Data analytics and artificial intelligence for inventory and supply chain management / / edited by Dinesh K. Sharma, Madhu Jain

• Pubblicazione: Singapore : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (293 pages)
• Disciplina: 260
• Soggetto topico: Business logistics - Data processing; Logística industrial; Processament de dades
• Soggetto genere / forma: Llibres electrònics
• Persona (resp. second.): SharmaD. K (Dinesh K.); JainMadhu
• Nota di contenuto: Intro -- Foreword -- Preface -- Acknowledgements -- About This Book -- Contents -- Editors and Contributors -- 1 Markov Decision Processes of a Two-Tier Supply Chain Inventory System -- 1.1 Markov Decision Processes of a Supply Chain -- 1.2 M/M1 + M2/1 /K-Rule/f1-policy Queues + Inventory Model with Backorders -- 1.2.1 The Stable M/M1 + M/1 /K-rule/f1-policy Model -- 1.3 Performance Measures for the Inventory-Queueing System -- 1.4 Results for the SCM Attached to M/M/1 Queues with Zero Lead Time -- 1.5 SCM Attached M/M1 + M2/1/K-rule/f2-Policy with Dissatisfied Customers -- 1.5.1 Optimum Order Quantity "Q*" -- 1.6 Conclusion -- References -- 2 Nature-Inspired Optimization for Inventory Models with Imperfect Production -- 2.1 Introduction -- 2.2 An Imperfect Production Inventory Model -- 2.3 Inventory Production Systems with Process Reliability -- 2.4 Nature-Inspired Optimization at a Glance -- 2.5 Important Contributions on Nature-Inspired Optimization for Inventory Control -- 2.6 Economic Production Quantity (EPQ) Models and NIO Algorithms -- 2.7 Conclusions -- References -- 3 A Multi-objective Mathematical Model for Socially Responsible Supply Chain Inventory Planning -- 3.1 Introduction -- 3.2 Literature Survey -- 3.3 Assumptions and Notation -- 3.4 Multi-objective Model -- 3.4.1 Objective Functions -- 3.4.2 Constraints Sets -- 3.5 Solution Methodology and Result -- 3.6 Conclusions -- References -- 4 Artificial Intelligence Computing and Nature-Inspired Optimization Techniques for Effective Supply Chain Management -- 4.1 Introduction -- 4.2 Basic Concepts of AI -- 4.2.1 Categorization of AI -- 4.3 Nature-Inspired Optimization (NIO) -- 4.4 Supply Chain Management -- 4.4.1 Two Echelon Supply Chain Inventory Model -- 4.5 Role of AI and NIO Algorithm in SCM -- 4.5.1 Artificial Neural Network -- 4.5.2 Adaptive Neuro-Fuzzy Inference System (ANFIS). ; 4.5.3 Inventory Control and Planning -- 4.5.4 Transportation Network Design -- 4.5.5 Purchasing and Supply Management -- 4.5.6 e-Synchronized SCM -- 4.6 Adapted Model Related to Operational Decisions in Supply Chain (SC) Network -- 4.7 Literature Survey on AI, NIO, and Supply Chain Management (SCM) -- 4.8 Research Directions for the Future and Closing Remarks -- References -- 5 An EPQ Model for Imperfect Production System with Deteriorating Items, Price-Dependent Demand, Rework and Lead Time Under Markdown Policy -- 5.1 Introduction -- 5.2 Literature Survey -- 5.3 Assumptions and Notations -- 5.3.1 Notations -- 5.3.2 Assumptions -- 5.4 Mathematical Model -- 5.5 Numerical Illustration -- 5.6 Conclusions -- References -- 6 Retrial Inventory-Queueing Model with Inspection Processes and Imperfect Production -- 6.1 Introduction -- 6.2 Model Description -- 6.3 Joint Probability Distributions -- 6.3.1 Governing Equations -- 6.3.2 Derivation of Joint Probability Distribution Function -- 6.4 System Performance Indices -- 6.5 Cost Optimization -- 6.6 Numerical Illustration and Sensitivity Analysis -- 6.7 Conclusions -- References -- 7 Inventory Model for Growing Items and Its Waste Management -- 7.1 Introduction -- 7.1.1 Literature Survey -- 7.1.2 Motivation -- 7.2 Mathematical Model and Analysis -- 7.3 Profit Function -- 7.4 Profit from