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.

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

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

How can we customize multi-modal travel journey planning while accounting for user's preferences as well as the integration between fixed- and flexible on-demand services? https://lnkd.in/eCkzzU7g We propose a preference-based optimization framework for multi-modal trip planning with public transport, ride-pooling services, and shared micro-mobility fleets. We introduce a mixed-integer programming model that incorporates preferences into the objective function and solve it for real transport network data in a suburban area of Rotterdam. Model application results demonstrate that the proposed algorithm can efficiently obtain near-optimal solutions. Managerial insights are gained from comprehensive experiments that consider various passenger segments, costs of micro-mobility vehicles, and availability fluctuation of shared mobility. open-access, with Yimeng Zhang and Shadi Sharif Azadeh, part of the SUM Project funded by the European Commission, in collaboration with RET.