Resource Allocation Problems in Supply Chains
Published on 10. August 2015
200 pages
978-1-78560-398-3 (ISBN)
Description
Resource Allocation (RA) involves the distribution and utilization of available resources in the system. Because resource availability is usually scarce and expensive, it becomes important to find optimal solutions to such problems. Thus RA problems represent an important class of problems faced by mathematical programmers. This book focuses on development of models and heuristics for six new and complex sub-classes of RA problems in Supply Chain (SC) networks, focusing on bi-objectives, dynamic input data, and multiple performance measures based allocation and integrated allocation, and routing with complex constraints. It considers six set of variants of the RA problems normally encountered in practice but have not yet been studied. These variants of the classical RA are complex and pertaining to both manufacturing and service industry.
More details
Language
English
Place of publication
Bingley
United Kingdom
Target group
Professional and scholarly
File size
4,01 MB
ISBN-13
978-1-78560-398-3 (9781785603983)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
K. Ganesh | Sanjay Mohapatra | R. A. Malairajan
Resource Allocation Problems in Supply Chains
Book
08/2015
Emerald Group Publishing Limited
€129.90
Shipment within 10-20 days
Persons
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
