This book considers new analytics and AI approaches in the areas of inventory control, logistics, and supply chain management. It provides valuable insights for the retailers and managers to improve business operations and make more realistic and better decisions. It also offers a number of smartly designed strategies related to inventory control and supply chain management for the optimal ordering and delivery policies. The book further uses detailed models and AI computing approaches for demand forecasting to planning optimization and digital execution tracking. One of its key features is use of real-life examples, case studies, practical models to ensure adoption of new solutions, data analytics, and AI-lead automation methodologies are included.The book can be utilized by retailers and managers to improve business operations and make more accurate and realistic decisions. The AI-based solution, agnostic assessment, and strategy will support the companies for better alignment and inventory control and capabilities to create a strategic road map for supply chain and logistics. The book is also useful for postgraduate students, researchers, and corporate executives. It addresses novel solutions for inventory to real-world supply chain and logistics that retailers, practitioners, educators, and scholars will find useful. It provides the theoretical and applicable subject matters for the senior undergraduate and graduate students, researchers, practitioners, and professionals in the area of artificial intelligent computing and its applications in inventory and supply chain management, inventory control, and logistics.
Author(s): Dinesh K. Sharma, Madhu Jain
Series: Inventory Optimization
Publisher: Springer
Year: 2022
Language: English
Pages: 292
City: Singapore
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
