Optimization Challenges in Transportation

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.

Most logistics consultants skip this step when optimizing small parcel services. It's the reason your ops are stuck at 80% efficiency.👇 Here's the truth: data is king in logistics optimization. But not just any data. The right data. The step most consultants miss? Comprehensive carrier performance analysis. They focus on rates, but ignore: - Actual transit times vs. promised - Damage rates by route and carrier - Exception handling efficiency - Claims resolution speed Without this intel, you're flying blind. Your optimization efforts hit a ceiling. You can't improve what you don't measure. How to fix it: 1. Implement detailed tracking for every shipment 2. Analyze patterns over 3-6 months 3. Identify weak points in your carrier mix 4. Negotiate based on real performance, not just rates 5. Continuously monitor and adjust Result? Happier customers, lower costs, smoother operations. The difference between good and great logistics is hidden in the details most overlook. Master these details, and watch your logistics transform. Optimize smarter, not harder. #LogisticsOptimization #DataDriven #CarrierPerformance #EfficiencyBoost #SupplyChainManagement #ParcelDelivery #OperationalExcellence #PerformanceAnalysis #ShipmentTracking #ContinuousImprovement

At NextBillion.ai, we're tackling some fascinating routing challenges that traditional systems simply can't handle. Waste management routing is a perfect example. Here are some constraints most mapping solutions aren’t set up for: But first, let me break down how waste management routing is different from standard delivery operations: 1. Route optimization Unlike precise delivery points, waste bins are often spread across entire neighborhoods— so 500 bins along seven streets rather than 10 specific addresses. This fundamentally changes how we approach route optimization. 2. Vehicle size Large waste trucks can maneuver the same way delivery vans can, so we need to avoid U-turns and sharp turns for safety and cost reasons. 3. Side-of-street requirements Manned and unmanned waste collection needs to be handled quite differently. Automated waste collection trucks need to approach bins from the correct side of the street, unlike manual collection where workers can cross the street to access bins. 4. Waste types Residential, commercial, and construction waste. Hazardous materials disposal has different trucking regulations than everyday household garbage pickup. Construction waste means multi-point scenarios—placing empty dumpsters at sites, collecting full ones, emptying them at the dump facility, and returning them to warehouses. Many of these scenarios are not easily tackled by typical route planning systems. Our route optimization API has specific parameters to solve for the very real problems waste management companies face. They can specify the streets they want to traverse, the correct direction waste bins should be approached, avoid inconvenient traffic maneuvers, and address the unique challenge of multi-point drop-offs.

When AI Falls Off the Tracks Transportation and logistics industries are prime candidates for AI-driven efficiencies—optimizing routes, scheduling maintenance, and forecasting demand. Yet, countless AI pilots have derailed because the underlying data was inconsistent or incomplete. Imagine an AI system designed to optimize train schedules, only to discover that delay logs from different stations used varying formats and timestamps. The system’s recommendations became unreliable, causing confusion and delays instead of improvements. To keep AI on track, focus on: Standardized Data Archiving: Aggregate data from GPS trackers, maintenance logs, and operations centers into a centralized archive with uniform schemas and metadata. Consistent Timestamping and Geocoding: Enforce strict rules for how time and location data are recorded—avoiding mismatches that confuse AI models. Governance for Cross-Functional Collaboration: Encourage IT, operations, and analytics teams to co-own data standards, ensuring everyone speaks the same data language. When transportation companies invest in these foundational elements, AI becomes a reliable partner—minimizing delays, reducing fuel consumption, and improving customer satisfaction. Without robust data management, AI recommendations can misguide operations and strain precious resources. Build a solid data runway first, and watch your AI take flight.

🚛 Navigating the Current Challenges in the Cargo Freight Industry 🚛 The cargo freight industry is facing unprecedented challenges—global disruptions, increased fuel costs, labor shortages, and evolving regulations are putting immense pressure on businesses. But in every challenge lies an opportunity for growth and innovation. 🔍 Key Challenges: 1. Supply Chain Disruptions: Delays in shipping and congested ports have become the new normal. 2. Rising Fuel Costs: Increasing operational expenses directly impact profit margins. 3. Driver Shortages: A lack of qualified drivers is making it harder to meet demand. 4. Sustainability Regulations: Stricter environmental guidelines are reshaping how we do business. 💡 Our Approach to Solutions: 1. Tech-Driven Efficiency: We’re leveraging real-time tracking, AI, and automation to improve route optimization, reduce idle time, and increase fuel efficiency. 2. Strategic Partnerships: Building strong alliances with suppliers and logistics partners to mitigate disruptions and ensure timely delivery. 3. Driver Recruitment & Retention: Offering competitive pay, better working conditions, and ongoing training programs to attract and retain top talent. 4. Sustainability Initiatives: Investing in eco-friendly trucks and exploring alternative fuels to stay ahead of environmental regulations and reduce our carbon footprint. The future is full of possibilities, and by staying agile and committed to innovation, we will continue to drive growth and success in the cargo freight industry. #CargoIndustry #FreightSolutions #LogisticsInnovation #SupplyChain #Sustainability #TransportationChallenges #FreightTech

