Transportation Network Optimization

Reducing Steel Logistics Costs in India: Strategic Framework Logistics accounts for 10–20% of steel’s delivered cost and up to 28% of factory cost. Reducing this burden is key to improving competitiveness. A multi-pronged strategy involving infrastructure, modal shifts, digital tools, and policy reforms can yield significant savings. 1. Shift to Rail, Water, and Pipelines Road transport, though flexible, is 2–3x costlier. Rail movement via rakes and sidings can cut costs by 20–30%. Inland waterways (e.g., Ganga, Brahmaputra) save 40–60% for long-haul bulk cargo. Slurry pipelines, at Rs. 80–100/tonne for 250 km, are vastly cheaper than rail or road and must be expanded for inland plants. 2. Leverage PFTs and DFCs Private Freight Terminals reduce first/last-mile costs. Eastern and Western DFCs offer faster, reliable movement. Time-tabled rakes and rake-sharing improve predictability and lower costs. 3. Improve First & Last-Mile Efficiency Rail sidings, Ro-Ro services, and containerization reduce handling loss and costs. Better road access to ports via PPPs boosts multimodal efficiency. 4. Upgrade Infrastructure Developing dedicated rail/road corridors and multimodal logistics parks under Bharatmala and Sagarmala enhances connectivity. Coastal hubs at Vizag, Kandla, Paradip allow direct port loading, avoiding double handling. 5. Adopt Technology Use of Transport Management Systems (TMS), GPS tracking, and AI-based route optimization improves asset utilization and reduces fuel use. Automation in loading/unloading cuts turnaround time and damages. 6. Streamline Supply Chain Set up regional hubs near consumption centers. Aggregate demand to enable full-rake dispatch. Just-in-Time (JIT) inventory models cut warehousing and demurrage. Collaborate with 3PLs for cost-effective delivery and tracking. 7. Align with Policy & Incentives Leverage the National Logistics Policy’s aim to reduce logistics costs to 5–6% of GDP. Tap freight subsidies, tax incentives for logistics infra, GST pass-through, and single-window clearance for sidings and terminals. 8. Optimize Last-Mile & Maintenance Route planning tools reduce last-mile costs. Strategically located warehouses shorten delivery time. Preventive maintenance of fleets improves uptime and fuel efficiency. Impact Snapshot Rail over road: 20–30% cost saving Waterways: 40–60% Route optimization/backhauling: 10–15% Terminal/siding access: 5–10% Conclusion Combining modal shift, infrastructure upgrades, tech adoption, and policy alignment can reduce logistics costs by up to 40%. This is critical to meeting India’s steel production target of 255–300 million tonnes by 2030 and boosting global competitiveness.

One of the most fascinating projects I have worked on eventually became US Patent… a system for multi-modal journey optimization. At first glance, it sounds straightforward: get a traveler from point A to point B as quickly as possible. But in reality, this is not a “shortest path” problem. It is a problem of navigating combinatorial explosion under uncertainty while still producing results that humans will actually use. The lesson was simple, but profound: a single “optimal” route is often the wrong answer. In practice, commuters do not blindly follow whatever the algorithm declares “fastest.” They balance hidden costs (number of transfers, reliability, waiting time) against raw travel time. A route that is one minute slower but has one fewer transfer will often be preferred. We approached this by abandoning the idea of returning just one solution. Instead, we designed an iterative search that keeps a fixed-length priority queue of candidate paths, pruning aggressively to keep the search tractable, but always preserving multiple high-quality alternatives. The output is a set of Pareto-efficient options: fast, but also different enough that a user can choose the one that fits their risk tolerance, comfort level, or schedule flexibility. This project shifted how I think about optimization. The real challenge isn’t mathematical purity, it is making decisions robust to the messiness of the real world. If the solution space is reduced to a single “optimal” point, you risk oversimplifying reality and delivering something no one wants to use. When we expose the trade-offs explicitly, we help people make better decisions.

