Applications of Advanced Optimization Techniques in Industrial Engineering

✍ Scribed by Abhinav Goel; Anand Chauhan; A K Malik

• Publisher: CRC Press
• Year: 2022
• Tongue: English
• Leaves: 243
• Category: Library

✦ Synopsis

This book provides different approaches used to analyze, draw attention, and provide an understanding of the advancements in the optimization field across the globe. It brings all of the latest methodologies, tools, and techniques related to optimization and industrial engineering into a single volume to build insights towards the latest advancements in various domains.

Applications of Advanced Optimization Techniques in Industrial Engineering includes the basic concept of optimization, techniques, and applications related to industrial engineering. Concepts are introduced in a sequential way along with explanations, illustrations, and solved examples. The book goes on to explore applications of operations research and covers empirical properties of a variety of engineering disciplines. It presents network scheduling, production planning, industrial and manufacturing system issues, and their implications in the real world.

The book caters to academicians, researchers, professionals in inventory analytics, business analytics, investment managers, finance firms, storage-related managers, and engineers working in engineering industries and data management fields.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Editors
Contributors
Chapter 1: Dynamical Analysis in Modulated Logistic Maps
1.1 Introduction
1.2 Preliminaries
1.3 Modulated Logistic Maps
1.4 Dynamical Analysis in μx p (1 − x) q for p = 2 and q = 1
1.4.1 Period-Doubling Bifurcation Analysis for the Map M 2,1 ( μ, x)
1.5 Dynamical Analysis in μx p (1 − x) q for p = 1 and q = 2
1.5.1 Period-Doubling Bifurcation Analysis for the Map M 1, 2 ( μ, x)
1.6 Dynamical Analysis in μx p (1 − x) q for p = 2 and q = 2
1.6.1 Period-Doubling Bifurcation Analysis for the Map M 2,2 ( μ, x)
1.7 Conclusion
References
Chapter 2: A Survey on Evolutionary Clustering Algorithms and Applications
2.1 Introduction
2.1.1 What Are the Evolutionary Algorithms?
2.1.2 Evolutionary Algorithms for Solving Optimization Problems
2.1.3 Clustering Approaches and Their Categorical Division
2.2 Literature Survey and Related Work
2.3 Applications of Evolutionary Clustering Algorithms
2.3.1 Image Segmentation Using Evolutionary Clustering Algorithms
2.3.2 Medical and Healthcare Data Clustering Using Evolutionary Clustering Algorithms
2.3.3 Text Document Clustering Using Evolutionary Clustering Algorithms
2.4 Performance Evaluation of Evolutionary Clustering Algorithms
2.5 Conclusion
References
Chapter 3: Solving Linear Fractional Programming Problem Using Revised and Column Simplex Method
3.1 Introduction
3.2 Literature Review
3.3 Methodology
3.3.1 Definition and Mathematical Formulation of LP and LFP
3.3.2 Revised Simplex Method for Solving LFP Problem
3.3.3 Column Simplex Method for Solving LFP Problem
3.4 Numerical Illustration
3.4.1 Numerical Examples Based on Revised Simplex Method
3.4.2 Numerical Examples of Column Simplex Method
3.5 Comparison and Discussion
3.6 Conclusion
References
Chapter 4: The Tradeoff in Managing Overall Cost and Backorder Minimization in Two-Stage Multi Commodity Supply Chain Problem
4.1 Introduction
4.2 Literary Background
4.2.1 Indices
4.2.2 Parameters
4.2.3 Decision Variables
4.3 Problem Description and Formulation
4.3.1 Mathematical Formulation
4.4 Computational Results and Managerial Insights
4.5 Conclusion
References
Chapter 5: An Optimization Study on Behavior of Actinide Monochalcogenides at High Pressure
5.1 Introduction
5.2 Brief About Optimization in Phase Transition Study
5.2.1 Reconstructive Type
5.2.2 Martensitic Type
5.2.3 Distortive Type
5.2.4 Structural Phase Transitions
5.2.5 Pressure-Induced Phase Transitions
