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Optimization of lead time through genetic algorithm : a case study of equipment manufacturing industry
Joint Authors
Shafiq, Muhammad
Sadiqi, Qasim Rahman
Source
Journal of Engineering Research
Issue
Vol. 10, Issue 2 A (30 Jun. 2022), pp.242-257, 16 p.
Publisher
Kuwait University Academic Publication Council
Publication Date
2022-06-30
Country of Publication
Kuwait
No. of Pages
16
Main Subjects
Abstract EN
Lead time is an important indication of production process and plays a vital role in meeting targets.
The main aim of this study is to optimize lead time of a sugar industry through effective management of resources to ensure timely completion of the projects.
The study is based on a manufacturing process at Heavy Mechanical Complex (HMC) for supply of conventional 02 roller machines to sugar industry.
The proposed model is solved using Genetic Algorithm (GA) with some modifications as per requirement.
The results showed that the implementation of proposed model enable HMC to deliver products as per schedule without increasing planned cost.
Sensitivity analyses are also conducted to find out the impact of various process parameters.
From analyses, it is found that the proposed model facilitate stakeholders in developing strategic relation by helping in meeting agreed terms and conditions.
American Psychological Association (APA)
Sadiqi, Qasim Rahman& Shafiq, Muhammad. 2022. Optimization of lead time through genetic algorithm : a case study of equipment manufacturing industry. Journal of Engineering Research،Vol. 10, no. 2 A, pp.242-257.
https://search.emarefa.net/detail/BIM-1494686
Modern Language Association (MLA)
Sadiqi, Qasim Rahman& Shafiq, Muhammad. Optimization of lead time through genetic algorithm : a case study of equipment manufacturing industry. Journal of Engineering Research Vol. 10, no. 2 A (Jun. 2022), pp.242-257.
https://search.emarefa.net/detail/BIM-1494686
American Medical Association (AMA)
Sadiqi, Qasim Rahman& Shafiq, Muhammad. Optimization of lead time through genetic algorithm : a case study of equipment manufacturing industry. Journal of Engineering Research. 2022. Vol. 10, no. 2 A, pp.242-257.
https://search.emarefa.net/detail/BIM-1494686
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 256-257
Record ID
BIM-1494686