Resource Allocation Problems in Supply Chains.
Ganesh, K.
Resource Allocation Problems in Supply Chains. - 1st ed. - 1 online resource (197 pages)
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.
Resource Allocation is the utilization of available resources in the system. This book focuses on development of models for 6 new, complex classes of RA problems in Supply Chain networks, focusing on bi-objectives, dynamic input data, and multiple performance measure based allocation and integrated allocation, and routing with complex constraints.
9781785603983
Materials management -- Data processing.
Electronic books.
HD56-57.5
658.7
Resource Allocation Problems in Supply Chains. - 1st ed. - 1 online resource (197 pages)
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.
Resource Allocation is the utilization of available resources in the system. This book focuses on development of models for 6 new, complex classes of RA problems in Supply Chain networks, focusing on bi-objectives, dynamic input data, and multiple performance measure based allocation and integrated allocation, and routing with complex constraints.
9781785603983
Materials management -- Data processing.
Electronic books.
HD56-57.5
658.7