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Supply Chain Optimization Version 2
Supply Chain Optimization
New Release

Supply Chain Optimization Version 2

by Kirsh Naidu

English Version 2
₹1,750+ 18% GST
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About This Book

Chapter 1 — Python Foundations & Advanced Data Structures 1.1 Why Python for Supply Chain Optimization 1.2 Advanced NumPy: Tensor Operations & Sparse Matrices 1.3 Pandas at Scale: Dask and Chunked Processing 1.4 CVXPY: Convex Optimization Framework 1.5 Building High-Performance Cost Models 1.6 KPI Engineering: OTIF, COGS, Working Capital Chapter 2 — Mathematical Optimization — Advanced Theory 2.1 Convex vs. Non-Convex Problem Landscapes 2.2 Duality Theory and Economic Interpretation 2.3 Two-Stage Stochastic Programming 2.4 Robust Optimization Under Uncertainty 2.5 Multi-Objective Optimization & Pareto Frontiers 2.6 Decomposition Methods: Benders & Column Generation Chapter 3 — Advanced Procurement Optimization 3.1 Multi-Tier Supplier Risk Modelling 3.2 Strategic Sourcing with Category Management 3.3 Auction Theory & Reverse Auction Models 3.4 Contract Optimization: Fixed vs. Flexible Contracts 3.5 Sustainable Procurement: Carbon-Constrained Sourcing 3.6 Research Case Study: Automotive Tier-1 Sourcing Chapter 4 — Stochastic Inventory Optimization 4.1 Newsvendor Model and Critical Ratio 4.2 (s,S) and (r,Q) Policy Optimization 4.3 Multi-Echelon Inventory: Clark-Scarf Model 4.4 Perishable Inventory with Expiry Constraints 4.5 Inventory Pooling and Risk Aggregation 4.6 Research Case Study: Pharmaceutical Cold Chain Chapter 5 — Advanced Network Flow & Graph Algorithms 5.1 Minimum Cost Flow: Theory and Implementation 5.2 Maximum Flow and Bottleneck Analysis 5.3 Dynamic Network Flow Over Time 5.4 Multi-Commodity Network Design 5.5 Resilient Network Design Under Disruption Chapter 6 — Vehicle Routing: Advanced Variants 6.1 VRPTW with Rolling Horizons 6.2 Electric Vehicle Routing with Charging Constraints 6.3 Stochastic VRP with Uncertain Demand 6.4 Two-Echelon City Logistics Routing TABLE OF CONTENTS Chapter 1 — Python Foundations & Advanced Data Structures 1.1 Why Python for Supply Chain Optimization 1.2 Advanced NumPy: Tensor Operations & Sparse Matrices 1.3 Pandas at Scale: Dask and Chunked Processing 1.4 CVXPY: Convex Optimization Framework 1.5 Building High-Performance Cost Models 1.6 KPI Engineering: OTIF, COGS, Working Capital Chapter 2 — Mathematical Optimization — Advanced Theory 2.1 Convex vs. Non-Convex Problem Landscapes 2.2 Duality Theory and Economic Interpretation 2.3 Two-Stage Stochastic Programming 2.4 Robust Optimization Under Uncertainty 2.5 Multi-Objective Optimization & Pareto Frontiers 2.6 Decomposition Methods: Benders & Column Generation Chapter 3 — Advanced Procurement Optimization 3.1 Multi-Tier Supplier Risk Modelling 3.2 Strategic Sourcing with Category Management 3.3 Auction Theory & Reverse Auction Models 3.4 Contract Optimization: Fixed vs. Flexible Contracts 3.5 Sustainable Procurement: Carbon-Constrained Sourcing 3.6 Research Case Study: Automotive Tier-1 Sourcing Chapter 4 — Stochastic Inventory Optimization 4.1 Newsvendor Model and Critical Ratio 4.2 (s,S) and (r,Q) Policy Optimization 4.3 Multi-Echelon Inventory: Clark-Scarf Model 4.4 Perishable Inventory with Expiry Constraints 4.5 Inventory Pooling and Risk Aggregation 4.6 Research Case Study: Pharmaceutical Cold Chain Chapter 5 — Advanced Network Flow & Graph Algorithms 5.1 Minimum Cost Flow: Theory and Implementation 5.2 Maximum Flow and Bottleneck Analysis 5.3 Dynamic Network Flow Over Time 5.4 Multi-Commodity Network Design 5.5 Resilient Network Design Under Disruption Chapter 6 — Vehicle Routing: Advanced Variants 6.1 VRPTW with Rolling Horizons 6.2 Electric Vehicle Routing with Charging Constraints 6.3 Stochastic VRP with Uncertain Demand 6.4 Two-Echelon City Logistics Routing

