“Transport optimization” refers to the algorithmically supported planning and execution of shipments, routes, and networks to reduce costs, travel times, and empty miles while meeting service levels and complying with legal and operational constraints (e.g., time windows, drivers’ hours of service, vehicle and cargo restrictions). It is typically implemented in TMS, route planning, or fleet management software and spans strategic, tactical, and operational decisions from consolidation and routing to real-time re-optimization.
Order & shipment consolidation: Grouping shipments into cost-efficient routes considering weight, volume, dates, and service classes.
Route & tour planning (VRP/VRPTW): Automated calculation of optimal routes with time windows, multi-stop/multi-depot settings, and constraints (e.g., hazmat, cold chain, vehicle profiles).
Load & packing optimization (3D/2D): Best possible use of trailers, pallets, and bins including stacking and compatibility rules.
Dynamic re-optimization & ETA: Continuous adjustment of routes in case of disruptions (traffic, breakdowns, ad-hoc orders) with updated arrival times.
Driver & workforce scheduling: Incorporation of qualifications, availability, and hours-of-service/work rules.
Carrier selection & rate shopping: Automated choice of carriers/modes including price and performance evaluation.
Dock & slot management: Scheduling loading/unloading to reduce waiting time and detention.
Mode & network optimization: Choosing between road, rail, ocean, air, or intermodal options; hub/depot design.
Cost and CO₂ optimization: Calculation of transport costs, tolls, surcharges, and emissions (e.g., CO₂ per shipment).
What-if & scenario analysis: Comparing alternatives (e.g., different depot structures, service levels, fleet sizes) via simulation.
Track & trace with telematics: Integrating GPS/telematics for status, position, temperature, and exception handling.
Rule & constraint engine: Maintaining business rules (priorities, customer time slots, SLAs, blacklists).
Optimization methods: Use of MILP, heuristics, and metaheuristics (e.g., tabu search, genetic algorithms) for large problem spaces.
KPIs, dashboards & reporting: Service level, utilization, cost per stop/km, on-time performance, empty-mile ratio, CO₂ indicators.
API & master data integration: Connections to ERP, WMS, TMS, e-commerce, map/geo services, and customs/hazmat databases.
Returns & reverse logistics planning: Incorporating pickups, returnables, and backhauls into existing routes.
A grocery retailer plans daily store deliveries with cold-chain constraints and strict time windows to cut empty miles and safeguard freshness.
An e-commerce hub consolidates same-day orders and dynamically adapts routes as new jobs arrive or traffic conditions change.
A machinery manufacturer implements milk-runs between suppliers and the plant to bundle transports and lower inventory.
A carrier improves LTL operations through 3D load planning and minimizes rehandling in a hub-and-spoke network.
A pharma logistics provider schedules temperature-controlled routes with sensor monitoring to ensure compliance and on-time delivery.
An international shipper evaluates intermodal rail/road options to reduce CO₂ per shipment while maintaining lead times.