000			05345namaa22004331i 4500
003			EG-GICUC
005			20260119134008.0
008			260105s2025 ua a|||frm||| 000 0 eng d
040			_aEG-GICUC _beng _cEG-GICUC _dEG-GICUC _erda
041	0		_aeng _beng _bara
049			_aDeposit
082	0	4	_a658.403
092			_a658.403 _221
097			_aPh.D
099			_aCai01.20.02.Ph.D.2025.Yo.I
100	0		_aYomna Hossam Eldin Abdel Aziz Gaber, _epreparation.
245	1	3	_aAn integrated framework for planning decisions in serial manufacturing system / _cby Yomna Hossam Eldin Abdel Aziz Gaber ; Supervision Prof. Dr. Ihab Ahmed Fahmy El-khodary, Prof. Dr. Hisham Mohamed Abdelsalam.
246	1	5	_aإطار متكامل لقرارات التخطيط في نظام التصنيع التسلسلي
264		0	_c2025.
300			_a138 Leaves : _billustrations ; _c30 cm. + _eCD.
336			_atext _2rda content
337			_aUnmediated _2rdamedia
338			_avolume _2rdacarrier
502			_aThesis (Ph.D)-Cairo University, 2025.
504			_aBibliography: pages 132-138.
520		3	_aOptimization is paramount in manufacturing, serving as the cornerstone for maintaining competitiveness and long-term viability. This thesis addresses the intricate task of integrating three planning decisions (quantity, quality, and maintenance) within a serial multistage production system. At its core, this research introduces a comprehensive joint optimization model, which serves as the main building block for the solution framework and thoroughly examines the interconnections among these three critical aspects. The novel joint model is designed to encompass buffer allocation for efficiently managing stochastic processing times, part quality inspection planning to minimize defects, and preventive maintenance strategies to mitigate machine deterioration. By integrating these functions, the manufacturing system achieves enhanced overall performance and significant cost reductions. The research demonstrates an average cost reduction of 15% compared to existing models in the literature. The proposed model is formulated as a stochastic mixed-integer nonlinear programming (MINLP) problem, characterized by its NP-hard and combinatorial nature. To effectively address this complex problem, a comprehensive approach is employed, combining a genetic algorithm (GA) as the primary generative method, augmented with hybrid GA variations. Additionally, a simulation-based recursive approach serves as the evaluative technique, both of which collectively form the solution framework. The model and solution algorithm undergo rigorous verification and validation through computational experiments, supported by a design of experiments to fine-tune its parameters. Additionally, a comprehensive discussion of the research findings and their implications is provided, offering valuable insights for practitioners and decision-makers aiming to optimize production processes. This research makes a significant contribution to the existing body of knowledge by presenting a robust framework. This framework not only enhances performance but also reduces costs, effectively addressing the interconnected challenges of quantity, quality, and maintenance in serial multistage production systems.
520		3	_aتقدم الرسالة إطارًا شاملاً للتحسين المشترك في قرارات التخطيط في أنظمة التصنيع التسلسلي، من خلال دمج القرارات المتعلقة بالكمية والجودة والصيانة ضمن إطار عمل تكاملي. يعالج النموذج تخصيص المخازن، وفحص جودة الأجزاء، والصيانة الوقائية بهدف تقليل التكاليف وتحسين كفاءة الإنتاج. تم صياغة النموذج كمشكلة برمجة غير خطية مختلطة الأعداد باستخدام خوارزمية جينية مع تحسينات هجينة ونهج محاكاة تكراري للتقييم. أظهر الإطار انخفاضاً في متوسط التكلفة بنسبة ١٥% مقارنة بالنماذج الحالية. تم التحقق من فعالية الإطار من خلال تجارب حسابية دقيقة، مما يعزز من موثوقيته وقابليته للتطبيق في تحسين أنظمة التصنيع.
530			_aIssues also as CD.
546			_aText in English and abstract in Arabic & English.
650		0	_aOperations research
650		0	_aبحوث العمليات
653		1	_aJoint Optimization _aPart quality inspection planning _aBuffer allocation problem _aPreventive maintenance _aGenetic algorithm _alocal search _aSerial manufacturing system line _aModeling _aالأمثلية المشتركة _aتخطيط فحص جودة الأجزاء
700	0		_aIhab Ahmed Fahmy El-khodary _ethesis advisor.
700	0		_aHisham Mohamed Abdelsalam _ethesis advisor.
900			_b01-01-2025 _cIhab Ahmed Fahmy El-khodary _cHisham Mohamed Abdelsalam _UCairo University _FFaculty of Computers and Artificial Intelligence _DDepartment of Operations Research and Decision Support
905			_aShimaa _eEman Ghareb
942			_2ddc _cTH _e21 _n0
999			_c177424