| Titre : | Performance Evaluation of Server |
| Auteurs : | BOUCHIKHI Mustapha, Auteur ; MORADJI FATMA, Auteur ; Mekour mansour, Directeur de thèse |
| Type de document : | texte manuscrit |
| Editeur : | Université de Saida - Dr Moulay Tahar. Faculté des Sciences. Département de Mathématiques., 2024/2025 |
| Format : | 53ص |
| Accompagnement : | CD |
| Langues: | Anglais |
| Index. décimale : | BUC-M 003720 |
| Catégories : |
Mémoire de Master en informatique Spécialité : Réseaux informatique et Système Répartis (RISR) |
| Mots-clés: | This master’s thesis focuses on the performance evaluation of server scheduling system. The problem is modeled within a Job Shop environment, characterized by sequential operations that must be executed on specific machines. To address this complex problem, an approach based on Ant Colony Optimization (ACO) is proposed. This metaheuristic, recognized for its effectiveness in complex scheduling scenarios, enables intelligent exploration of the solution space. An interactive software tool, developed using Python and PyQt5, is designed to simulate and optimize the entire order management cycle. The results, analyzed through key performance indicators such as makespan and Gantt charts, demonstrate that this method significantly enhances system efficiency, responsiveness, and overall scheduling performance |
| Résumé : |
تتناول هذه المذكرة تقييم أداء نظام جدولة الخوادم.
تم نمذجة المشكلة في بيئة من نوع "ورشة الإنجاز (Job Shop) ، وهي بيئة تتميز بوجود عمليات متسلسلة يجب تنفيذها على آلات محددة. ولحل هذه المشكلة المعقدة، تم اقتراح منهجية تعتمد على خوارزمية تحسين مستعمرة النمل (Ant Colony Optimization - ACO) هذه الآلية التقديرية التي يعترف بكفاءتها في سيناريوهات الجدولة المعقدة، تتيح استكشافاً ذكياً لفضاء الحلول. كما تم تطوير أداة برمجية تفاعلية باستخدام لغة Python وواجهة PyQt5 ، بهدف محاكاة وتحسين دورة إدارة الطلبات بالكامل. خلال تحليل النتائج باستخدام مؤشرات الأداء الرئيسية مثل مدة إنجاز كل المهام" (makespan) ومخططات غانت (Gantt charts)، تبين أن هذه الطريقة تحدث تحسناً ملحوظاً في كفاءة النظام واستجابته والأداء العام لعملية الجدولة. |
| Note de contenu : |
General Introduction 9
Problems and objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Objectives of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Methodological approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Structure of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Glossary and Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1 The art of order management for operational excellence 12 1.1 Mastering order preparation, a strategic challenge . . . . . . . . . . 12 1.2 Order management: When the customer promise takes shape . . . 13 1.3 Ordering: The starting point of the customer journey . . . . . . . . 13 1.4 Order execution: The operational heart . . . . . . . . . . . . . . . . 13 1.5 Inventory Management: The Pulse of Availability . . . . . . . . . . 14 1.6 The Order Management System (OMS): The digital control tower . 14 1.7 The critical importance of customer order management . . . . . . . 15 1.7.1 The direct impact on customer experience . . . . . . . . . . 15 1.8 The essential features of effective order management . . . . . . . . 15 1.9 Order Processing: From Click to Receipt . . . . . . . . . . . . . . . 16 1.10 Key steps in order processing . . . . . . . . . . . . . . . . . . . . . 16 1.11 Order preparation processes: The art of picking, sorting and packing 17 1.11.1 Sampling methods: Adapting to flows . . . . . . . . . . . . 17 1.11.2 Sorting modes: Organize the flow of articles . . . . . . . . 18 1.11.3 Order preparation methods: Diversity of approaches . . . . 18 1.12 The “Pick to belt” preparation mode: Mass picking towards the conveyor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.13 Order preparation areas: Structuring warehouse efficiency . . . . . 20 1.13.1 The Consolidation Zone: The Gathering Point . . . . . . . 20 1.13.2 Packaging areas: The final touch before shipping . . . . . . 21 1.13.3 Departure control zones: Quality above all . . . . . . . . . 21 1.14 Departure waiting areas: Anticipating hazards . . . . . . . . . . . . 21 4 1.15 Factors for optimizing order preparation: Towards logistics excel- lence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.16 Weight control: A double-sided verification . . . . . . . . . . . . . . 22 1.17 The best solution: Towards integrated and targeted control . . . . . 23 1.18 Reducing journey length: Optimizing the preparer’s route . . . . . 24 1.19 Limiting load breaks: Fluidity of movement . . . . . . . . . . . . . 24 1.20 Limitation of damage risks: Preserving the integrity of products . . 24 1.21 Optimizing article accessibility: Making the preparer’s work easier 25 1.22 Using suitable storage furniture: The right tool in the right place . 25 1.23 Workstation ergonomics: Well-being at the service of performance 25 1.24 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2 Theoretical framework of the workshop scheduling problem 27 2.1 Definition of the Job Shop Scheduling Problem (JSSP) . . . . . . . . 27 2.1.1 Overview of the scheduling problem . . . . . . . . . . . . . 27 2.1.2 Difference between planning and scheduling . . . . . . . . . 27 2.1.3 Definition of the Job Shop Scheduling Problem (JSSP) . . . 28 2.1.4 The Job Shop Problem (JSSP) . . . . . . . . . . . . . . . . . 28 2.2 Mathematical modeling of the JSSP . . . . . . . . . . . . . . . . . . 30 2.3 Main formulas of the ACO applied to the JSSP . . . . . . . . . . . . 30 2.4 Summary of ratings . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.5 Problem complexity and computational difficulty . . . . . . . . . . 31 2.6 Examples of applications of scheduling . . . . . . . . . . . . . . . . 32 2.7 Scheduling constraints in industry . . . . . . . . . . . . . . . . . . . 32 2.8 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3 Evolution of scheduling algorithms 33 3.1 Limits of classical solutions . . . . . . . . . . . . . . . . . . . . . . . 33 3.2 Metaheuristics and their application to the JSSP . . . . . . . . . . . 33 3.3 The Ant Colony Algorithm (ACO) . . . . . . . . . . . . . . . . . . . 34 3.3.1 Ant System . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.3.2 Biological principles . . . . . . . . . . . . . . . . . . . . . . 35 3.3.3 ACO principle applied to the JSSP . . . . . . . . . . . . . . 35 3.3.4 Mathematical modeling . . . . . . . . . . . . . . . . . . . . 37 3.3.5 Search and convergence mechanisms . . . . . . . . . . . . . 37 3.4 Applications of ACO to workshop scheduling . . . . . . . . . . . . 38 3.5 Graphical representations: disjunctive graph and Gantt chart . . . . 39 3.6 What does each element of the ant colony represent in the context of the workshop scheduling (JSSP) for the simulation? . . . . . . . . 41 3.6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 5 4 Experimental simulation and analysis of results 42 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.2 Experimental protocol . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.3 Organized the use of the ACO interface for workshop scheduling . 42 4.4 Presentation of the application . . . . . . . . . . . . . . . . . . . . . 43 4.4.1 Simulation of the ACO algorithm on this case . . . . . . . . 45 4.5 Analysis and Discussion of Results . . . . . . . . . . . . . . . . . . . 46 4.6 Conclusion of the case study . . . . . . . . . . . . . . . . . . . . . . 46 4.7 Difficulties encountered . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.8 Recommendations and perspectives . . . . . . . . . . . . . . . . . . 47 4.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 General Conclusion 49 References 50 |
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