The goal of supply chain operations? To create a fully digital mirror of the transportation process. While a Transportation Management System (TMS) is crucial, it's only one piece of the puzzle. You have to consider all of the other moving parts: integrations, tech, resourcing, BI capabilities, etc. Software providers claim to have the ultimate solution, but they can only take you so far. To truly optimize your transportation process, you need a holistic approach that goes BEYOND just implementing a TMS. You need actionable, accurate, and reliable data. And it's no walk in the park. You need a comprehensive strategy: --> Thorough evaluation of integrations and tech compatibilities --> The right people and training --> Robust BI capabilities --> Manual management of processes to ensure accuracy --> Continuous improvement and adaptation It's easier said than done. But when you get it right, you'll have so much data at your fingertips that will make it worthwhile. #SupplyChain #TMS #Logistics

Thrilled to share our latest publication on multi-phase optimization of transit network electrification under energy uncertainty, published in Transportation Research Part C: Emerging Technologies! 📄 Read the full paper here: https://lnkd.in/gTm46Zfx Our study presents an incremental transition plan for electric buses, taking into account both planning and operational decisions using a two-stage stochastic programming approach. 🎯 Goal: Electrification that maintains service reliability while optimizing charging infrastructure and fleet investments. 🔑 Key findings: * Charging strategies: Most charging occurs at depots overnight, with additional opportunity charging during off-peak energy hours. * Incremental transition: More charging stations in early phases, followed by larger fleet investments in later phases for efficient charging scheduling. * Fleet size impact: - About 15% increase in fleet size with current battery and charging technologies. - About 6% fleet size increase with extended battery capacity. - Negligible fleet size increase with fast charging, and 50% fewer charging stations. 🎉 Congratulations to Behnam Davazdah Emami, the lead author and a PhD candidate in #TransitLab! Thanks to my co-authors Yiling Zhang and Ying Song for their contributions, and to the Center for Transportation Studies, University of Minnesota, for supporting this research. #Optimization #Transit #Electrification #Energy #Uncertainty

🚛 Reducing Transportation Costs in the Supply Chain: The Digital Advantage 📉 In high-volume process industries, transportation accounts for 50-70% of total supply chain costs—meaning even small improvements can lead to significant profitability gains. While many “cost-saving” strategies are commonly recommended, most fall short in today’s volatile logistics landscape. Traditional approaches like route optimization, load consolidation, and carrier negotiation offer limited impact unless combined with digital transformation. Leading companies are moving beyond these generic solutions by: ✅ Eliminating unnecessary product movement (e.g., optimizing ship points dynamically) ✅ Reducing volatility in carrier selection (e.g., using AI-driven tools like LevelLoad) ✅ Optimizing truckloads with smart planning (e.g., Unilever’s AI-based load optimization) ✅ Minimizing carrier delays (e.g., dynamic scheduling with https://hubs.li/Q036YzmF0) Companies like Kimberly-Clark, Procter & Gamble, and Unilever are leveraging AI, machine learning, and advanced planning tools to drive 5-10% cost reductions and near-100% first tender acceptance rates. The future of supply chain cost reduction isn’t about squeezing carriers—it’s about intelligent, data-driven optimization. 🚀 How is your company tackling transportation cost challenges? Let’s discuss! 👇 hashtag#SupplyChain hashtag#Logistics hashtag#Transportation hashtag#CostReduction hashtag#DigitalTransformation hashtag#AI hashtag#TMS hashtag#FreightOptimization https://hubs.li/Q036Ypvm0

One of the biggest challenges with the conventional approach of transportation optimization is that for drivers and fleet managers, who are averse to change, it is easy to blow holes in the solution. They can find hundred different ways to "demonstrate" that the routes generated will not work in the real world. A traditional fleet or route optimization solution only incorporates a few essential inputs in the optimization process and creates routes based on those criteria. While those aspects are essential inputs for route or fleet optimization, unfortunately, the world is not Black or White. There is a multitude of factors that may impact the daily routes of your fleet that are not incorporated in classic route optimization solutions. I do not advocate for the most popular off-the-shelf VRP tools in the market. There are so few input variables in these tools, and the architecture does not allow for incorporating some of the issues drivers face in the real world. In the era where real-time dynamic route optimization is possible, there seems to be no excuse to keep deploying these solutions. Are these off-the-shelf solutions helpful at all? Yes, they are, but I think only for strategic-level analysis. These solutions may be suitable for initiatives like fleet optimization, driver headcount reduction, transportation network optimization, or a high-level evaluation of the impact of network redesign on last-mile transportation. However, as far as periodic route optimization goes, I suggest leveraging a customized solution if you have the resources. I find it frustrating to see companies leveraging an off-the-shelf tool for periodic route optimization in today's age of easy access to pertaining technologies. Technologies that can incorporate most of the sh*t the world can throw at your routes. Technologies that can help you build customized solutions. And even powered by Generative AI. #ai #transportation #logistics #vrp #routeoptimization #vehiclerouteoptimization #transportationrouteoptimization #logistics #supplychain #generativeai #optimization #orms #designedanalytics #data #analytics #datascience #operationsresearch #ml #digital #tech #technology #digitaltransformation #change