approach uses the discrete event simulation to mimic the manufacturing environment, the constraints imposed by the real environment and the different levels of variability associated with the resources. Findings Compared to existing evolutionary algorithm-based methods, the proposed framework considers the interrelationship between succeeding and preceding processes and the variability induced by both job sequence and buffer size problems on each other. A computational analysis shows significant improvement by applying the proposed framework. Originality/value Significant body of work exists in the area of continuous process improvement, discrete event simulation and GAs, a little work has been found where GAs and discrete event simulation are used together to implement continuous process improvement as an iterative approach. Also, a modified GA simultaneously addresses the job sequencing and buffer size optimization problems by considering the interrelationships and the effect of variability due to both on each other.

show abstract

Throughput Concepts

2024

Manufacturing System Throughput Excellence

View full text Add to dashboard Cite

Standalone closed-form formula for the throughput rate of asynchronous normally distributed serial flow lines

Cited by 4 publications

References 26 publications

Why slow down? Factors affecting speed loss in process manufacturing

Why slow down? Factors affecting speed loss in process manufacturing

Continuous process improvement implementation framework using multi-objective genetic algorithms and discrete event simulation

Throughput Concepts

Contact Info

Product

Resources

About