Intermodal drayage remains one of the most operationally complex segments in trucking. Marine terminals, rail ramps, chassis pools, and customer facilities each impose strict time windows, equipment requirements, and financial penalties. Traditional dispatch methods relying on manual pairing of loads frequently result in excessive bare chassis repositioning, prolonged per diem charges, and avoidable demurrage.
At Strong Tower Consulting we apply directed multi-graph modeling and constrained shortest-path algorithms to move drayage planning from manual pairing toward decisions that are optimal for an explicit cost model. This approach simultaneously optimizes chassis splits and dual-transaction sequences while respecting all operational constraints.
- A chassis split forces bare chassis moves that cost fuel, driver time, and potential per diem fees.
- Model the network as a directed time-dependent multi-graph of terminals, rail ramps, chassis pools, and customer docks.
- Solve two problems together: minimum-cost chassis-split assignment and maximum dual-transaction pairing.
- Run the graph engine as a microservice beside the TMS, reading EDI 204 and 214 and writing back via REST or SQL staging tables.
- Recalculate the model every 15-30 minutes as tenders, status changes, and appointments arrive.
Problem Definition: The Chassis Split and Dual-Transaction Challenge
In intermodal operations a single chassis is rarely used for both the loaded container delivery and the subsequent empty return or repositioning move. This creates a "chassis split" where the equipment used on one revenue leg must be returned or swapped at a pool before the next move can begin. Each bare chassis move incurs fuel, driver time, and potential per diem fees. When multiplied across hundreds of daily transactions at major ports such as Los Angeles/Long Beach, Oakland, or Savannah, these non-revenue miles become a significant cost center.
Simultaneously, terminals and railroads assess demurrage and storage charges when containers are not returned within allotted free time. The dual-transaction problem therefore requires finding sequences that both minimize chassis repositioning and guarantee timely return of equipment.
Graph-optimized planning
- Directed multi-graph modeling of every location and time window
- Constrained shortest-path and minimum-cost flow algorithms
- Chassis splits and dual transactions optimized together
Traditional dispatch
- Manual pairing of loads
- Heuristic guesswork at scale
- Excessive bare chassis repositioning and avoidable demurrage
Graph-Theoretic Formulation
We model the operating environment as a directed time-dependent multi-graph G = (V, E, T). Nodes V represent physical locations (marine terminals, rail ramps, chassis pools, customer docks) at discrete time points. Edges E encode feasible moves with associated costs, travel times, and equipment compatibility constraints.
G = (V, E, T)
V = {terminals, rail ramps, chassis pools, customer docks} at discrete time points
E = feasible moves (cost, travel time, equipment compatibility)
Each edge carries multiple attributes:
- Travel time and distance
- Chassis type required (20 ft, 40 ft, 53 ft, reefer capable)
- Time-window feasibility at origin and destination
- Whether the move is loaded, empty, or bare chassis
- Demurrage/per diem cost accrual rate
Using this structure we solve two related problems:
- Minimum-cost chassis-split assignment that satisfies all loaded and empty container commitments
- Maximum dual-transaction pairing that eliminates bare chassis legs while respecting driver hours-of-service and appointment windows
| Metric | How to Measure | Source System |
|---|---|---|
| Bare chassis miles | Miles run with a chassis and no container, per day | TMS move legs, ELD or GPS mileage |
| Chassis per diem days | Chassis days billed beyond pool free time | Chassis pool provider invoices |
| Demurrage incurred | Terminal and rail storage charges by container, after free time | Terminal and railroad invoices, EDI 214 gate events |
| Driver paid hours | Paid hours per day for drivers on drayage moves | Driver settlement records |
| Total daily cost | Sum of the components above plus fuel for the day's moves | Accounting, settlement, and fuel card data |
Change = (Pilot value - Baseline value) / Baseline value
Compare like periods: same terminals, similar volume, same season where possible
Integration with Existing TMS Platforms
The graph engine is designed as an independent microservice that consumes standard EDI transactions (204 tender, 214 status) and appointment data from terminal portals. Optimized move sequences are returned via REST API or written directly into SQL staging tables compatible with McLeod, Truckmate, or TMW systems. Driver settlement rules, fuel surcharge calculations, and detention billing logic remain untouched in the host TMS while the dispatch board receives optimized load sequences.
