
Resource Allocation Problems in Supply Chains
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Content
- Front Cover
- Resource Allocation Problems in Supply Chains
- Copyright page
- Abstract
- Contents
- List of Tables
- List of Figures
- List of Symbols and Abbreviations
- About the Authors
- Section 1 Introduction
- 1.1. Supply Chain Management
- 1.2. Resource Allocation Problems in Supply Chain
- 1.3. Motivation of Resource Allocation Problems
- 1.3.1. Resource Allocation Variant in Bi-Objective Capacitated Supply Chain Network
- 1.3.2. Resource Allocation Variant in Bi-Objective Bound Driven Capacitated Supply Chain Network
- 1.3.3. Resource Allocation Variant in Multiple Measures Driven Capacitated Multi-Echelon Supply Chain Network
- 1.3.4. Resource Allocation Variant in Integrated Decision and Upper Bound Driven Capacitated Multi-Echelon Supply Chain Network
- 1.3.5. Resource Allocation Variant in Integrated Decision and Time Driven Capacitated Multi-Echelon Supply Chain Network
- 1.3.6. Resource Allocation Variant in Integrated Decision, Bound and Time Driven Capacitated Multi-Echelon Supply Chain Network
- 1.4. Scope of the Present Study
- Section 2 Literature Review
- 2.1. Resource Allocation Problem
- 2.2. Review of the RA Variants Addressed in Current Research
- 2.2.1. Bi-Objective Generalized Assignment Problem
- 2.2.2. Multi-Commodity Network Flow Problem
- 2.2.3. Multiple Measures Resource Allocation Problem
- 2.2.4. Mixed Capacitated Arc Routing Problem
- 2.2.5. Employee Routing Problem
- 2.2.6. Vehicle Routing Problem with Backhauls with Time Windows
- 2.3. Observations and Research Gap
- 2.4. Summary
- Section 3 Bi-Objective Capacitated Supply Chain Network
- 3.1. Bi-Objective Resource Allocation Problem with Varying Capacity
- 3.2. Solution Methodology to Solve BORAPVC
- 3.2.1. Mathematical Programming Model for BORAPVC
- 3.2.2. Simulated Annealing with Population Size Initialization through Neighborhood Generation for GAP and BORAPVC
- 3.2.2.1. Parameter settings for SAPING
- 3.3. Computational Experiments and Results
- 3.4. Conclusion
- Section 4 Bi-Objective Bound Driven Capacitated Supply Chain Network
- 4.1. Bi-Objective Resource Allocation Problem with Bound and Varying Capacity
- 4.2. Solution Methodology to Solve IRARPUB
- 4.2.1. Recursive Function Inherent Genetic Algorithm (REFING) for MCNF and BORAPBVC
- 4.3. Computational Experiments and Results
- 4.3.1. Performance of Solution Methodology
- 4.4. Case Study Demonstration
- 4.4.1. Problem Identification and Discussion
- 4.4.1.1. Patient Distribution System (PDS)
- 4.4.1.2. Input to the Central Body
- 4.4.1.3. Flow chart for the allocation of patients
- 4.4.1.4. Problem identification
- 4.4.1.5. Assumptions
- 4.4.2. Formulation of the Problem
- 4.4.3. Model Testing
- 4.4.4. Analysis of Results and Discussion
- 4.4.5. Managerial Implications
- 4.4.6. Summary for Case Study
- 4.5. Conclusion
- Section 5 Multiple Measures Driven Capacitated Multi-Echelon Supply Chain Network
- 5.1. Multiple Measures Resource Allocation Problem for Multi-Echelon Supply
- 5.2. Solution Methodology for MMRAPMSC
- 5.2.1. Simulation Modeling with Multiple Performances Measures (SIMMUM) for MMRAPMSC
- 5.2.2. Model Descriptions
- 5.2.3. SIMMUM Model Assumptions
- 5.2.4. Decision Variables in SIMMUM
- 5.2.5. Multiple Performance Measures of Multi-Echelon Supply Chain
- 5.2.6. SIMMUM Model Initialization
- 5.2.7. SIMMUM Model Execution
- 5.2.7.1. Consumer placing an order
- 5.2.7.2. Sourcing
- 5.2.8. Output of SIMMUM Model
- 5.2.9. SIMMUM Model Implementation
- 5.3. Simulation Model Experimentations and Results
- 5.4. Case Study for Inventory and Purchasing Policy
- 5.4.1. Procurement Policy for all "A" Class Items
- 5.4.2. Inventory Policy for all "A" Class Items
- 5.4.3. Procurement and Inventory Policy for all "b" "c" Class Items
- 5.5. Conclusion
- Section 6 Integrated Decision and Upper Bound Driven Capacitated Multi-Echelon Supply Chain Network
- 6.1. Integrated Resource Allocation and Routing Problem with Upper Bound
- 6.1.1. Constraints
- 6.1.2. Assumptions of IRARPUB Problem
- 6.2. Solution Methodology to Solve IRARPUB
- 6.2.1. Dijkstra's Algorithm and Local Search Inherent Genetic Algorithm (DIALING) for MCARP and IRARPUB
- 6.2.2. Parameter Settings for DIALING
- 6.3. Computational Experiments and Results
- 6.3.1. Performance of Solution Methodology
- 6.4. Case Study for IRARPUB
- 6.5. Conclusion
- Section 7 Integrated Decision and Time Driven Capacitated Multi-Echelon Supply Chain Network
- 7.1. Integrated Resource Allocation and Routing Problem with Time Window
- 7.2. Solution Methodology to Solve IRARPTW
- 7.2.1. Clustering Inherent Genetic Algorithm (CLING) for VRPTW and IRARPTW
- 7.2.2. Parameter Settings for CLING
- 7.3. Computational Experiments and Results
- 7.3.1. Performance of Solution Methodology
- 7.4. Conclusion
- Section 8 Integrated Decision, Bound and Time Driven Capacitated Multi Echelon Supply Chain Network
- 8.1. Integrated Resource Allocation and Routing Problem with Bound and Time Window
- 8.2. Solution Methodology to Solve IRARPBTW
- 8.2.1. Decision Support System Based on Mixed Integer Linear Programming (DINLIP) for VRPBTW and IRARPBTW
- 8.3. Computational Experiments and Results
- 8.3.1. Performance of Heuristics
- 8.3.1.1. VRPBTW datasets
- 8.3.1.2. IRARPBTW datasets
- 8.4. Case Study Demonstration for IRARPBTW
- 8.4.1. IRARPBTW for Case Study
- 8.4.2. Survey and Data Collection Methodology
- 8.4.3. Results and Discussions for Case Study
- 8.5. Decision Support System for Vehicle Routing at Sangam: Design of Decision Support System
- 8.5.1. Deployment of Decision Support System
- 8.6. Conclusion
- Section 9 Conclusions
- 9.1. Summary
- 9.2. Scope for Further Work
- Bibliography
- Index
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