MARC details
| 000 -LEADER |
|---|
| fixed length control field |
06823namaa22004331i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
EG-GICUC |
| 005 - أخر تعامل مع التسجيلة |
|---|
| control field |
20260312163643.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
260312s2025 ua a|||frm||| 000 0 eng d |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloguing agency |
EG-GICUC |
| Language of cataloging |
eng |
| Transcribing agency |
EG-GICUC |
| Modifying agency |
EG-GICUC |
| Description conventions |
rda |
| 041 0# - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
eng |
| Language code of summary or abstract |
eng |
| -- |
ara |
| 049 ## - Acquisition Source |
|---|
| Acquisition Source |
Deposit |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
005.12 |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) |
|---|
| Classification number |
005.12 |
| Edition number |
21 |
| 097 ## - Degree |
|---|
| Degree |
Ph.D |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) |
|---|
| Local Call Number |
Cai01.20.04.Ph.D.2025.Sh.L |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Authority record control number or standard number |
Shereen Ali Abd Al Fattah Al Fayoumi, |
| Preparation |
preparation. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Leveraging machine learning and genetic algorithms in optimizing mass customization products / |
| Statement of responsibility, etc. |
by Shereen Ali Abd Al Fattah Al Fayoumi ; SupervisionProf. Neamat Eltazi, Prof. Amal Elgammal. |
| 246 15 - VARYING FORM OF TITLE |
|---|
| Title proper/short title |
الاستفادة من التعلم الألي و الخوارزمية الجينية في تحسين الانتاج الضخم المخصص |
| 264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
2025. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
100 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
30 cm. + |
| Accompanying material |
CD. |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Source |
rda content |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
Unmediated |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
volume |
| Source |
rdacarrier |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Thesis (Ph.D)-Cairo University, 2025. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Bibliography: pages 95-100. |
| 520 #3 - SUMMARY, ETC. |
|---|
| Summary, etc. |
Planning in mass-customization supply and manufacturing processes is a complex task that requires <br/>ongoing optimization to minimize time and cost across many options in large production volumes. Various <br/>artificial intelligence (AI) techniques are now used to find planning solutions for supply chains, which <br/>include suppliers, manufacturers, wholesalers, and customers. Continual optimization of these chains is <br/>vital to improve their overall performance. However, the manufacturing sector remains a crucial stage <br/>within the supply chain, needing continuous refinement. Mass Customization Manufacturing, a production <br/>method that involves high-volume manufacturing with a wide variety of materials, presents unique <br/>challenges because it must balance high volume with high variability. Despite its importance, research in <br/>this area remains limited. <br/>To our knowledge, genetic algorithms have not been applied to minimize both time and cost in <br/>mass customization manufacturing simultaneously. Additionally, machine learning techniques present a <br/>promising opportunity to optimize supply and manufacturing planning as practical solutions for industrial <br/>optimization problems. <br/>In this study, we propose an artificial intelligence-based solution that utilizes genetic algorithms to <br/>develop a model designed to minimize the time and cost associated with mass-customized orders. <br/>Furthermore, we examine supervised machine learning and deep learning techniques for planning <br/>manufacturing time and cost across various scenarios in a large-scale real-life pilot study within the bicycle <br/>manufacturing domain. <br/>Our proposed optimization model employs two approaches to solve the problem. The first uses a <br/>genetic algorithm with a single-objective function to optimize either time or cost, and it also uses the multi-<br/>objective NSGA-II algorithm to optimize both at the same time. The second approach tests multiple <br/>machine learning models, including K-Nearest Neighbors (K-NN) with regression, Random Forest, and <br/>Decision Tree from traditional methods, along with Neural Networks and Ensembles as deep learning <br/>options. Additionally, Reinforcement Learning was used in cases where real-world data or historical <br/>experiences were not available. <br/>The pilot study's training performance was evaluated using cross-validation, supported by statistical <br/>analysis methods, including the t-test and the Wilcoxon test. The effectiveness of the proposed models was <br/>tested through a real-world case study, showing that genetic algorithms for mass customization optimization <br/>outperformed expert estimations in finding efficient solutions. Additionally, the results showed that <br/>machine learning techniques outperformed genetics, deep learning, and reinforcement learning approaches, <br/>with K-NN combined with regression producing the best outcomes. |
| 520 #3 - SUMMARY, ETC. |
|---|
| Summary, etc. |
يعدّ التخطيط في عمليات التصنيع والتوريد ضمن نظام التصنيع حسب الطلب الجماعي مهمة معقدة تتطلب تحسينًا مستمرًا لتقليل الوقت والتكلفة عبر مجموعة واسعة من الخيارات في الإنتاج واسع النطاق. تُستخدم تقنيات الذكاء الاصطناعي على نطاق واسع لتحسين أداء سلسلة التوريد، ولكن تظل مرحلة التصنيع، وخاصة في التصنيع حسب الطلب الجماعي، تمثل تحديات فريدة نظرًا للحاجة إلى تحقيق توازن بين الحجم الكبير والتنوع العالي. وعلى الرغم من أهمية هذا المجال، إلا أن الأبحاث فيه لا تزال محدودة. تقدم هذه الدراسة حلاً يعتمد على الذكاء الاصطناعي باستخدام الخوارزميات الجينية لتقليل الوقت والتكلفة في أوامر التصنيع حسب الطلب. كما تستكشف تقنيات التعلم الآلي والتعلم العميق لتخطيط الوقت والتكلفة في سيناريوهات واقعية، وخصوصًا في صناعة الدراجات الهوائية. يتبع النموذج المقترح استراتيجيتين: الأولى تعتمد على الخوارزميات الجينية، بما في ذلك خوارزمية NSGA-II، لتحسين هدف واحد أو عدة أهداف؛ أما الثانية فتستخدم عدة طرق من التعلم الآلي مثل الجار الأقرب (K-NN)، الغابات العشوائية، والشبكات العصبية. وقد أظهرت النتائج أن طريقة الجار الأقرب مع الانحدار كانت الأكثر فعالية، كما تم استخدام التعلم التعزيزي في الحالات التي تفتقر إلى البيانات التاريخية أو الخبرات الواقعية. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE |
|---|
| Issues CD |
Issues also as CD. |
| 546 ## - LANGUAGE NOTE |
|---|
| Text Language |
Text in English and abstract in Arabic & English. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Information Systems |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
نظم المعلومات |
| 653 #1 - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
Supply Chain |
| -- |
Mass Customization Manufacturing |
| -- |
Optimization |
| -- |
Genetic Algorithm |
| -- |
Machine Learning |
| -- |
Deep Learning |
| -- |
Reinforcement Learning |
| -- |
سلاسل الإمداد |
| -- |
تصنيع الانتاج الضخم |
| 700 0# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Neamat Eltazi |
| Relator term |
thesis advisor. |
| 700 0# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Amal Elgammal |
| Relator term |
thesis advisor. |
| 900 ## - Thesis Information |
|---|
| Grant date |
01-01-2025 |
| Supervisory body |
Neamat Eltazi |
| -- |
Amal Elgammal |
| Universities |
Cairo University |
| Faculties |
Faculty of Computers and Artificial Intelligence |
| Department |
Department of Information Systems |
| 905 ## - Cataloger and Reviser Names |
|---|
| Cataloger Name |
Shimaa |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Thesis |
| Edition |
21 |
| Suppress in OPAC |
No |