5.3 Method of Calculations
5.4 Results and Discussion
5.5 Conclusion
References
Chapter 6: The Dynamics of a Continuous Innovation Diffusion Model with Advertisements as Well as Interpersonal Communications
6.1 Introduction
6.2 Mathematical Model
6.3 Basic Preliminaries
6.4 Existence of Equilibria and Basic Influence Number
6.5 Stability Analysis of Various Equilibria
6.5.1 Stability of Adopter Free Equilibrium Point E 0
6.5.2 Stability of Interior Equilibrium Point E ∗
6.6 Hopf-Bifurcation Analysis
6.7 Sensitivity Analysis
6.8 Numerical Simulations
6.9 Results and Discussion
6.10 Conclusion
References
Chapter 7: Stochastic Analysis of a Priority-Based Warm Standby System Working under k-out-of-n: G Policy Using Multi-Dimensional Repair
7.1 Introduction
7.2 Model Description and Notations
7.2.1 System Description
7.2.2 Expectations Aimed for the Model
7.2.3 Notations
7.3 System Configuration and State Transition Diagram
7.4 State Explanation
7.5 Mathematical Formularizations for the Model
7.6 Analytical Study
7.6.1 Availability Analysis
7.6.2 Reliability of the System
7.6.3 Mean Time to Failure (MTTF)
7.6.4 Cost Analysis
7.7 Conclusion
References
Chapter 8: An Inventory Policy for Increasing Holding Cost under the Effect of Stock-Dependent Deterioration and Partial Backlogging
8.1 Introduction
8.2 Assumptions and Notations
8.3 Model Formulation
8.4 Model Without Shortages
8.5 Model with Partial Shortages
8.6 Optimal Criteria
8.7 Numerical Illustration
8.7.1 Illustration for Model Without Shortages
8.8 Sensitivity Analysis
8.9 Observations
8.10 Conclusion
References
Chapter 9: A Fuzzy Inventory Model for Non-Instantaneous Oxidizing Items with a Nonlinear-Hexagonal Fuzzy Number under the Effect of Learning
9.1 Introduction
9.2 Literature Review
9.3 Preliminary Concepts
9.4 Assumption and Notation
9.4.1 Assumptions for the Model
9.5 Mathematical Model in Fuzzy Environment
9.6 Algorithm for Optimality Criteria
9.7 Numerical Illustration
9.8 Sensitivity Analysis
9.9 Conclusion
References
Chapter 10: Optimal Analysis of Machine Interference Problem with Standby, Random Switching Failure, Vacation Interruption and Synchronized Reneging
10.1 Introduction
10.2 Literature Review
10.3 Model Description and State Probabilities
10.4 Chapman–Kolmogorov Equation
10.5 The Steady-State Solution
10.6 System Performance Measures
10.6.1 Expected Total Cost
10.6.2 The Quasi-Newton Method
10.7 Numerical Results
10.8 Conclusion
References
Chapter 11: Optimal Cluster Head Election in Industrial WSNs Using the Multi-Objective Genetic Algorithm
11.1 Introduction
11.2 Multi-Objective Genetic Algorithm (MOGA)
11.3 MOGA-based CH Selection
11.3.1 Set-up Phase
11.3.2 Steady-State Phase
11.4 Experiment Results and Discussion
11.5 Conclusion
References
Chapter 12: Monitoring Social Distancing for Industries and in Public Areas Using Machine Learning
12.1 Introduction: Background and Driving Forces
12.2 Literature Review
12.3 Proposed Methodology
12.3.1 Object Detection Using YOLO
12.4 Object Tracking Using OpenCV
12.4.1 Distance Measurement
12.5 Outcomes
12.6 Conclusion
References
Chapter 13: Profit-Maximization Inventory Model with Stock-Dependent Demand
13.1 Introduction
13.2 Notations and Assumptions
13.3 Mathematical Formulation
13.4 Numerical Examples
13.5 Sensitivity Analysis
13.6 Conclusion
References
Chapter 14: Models of Supply Chain Sustainability in Industrial Engineering
14.1 Introduction
14.2 Literature Review
14.3 Research Methodology
14.4 Hypotheses Tested
14.5 Research Questions and Objectives
14.5.1 Analysis of Results
14.6 Conclusion
References
Chapter 15: Optimal Stabilization in Chaotic Maps Using Ishikawa Feedback Technique
15.1 Introduction
15.2 Preliminaries
15.3 Chaotic Map in the Ishikawa Feedback Technique
15.4 Optimal Stabilization in Chaotic Maps
15.5 Conclusion
References
Index