I turned Singapore's road network into a graph. Here is what it revealed for urban decision-making. Using OSMnx, NetworkX, and Python, I built a morphological graph of Singapore's street network. The output is a topological map of approximately 15,000 intersections connected by roughly 30,000 road segments spanning thousands of kilometers. This is not just a visualization. It is a decision-making tool. Six practical insights this graph enables: One, identify critical intersections. Nodes with high degree connect six or more roads. If one of these fails, a large area becomes paralyzed. These intersections deserve priority maintenance and redundant traffic systems. Two, optimize emergency response. Calculate five-minute coverage zones from fire stations and hospitals. Find underserved neighborhoods before an incident happens, not after. Three, guide retail placement. A convenience store at a high-degree intersection reaches three times more passing traffic than one on a quiet street. Delivery hubs and billboards belong at these nodes. Four, detect accessibility gaps. Connected components reveal isolated communities. If a neighborhood sits in a small component, residents have fewer route options and longer emergency travel times. Build bridges or add alternative routes. Five, predict congestion bottlenecks. Betweenness centrality identifies roads that carry the most through traffic. Adjust signal timing and lane allocation before congestion becomes chronic. Six, support environmental planning. Low network density and long road segments correlate with urban heat. These become priority zones for tree planting and shade infrastructure. The graph is now ready for Graph Neural Networks. Adding bus stops, planning zones, and contiguity edges will transform it into a heterogeneous urban graph for predictive modeling. All built with open tools and in Python. #UrbanAnalytics #GraphTheory #NetworkX #Singapore #SmartCity #TransportPlanning #DataScience #Geospatial

🚇 [JUST PUBLISHED!] How can flexible train formation and skip-stop operations enhance the efficiency of urban rail transit? This new study by @Feng Li, @Yue Zhang, @Xin Guo, and @Tingxu Chen introduces an integrated optimisation framework that synergises flexible train formation and skip-stop strategies to boost operational efficiency and capacity utilisation. Key takeaways: 🔍 The proposed model optimises train stop schedules, arrival and departure timings, and formation configurations to improve urban rail system performance. 🚉 Flexible train formation dynamically adjusts capacity through coupling/decoupling, aligning real-time service with fluctuating passenger demand. ⏩ Skip-stop strategy reduces travel time by selectively bypassing stations while maintaining accessibility and safety constraints. 📊 Case study on Beijing Subway Line 9 demonstrates: ✅ 24.8% fewer stranded passengers ✅ 13.2% reduction in average waiting time ✅ 14.1% fewer train formations used 🧠 The study’s dual-strategy coordination mechanism establishes a data-driven foundation for intelligent rail transit scheduling and congestion mitigation. 🔗 Read the paper: https://bit.ly/48yKTaT #UrbanRailTransit #TimetableOptimisation #FlexibleTrainFormation #SkipStopStrategy #TransportPlanning #SmartMobility #TransitEfficiency #SustainableTransport #publictransport #scheduling #timetabling

Route optimisation is usually spoken about as if it’s a magic tool. In reality, it’s only the last 20%. The first 80% is transportation planning . getting the network right, designing the legs, understanding the geography, and choosing the right mode of movement. If hubs are misplaced, legs are inconsistent, or terrain and demand patterns aren’t understood, even the most advanced algorithms will struggle. A great routing engine on a weak network only produces complicated inefficiencies. Our experience at Driver has been simple: once the backbone is strong, routing becomes logical and predictable. We’ve seen this clearly in our PTL network : clean legs, data visibility, stable lane behaviour, and the routing layer suddenly starts to make sense. The industry is also shifting away from “shortest route” thinking to more contextual optimisation: reliability of lanes, cost per kg moved, ETA confidence, cross-dock logic, loadability, and constraint-based decisions. Tech has an important role, but it should serve the network design . not replace it. That’s the philosophy behind how we operate, and also how we’re building Qompass Now. Start with clarity. Build the backbone. Optimise after that. #scm #tms

