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A Mathnal Publication
Transport
Optimization
in Practice
A Consultant's Project-Based Playbook
From Business Problem to Deployed Model, with Excel & Python
12 CASE STUDIES  •  50+ WORKED PROBLEMS  •  COMPLETE SOLUTIONS
Krish Naidu
Founder & Lead Consultant, Mathnal  |  Decisions from Evidence
🚚Routing
🏭Network
📊Analytics
🛣️Logistics
🌍Sustainability
🤖AI + OR
12
Industry Case Studies
50+
Worked Problems
23
Step Framework
17
Chapters
57
Documented Pitfalls
About The Book

Real consulting projects.
Real data. Real solutions.

Most optimization textbooks start with equations. This book starts with a business problem.

Every chapter drops you into an industry scenario with named stakeholders, quantified pain points, complete data tables, and competing objectives. You solve each problem end-to-end using the same 23-step consulting framework that professional analytics consultants use in the field — from the first client meeting to the deployed model and its measured KPI impact.

Every model is implemented twice: in Excel Solver (for intuition and accessibility) and in Python (PuLP, OR-Tools, Pyomo — for scalability and production deployment). Every solution includes sensitivity analysis, deployment guidance, and a pitfall register drawn from real consulting experience.

Table of Contents

17 Chapters. 5 Parts. One Complete Journey.

Part I — Foundations
Ch 1: The Business of Moving Things3 worked problems
Ch 2: The Optimization Toolkit2 worked problems
Part II — Routing & Dispatch Projects
Ch 3: The Milk Run Problem — Shortest Path5 problems + full case study
Ch 4: The Bakery Distribution — Capacitated VRP6 worked problems
Ch 5: The Cold Chain Pharma — VRP with Time Windows5 worked problems
Ch 6: The E-Commerce Last-Mile — Dynamic P&D4 worked problems
Part III — Network Design & Flow
Ch 7: The Cement Transportation Problem — LP6 worked problems
Ch 8: The Automotive Parts — Transshipment4 worked problems
Ch 9: The Warehouse Location — Facility MILP5 worked problems
Ch 10: The Port-to-Plant — Multi-Modal4 worked problems
Part IV — Advanced & Frontier Topics
Ch 11: The Monsoon Disruption — Stochastic4 worked problems
Ch 12: The Driver-Shift — Column Generation3 worked problems
Ch 13: The Green Fleet — Sustainability VRP4 worked problems
Ch 14: The Intelligent Dispatcher — ML + OR3 worked problems
Part V — From Model to Business Value
Ch 15: Capstone — Integrated Network Redesign1 major case study
Ch 16: Deployment & GovernanceAnalytics translator role
Ch 17: Pitfalls & Checklists57 pitfalls, Go/No-Go list
12 Industry Case Studies

Every chapter is a consulting engagement

Each case includes: complete data tables, mathematical formulation, Excel + Python code, sensitivity analysis, deployment plan, and pitfall register.

Chapter 3
The Milk Run Problem
Annapurna Dairy, Telangana — 28 village collection points
31% route reduction • ₹5.4L/yr saved
Chapter 4
Bakery Distribution
Nandini Bakeries, Bengaluru — 3 trucks, 45 stores
25% distance cut • ₹4.5L/yr saved
Chapter 5
Cold Chain Pharma
MediCold Logistics, Mumbai — 120 clinics, time windows
18% → 2% late deliveries • ₹82L/yr
Chapter 6
E-Commerce Last-Mile
QuickBasket, Hyderabad — 10-min delivery, 50 riders
SLA breach: 22% → 5% • ₹1.2Cr/yr
Chapter 7
Cement Transportation
Dakshin Cements — 5 plants, 18 markets
10.5% cost reduction • ₹4.8Cr/yr
Chapter 8
Automotive Parts Flow
AutoFlow, Chennai — 6 suppliers, 4 cross-docks
14% logistics cost cut • ₹3.8Cr/yr
Chapter 9
Warehouse Location
UrbanThread D2C — pan-India FC placement
2-day delivery: 96% • ₹4.2Cr/yr + revenue
Chapter 10
Multi-Modal Port-to-Plant
ChemIndia, Vizag — sea/rail/road optimization
₹6.2Cr saved • 27% emission cut
Chapter 11
Monsoon Disruption
TropiFresh FMCG — stochastic planning
62% disruption cost reduction • ₹1.8Cr
Chapter 12
Driver-Shift Scheduling
MetroBus Corp, Pune — 120 buses, 340 trips
12% fewer driver-hours • ₹1.5Cr/yr
Chapter 13
Green Fleet
EcoLogis 3PL — 30% CO₂ reduction target
34% emission cut at 4.2% cost premium
Chapter 14
Intelligent Dispatcher
SwiftHaul 3PL — ML-augmented optimization
Decision gap: 12% → 4.3% • ₹2.5Cr
KN
Krish Naidu
Founder & Lead Consultant

Krish Naidu is the Founder of Mathnal, a Supply Chain Analytics consulting and training firm built on the philosophy of "Decisions from Evidence."

With extensive experience spanning supply chain optimization, logistics analytics, demand forecasting, and operations research, Krish has helped organizations transform transport operations through mathematical rigor and data-driven decision-making.

He has designed training programs covering 50+ forecasting algorithms, implemented optimization solutions using Excel, Python, Power BI, and consulted on network design, route optimization, and inventory analytics across industries.

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