Data Analytics and Artificial Intelligence for Inventory and Supply Chain Management

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