𝗧𝗿𝗮𝗻𝘀𝗽𝗼𝗿𝘁𝗮𝘁𝗶𝗼𝗻 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: 𝗧𝗵𝗲 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗘𝗻𝗴𝗶𝗻𝗲 𝗼𝗳 𝗠𝗼𝗱𝗲𝗿𝗻 𝗦𝘂𝗽𝗽𝗹𝘆 𝗖𝗵𝗮𝗶𝗻𝘀 𝗧𝗿𝗮𝗻𝘀𝗽𝗼𝗿𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝘀𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝗺𝗼𝘃𝗶𝗻𝗴 𝗴𝗼𝗼𝗱𝘀; 𝗶𝘁'𝘀 𝘁𝗵𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗲𝗻𝗴𝗶𝗻𝗲 𝗱𝗿𝗶𝘃𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗿𝗻 𝘀𝘂𝗽𝗽𝗹𝘆 𝗰𝗵𝗮𝗶𝗻𝘀 𝗮𝗻𝗱 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 This foundational role physically moves value, connecting suppliers, manufacturers, distribution centers, and customers to create crucial "place utility" and "time utility." 1) Diverse Logistics Modes & Intermodal Systems: We examine the distinct advantages of various logistics modes—Road, Rail, Marine, and Air. Each mode offers a unique balance of speed, cost, reliability, and carbon footprint. Notably, Intermodal systems brilliantly combine the long-haul efficiency of Rail with the flexibility of Truck for first/last mile, significantly enhancing cost and carbon efficiency through standardized containers. 2) Road Freight Dynamics: Understanding models like Full Truckload (FTL) and Less-than-Truckload (LTL) is crucial. FTL typically involves point-to-point direct movement for high-volume goods, while LTL operates on a hub-and-spoke model, consolidating smaller freights. Mastering LTL freight class logic, where density directly drives rates, is a key practical insight for cost optimization. 3) Freight Benchmarking & Pricing Models: Navigating market volatility in transportation rates demands rigorous benchmarking. By leveraging neutral tariffs (e.g., CZARLite), businesses can ensure competitive pricing and move beyond blanket rates to effectively utilize customer-specific or spot rates, guaranteeing "apples-to-apples" comparisons and unlocking significant cost savings. 4) Transportation Management Systems (TMS) Lifecycle: A TMS acts as the 'brain' of your transportation operations, linking strategic planning to daily operational routing, load building, auditing, and track & trace. The TMS lifecycle, from assessing business requirements and tool selection to deployment and continuous sustainment, is paramount. A robust TMS provides real-time visibility, centralized control, automation, and essential features like load tendering, automated freight cost tracking, and auditing, spanning operational, tactical, and strategic planning. 5) Key Performance Indicators (KPIs): To measure and evaluate performance effectively, a data-driven approach is essential. Critical KPIs include: - Financial: Cost as % of Sales, Cost per Unit/Mile. - Service: On-Time In Full (OTIF), Transit Time Accuracy. - Operational: Empty Miles, Asset Utilization, Truck-to-Load Ratio. Ultimately, effective transportation management transforms physical value movement into a strategic competitive advantage. It's about intelligently balancing inventory costs, leveraging cutting-edge technology, and making data-driven decisions to optimize every leg of your journey.