Waste Management -- 7.5 Conclusions -- References -- 8 Pavement Cracks Inventory Survey with Machine Deep Learning Models -- 8.1 Introduction -- 8.2 Literature Survey -- 8.3 Technical Background -- 8.3.1 Convolution -- 8.3.2 Activation -- 8.3.3 Max Pooling -- 8.3.4 Flatten Layer -- 8.3.5 Fully Connected Layers -- 8.3.6 Classifcation -- 8.4 Experimental Work -- 8.5 Observations on the Results -- 8.6 Conclusion -- References -- 9 Decarbonisation Through Production of Rhino Bricks From the Waste Plastics: EPQ Model. ; 9.1 Introduction -- 9.2 Motivation and Problem Description -- 9.3 Notations -- 9.4 Assumptions -- 9.5 Model Formulation -- 9.6 Solution Procedure -- 9.7 Numerical Illustration -- 9.8 Sensitivity Analysis -- 9.9 Managerial Implications -- 9.10 Conclusions -- References -- 10 Cost Analysis of Supply Chain Model for Deteriorating Inventory Items with Shortages in Fuzzy Environment -- 10.1 Introduction -- 10.2 Assumptions and Notations -- 10.3 Development and Analysis of the Model in Crisp Form -- 10.4 Developing Model and Computing Its Solution by Using FP -- 10.5 System of Non-Linear Equations and Its Solution -- 10.6 Numerical Computing and Sensitivity Analysis -- 10.7 Conclusions -- References -- 11 Multi-echelon Inventory Planning in Supply Chain -- 11.1 Introduction -- 11.2 Literature Survey -- 11.3 Model Description -- 11.4 Expected Lead Time -- 11.5 Optimal Policy -- 11.6 Some Special Cases -- 11.7 Cost Minimization Analysis -- 11.8 Numerical Results -- 11.9 Conclusions -- References -- 12 Impact of Renewable Energy on a Flexible Production System Under Preorder and Online Payment Discount Facility -- 12.1 Introduction -- 12.2 Review of Literature -- 12.3 Notations and Assumptions -- 12.3.1 Notations -- 12.3.2 Assumptions -- 12.4 Mathematical Modeling -- 12.5 Solution Methodology -- 12.6 Numerical Illustration -- 12.7 Concavity -- 12.8 Sensitivity Analysis -- 12.9 Observation -- 12.10 Conclusion -- References -- 13 Impact of Preservation Technology Investment and Order Cost Reduction on an Inventory Model Under Different Carbon Emission Policies -- 13.1 Introduction -- 13.2 Literature Review -- 13.3 Notations and Assumptions -- 13.3.1 Notations -- 13.3.2 Assumptions -- 13.4 Mathematical Modeling -- 13.4.1 Profit Function Under Different Carbon Tax Regulations -- 13.5 Numerical Illustration -- 13.6 Concavity -- 13.7 Sensitivity Analysis. ; 13.8 Observations -- 13.9 Conclusion -- References -- 14 The Impact of Corporate Credibility on Inventory Management Decisions -- 14.1 Introduction -- 14.2 Literature Review -- 14.3 Objective of Study -- 14.4 Research Methodology -- 14.5 Discussion -- 14.5.1 Genetic Algorithm (NSGA-II) -- 14.5.2 Neural Algorithm -- 14.6 Conclusion -- References -- 15 A Bidirectional Neural Network Dynamic Inventory Control Model for Reservoir Operation -- 15.1 Introduction -- 15.2 Basics of Dynamic Inventory Control and Dynamic Reservoir Operations -- 15.3 Structure of the Bidirectional Recurrent Neural Network-Based Dynamic Inventory Control Model -- 15.4 Design of Neuro-Fuzzy Irrigation Reservoir Operation Using Bidirectional Recurrent Neural Network (BRNN) -- 15.4.1 Input Layer: Water Demand and Supply Analysis -- 15.4.2 Output Layer -- 15.4.3 Fuzzy Interface -- 15.4.4 Hidden Layer -- 15.5 Training and Validation of the Irrigation Model Using Data -- 15.5.1 Training -- 15.5.2 Evaluation of Model Performance -- 15.6 Conclusion -- References.
• Titolo autorizzato: Data analytics and artificial intelligence for inventory and supply chain management
• ISBN: 981-19-6337-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996499871103316
• Lo trovi qui: Univ. di Salerno
• Serie: Inventory Optimization