What You'll Learn

Upon successful completion of this book, you will be able to:
Develop high-performance supply chain optimization applications in Python using advanced libraries including NumPy, Dask, Pandas, SciPy, and CVXPY for large-scale industrial optimization problems.
Design enterprise-scale analytical models by engineering supply chain KPIs such as OTIF, COGS, inventory turns, service levels, and working capital for strategic decision-making.
Formulate complex optimization problems using convex optimization, nonlinear programming, duality theory, and economic interpretation of optimization models.
Develop robust optimization models that explicitly account for uncertainty using stochastic programming, robust optimization, and scenario-based planning techniques.
Optimize multiple conflicting business objectives by constructing Pareto-efficient solutions using multi-objective optimization methodologies.
Solve computationally intensive optimization problems using decomposition techniques including Benders Decomposition and Column Generation for large-scale supply chain networks.
Design strategic procurement optimization models that integrate supplier risk, category management, auction theory, contract optimization, and sustainable sourcing into procurement decision frameworks.
Develop multi-tier supplier risk assessment models that incorporate resilience, sustainability, and carbon-aware procurement strategies.
Build stochastic inventory optimization models using Newsvendor Theory, (s,S) policies, (r,Q) policies, Multi-Echelon Inventory Optimization, and risk pooling methodologies.
Optimize inventory systems for complex environments including perishable products, pharmaceutical cold chains, and multi-echelon distribution networks under uncertainty.
Model and solve advanced network optimization problems including Minimum Cost Flow, Maximum Flow, Dynamic Network Flow, Multi-Commodity Networks, and disruption-resilient logistics networks.
Design resilient supply chain networks capable of maintaining operational performance under disruptions through advanced graph optimization techniques.
Develop advanced Vehicle Routing Problem (VRP) solutions including Vehicle Routing with Time Windows (VRPTW), Electric Vehicle Routing, stochastic routing, rolling horizon optimization, and two-echelon city logistics.
Apply graph theory and network optimization algorithms to improve transportation efficiency, reduce logistics costs, and enhance supply chain resilience.
Integrate sustainability into optimization models by incorporating carbon constraints, environmental objectives, and green logistics into strategic planning.
Evaluate optimization models using sensitivity analysis, scenario planning, robustness testing, and computational performance analysis for enterprise-scale decision support.
Develop research-grade optimization solutions that combine mathematical programming, stochastic optimization, graph theory, and advanced Python programming for real-world industrial applications.
Bridge Operations Research, Artificial Intelligence, and Supply Chain Analytics by integrating optimization algorithms with modern computational frameworks for intelligent decision-making.
Build scalable optimization architectures capable of solving large, high-dimensional supply chain problems encountered in manufacturing, retail, logistics, healthcare, energy, and global distribution networks.
Apply advanced optimization methodologies to design resilient, sustainable, and cost-effective supply chain systems that support strategic, tactical, and operational business decisions.
Upon Completion, You Will Be Able To
Build enterprise-scale optimization models using advanced Python libraries and modern computational optimization frameworks.
Apply stochastic programming, robust optimization, multi-objective optimization, and decomposition methods to solve complex supply chain decision problems.
Optimize procurement, strategic sourcing, supplier risk management, inventory planning, transportation, and network design under uncertainty.
Design advanced routing algorithms for electric vehicles, city logistics, stochastic demand, and large-scale distribution systems.
Develop resilient and sustainable supply chain solutions by incorporating carbon constraints, disruption risk, and environmental objectives into optimization models.
Solve computationally challenging Operations Research problems using graph algorithms, convex optimization, and large-scale decomposition techniques.
Integrate advanced mathematical optimization with business analytics to deliver research-grade, production-ready decision support systems.
Lead the design and implementation of AI-enabled, optimization-driven supply chain solutions that improve resilience, profitability, sustainability, and operational excellence across global supply chain networks.

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