This architecture preserves the carrier's investment in their core transportation management system while adding optimization where the planning problem is hardest to solve by hand.
Implementation Considerations for Motor Carriers
Successful deployment requires accurate digital representations of:
- Terminal and rail gate operating hours
- Chassis pool locations and exchange rules
- Customer appointment windows and receiving constraints
- Real-time container status via EDI 214 and API feeds
- Driver and tractor availability with hours-of-service compliance
Strong Tower Consulting works with carriers to extract and normalize this data from existing TMS databases, EDI transaction logs, and third-party visibility platforms. The resulting graph model is recalculated every 15-30 minutes to reflect new tenders, status changes, and appointment confirmations.
Metrics to Measure in a Pilot
The effect of graph-based chassis split optimization depends on terminal mix, chassis pool rules, appointment availability, and data quality, so it should be measured rather than assumed. Record a baseline period of bare chassis miles, chassis per diem days, demurrage charges, and driver paid hours from the TMS, pool invoices, terminal invoices, and settlement records. Then run the optimizer in recommendation mode, compare its plans against the moves actually run, and track the same metrics during a live pilot. Dispatcher override rates and schedule predictability for drivers are worth tracking alongside the cost figures.
Frequently Asked Questions
What is chassis split optimization in intermodal drayage?
Chassis split optimization models the assignment of container moves where the chassis used for the loaded transaction is different from the one required for the subsequent empty or repositioning move. Using directed multi-graphs with time windows, the model minimizes non-revenue bare chassis miles while respecting terminal, rail ramp, and customer appointment constraints.
How does graph theory improve drayage efficiency?
By representing marine terminals, rail intermodal ramps, chassis pools, and customer facilities as nodes in a time-dependent directed graph, we can apply shortest-path, minimum-cost flow, and vehicle routing algorithms with time windows. This identifies dual-transaction sequences that can remove empty repositioning legs and lower per diem and demurrage exposure, which each carrier should measure against its own baseline.
Can this approach integrate with existing TMS platforms like McLeod or Truckmate?
Yes. The graph model is typically implemented as a microservice that consumes EDI 204 tender data, real-time 214 status messages, and terminal appointment slots. Optimized move sequences are written back to the TMS via API or SQL staging tables, allowing seamless integration with McLeod, Truckmate, or TMW dispatch workflows.
How should a motor carrier measure results from chassis split optimization?
Results depend on terminal mix, chassis pool rules, appointment availability, and data quality, so there is no reliable general figure. Record a baseline period of bare chassis miles, chassis per diem days, demurrage charges, driver paid hours, and on-time performance from the TMS, pool and terminal invoices, and settlement records. Then compare the same metrics during a pilot of the optimizer against that baseline.
Conclusion
Intermodal drayage is a natural application for graph-based optimization. The combination of high equipment cost, strict time windows, and complex equipment exchange rules creates a combinatorial problem that human dispatchers cannot solve optimally at scale. By modeling the network as a directed multi-graph with time-dependent constraints, carriers can systematically identify avoidable bare chassis moves and per diem and demurrage exposure, then measure the change against their own baseline.
Strong Tower Consulting, LLC and Exspeedite LLC are separately organized sister companies under common ownership. Carriers never have to buy our software. Our only product is better decisions.
Stop paying for bare chassis miles.
Strong Tower Consulting provides independent systems architecture, feasibility audits, and engineering roadmaps for motor carriers and freight brokers. Contact our advisory team or call 763-244-1373 to discuss your operational data and evaluate practical implementation paths for your fleet.