#𝐋𝐎𝐆_𝐍𝐎_𝟔𝟕 🚏 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐢𝐧𝐭𝐨 𝐔𝐫𝐛𝐚𝐧 𝐏𝐮𝐛𝐥𝐢𝐜 𝐓𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭 🚋 Efficient urban public transport systems are critical for sustainable mobility, congestion management, and economic growth. The book "𝐔𝐫𝐛𝐚𝐧 𝐏𝐮𝐛𝐥𝐢𝐜 𝐓𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭" 𝐛𝐲 𝐑𝐢𝐳𝐚 𝐀𝐭𝐢𝐪 𝐑𝐚𝐡𝐦𝐚𝐭 delves into key engineering, operational, and planning aspects that shape modern transit systems. 📌 𝐊𝐞𝐲 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐀𝐬𝐩𝐞𝐜𝐭𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝: ✔ 𝐏𝐮𝐛𝐥𝐢𝐜 𝐓𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐃𝐞𝐬𝐢𝐠𝐧: ● Route optimization techniques and demand-responsive transit planning. ● GIS-based network analysis for efficient coverage and accessibility. ✔ 𝐓𝐫𝐚𝐟𝐟𝐢𝐜 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 & 𝐂𝐨𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧  ● Bus Rapid Transit (BRT), Light Rail Transit (LRT), and Metro Systems: Design standards, headway control, and signal priority systems. ● Traffic Signal Coordination: Adaptive and pre-timed signal systems for public transport priority at intersections. ✔ 𝐓𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭 𝐃𝐞𝐦𝐚𝐧𝐝 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 & 𝐂𝐚𝐩𝐚𝐜𝐢𝐭𝐲 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠: ● Multi-modal transport integration strategies. ● Simulation models for evaluating passenger flow, peak-hour demand, and transit-oriented development (TOD). ✔ 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 & 𝐑𝐨𝐥𝐥𝐢𝐧𝐠 𝐒𝐭𝐨𝐜𝐤 𝐃𝐞𝐬𝐢𝐠𝐧: ● Structural and material engineering considerations for rail and bus transit corridors. ● Pavement design for dedicated bus lanes and bridge load factors for transit vehicles. ✔ 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐓𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 (𝐈𝐓𝐒) 𝐢𝐧 𝐏𝐮𝐛𝐥𝐢𝐜 𝐓𝐫𝐚𝐧𝐬𝐢𝐭: ● Automated fare collection (AFC), real-time passenger information systems, and GPS-based fleet management. ● Predictive maintenance of transit fleets using IoT and AI-based monitoring. ✔ 𝐒𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲 & 𝐄𝐧𝐞𝐫𝐠𝐲 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: ● Low-emission transit vehicles, electrification of bus fleets, and alternative fuel technology. ● Lifecycle cost analysis (LCA) and carbon footprint reduction strategies in public transport. With rapid urbanization, integrating cutting-edge technology, efficient design, and robust policy frameworks is essential for building high-performance, scalable, and sustainable public transit systems. 📘 𝐑𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞: Urban Public Transport by Riza Atiq Rahmat #𝐏𝐮𝐛𝐥𝐢𝐜𝐓𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭 #𝐔𝐫𝐛𝐚𝐧𝐌𝐨𝐛𝐢𝐥𝐢𝐭𝐲 #𝐓𝐫𝐚𝐧𝐬𝐢𝐭𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 #𝐓𝐫𝐚𝐟𝐟𝐢𝐜𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 #𝐁𝐑𝐓 #𝐋𝐑𝐓 #𝐌𝐞𝐭𝐫𝐨𝐒𝐲𝐬𝐭𝐞𝐦𝐬 #𝐒𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐥𝐞𝐓𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭 #𝐒𝐦𝐚𝐫𝐭𝐂𝐢𝐭𝐢𝐞𝐬 #𝐈𝐓𝐒 #𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 #𝐓𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭𝐚𝐭𝐢𝐨𝐧𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 #𝐔𝐫𝐛𝐚𝐧𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 #𝐅𝐮𝐭𝐮𝐫𝐞𝐎𝐟𝐌𝐨𝐛𝐢𝐥𝐢𝐭𝐲 #𝐓𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭𝐃𝐞𝐦𝐚𝐧𝐝𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 #𝐆𝐈𝐒 #𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥𝐓𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭 #𝐂𝐨𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 #𝐓𝐫𝐚𝐧𝐬𝐢𝐭𝐎𝐫𝐢𝐞𝐧𝐭𝐞𝐝𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭

While AI continues to advance in various fields, traditional mathematical techniques like Linear Programming remain highly valuable, especially in optimizing routing processes. The image below illustrates a time-expanded graph depicting the optimal routing of three batches of vehicles, minimizing the overall duration until all cars reach their destinations. This optimization includes maintaining full flow conservation and adhering to maximum road capacities. Utilizing a graph database for the road network significantly reduced the number of timed road edges. The Linear Programming (LP) problem was automatically formulated using Cypher and JavaScript, considering three batches of cars, 20 time steps, and 12 edges, resulting in an LP file of nearly 500k lines. The user interface and visualization were facilitated through Graphileon with yFiles.

China is encountering new challenges in optimizing its merchandise distribution network amidst its expanding economy and global prominence. To address these challenges effectively, leveraging simulation and digital twin tools can significantly enhance cost-efficiency and elevate customer service standards. Similar to optimization projects worldwide, key components for a successful initiative include: - Forming a knowledgeable project team that considers product intricacies and network components. - Compiling data on network structures, transportation links, and their respective volumes over time. - Analyzing financial information related to transportation links to establish cost per unit for simulation purposes. - Consolidating fixed asset details, inventory specifics, and product categorizations. - Validating operational costs within a 5% margin annually, collaborating closely with the financial department for validation. - Strategizing various scenarios to achieve project objectives such as consolidation, territorial expansion, cost reduction, and inventory optimization. - Conducting simulations, validating assumptions through market research, and confirming feasibility. - Streamlining options by eliminating impractical choices based on predefined evaluation criteria. - Focusing on 2-3 viable scenarios for in-depth feasibility analysis. This approach offers substantial benefits including reduced transportation and warehousing expenses, enhanced customer service levels, quicker delivery times, increased supply chain flexibility, and improved inventory turnover. For tailored optimization frameworks or models based on specific business cases, geographies, or constraints like green logistics or last-mile delivery, GCL can provide detailed solutions. Share your experiences and insights to further enrich the optimization process. #SupplyChainOptimization #supplychain #Logistics #CustomerService #BusinessStrategy #Transportation #Inventory #Procurement #gclgroup