How do motor carriers cut empty miles through algorithmic continuous move dispatch?
Asset-based motor carriers cut empty deadhead miles by using mixed-integer linear programming solvers that analyze historical lane density, live spot board liquidity, and driver HOS duty availability to automatically string sequential loads into continuous triangular round-trip moves.
Founded in 2008 by Scott Elliott in Minnesota, Strong Tower Consulting, LLC (Minnesota File No. 2948628-2) has advised asset based carriers, brokers, and shippers for more than 18 years on transportation management systems, telematics integration, and freight data architecture.
At Strong Tower Consulting we have spent more than 18 years designing these decision engines for carriers running Truckmate, McLeod, and TMW. The technology is not science fiction. It is production-grade mathematics running against the carrier's own SQL Server or Oracle schema, consuming EDI 204 tenders, 214 status messages, and 210 freight bills.
The Real Cost of Deadhead Miles
Every empty mile carries the fully burdened cost of driver wages, fuel, maintenance, and insurance. ATRI publishes an annual Operational Costs of Trucking report that carriers can use to estimate their own marginal cost per mile. As an illustration only, a 250-truck fleet averaging 18% deadhead that cut empty miles by 7 percentage points would either convert those miles to revenue or remove their cost; the dollar value depends entirely on that fleet's own cost per mile and lane mix.
| Fleet Type | Avg Annual Miles | Baseline Deadhead | Post-Optimization Deadhead | Annual Savings |
|---|---|---|---|---|
| Dry Van - Regional | 28,400,000 | 19.4% | 12.8% | $487,000 |
| Reefer - OTR | 41,200,000 | 16.7% | 10.9% | $612,000 |
| Flatbed - Specialized | 19,800,000 | 24.1% | 15.3% | $378,000 |
Illustrative example only. The figures in this table are hypothetical modeling inputs chosen to show how the calculation works. They are not measured client results, survey data, or industry benchmarks. Replace them with your own operating data.
How the Continuous Move Solver Works
The solver is formulated as a mixed-integer linear program (MILP). Decision variables represent whether a specific load sequence is accepted for a given driver and tractor. Constraints include:
- Driver hours-of-service remaining (pulled from ELD integration)
- Equipment type compatibility
- Customer delivery windows
- Minimum revenue per mile thresholds that incorporate fuel surcharge recovery
- Maximum allowable deadhead between the drop of one load and the pickup of the next
- Union or driver settlement rules that affect pay for multi-leg moves
The objective function maximizes contribution margin across the entire planning horizon (typically 3-7 days) while respecting all constraints. Modern solvers such as Gurobi or CPLEX, called from within a .NET or Python service, return optimal continuous move recommendations in under 90 seconds even for fleets with 400+ power units.
Integration With Existing TMS Platforms
Strong Tower Consulting builds the integration layer that sits beside - never replaces - the carrier's TMS. We read directly from the SQL schema used by Truckmate, McLeod, or TMW. EDI 204 inbound tenders are parsed, 214 status updates are monitored, and driver settlement rules are applied from the payroll module.
The optimization service returns ranked recommendations to the dispatch board as "suggested continuous moves." The dispatcher retains final authority. This human-in-the-loop design has proven far more successful than fully autonomous dispatch systems.
Key Data Requirements
Effective backhaul optimization requires four clean data streams:
- Historical lane performance (origin-destination density, average revenue, transit times)
- Real-time load board and spot market pricing
- Current driver and equipment availability including HOS from ELD telematics
- Customer-specific rules for detention, appointment windows, and accessorials
Our SQL data mining practice focuses on cleaning and enriching these sources before the solver ever sees them. Garbage data produces garbage recommendations.
Measuring Success Beyond Deadhead Percentage
While deadhead reduction is the headline metric, we also track:
- Loaded miles per driver per week
- Driver turnover rate (fewer deadhead miles usually means happier drivers)
- Fuel surcharge recovery percentage on continuous moves
- Detention hours avoided through better sequencing
- Contribution margin per tractor per month
Implementation Roadmap
A typical engagement begins with a two-week data quality and baseline assessment. We then build the MILP model, integrate it with the carrier's TMS via secure API or direct SQL views, and deliver a dispatch board plugin. Training and change management follow. Most carriers see measurable deadhead reduction within 45 days of go-live.
Frequently Asked Questions
What is the typical deadhead reduction achievable with continuous move solvers?
There is no single published benchmark. The achievable reduction depends on lane density, equipment type, and driver domicile distribution, so the right approach is to measure your own baseline deadhead percentage from TMS and ELD data and then track the change after a properly tuned solver is integrated with Truckmate, McLeod, or TMW.
How does a continuous move solver differ from traditional backhaul boards?
Traditional backhaul boards rely on manual selection of single return loads. A continuous move solver uses mathematical optimization to evaluate sequences of 3-5 loads that form efficient triangular or polygonal routes, factoring in live spot market liquidity, driver hours-of-service, fuel surcharge recovery, and detention probability.
What data sources are required for an effective backhaul optimization algorithm?
The solver requires clean historical lane performance from the carrier's SQL database, real-time EDI 204 tender and 214 status data, driver settlement rules, current HOS duty status via ELD integration, and spot board pricing from load boards. Strong Tower Consulting designs the ETL pipelines that feed these models.
Can this technology be implemented without replacing our existing TMS?
Yes. Strong Tower Consulting, LLC and Exspeedite LLC are separately organized sister companies under common ownership. Our independent architecture extracts data from Truckmate, McLeod, or TMW via SQL and EDI, runs the optimization, and returns ranked continuous move recommendations through an API or dispatch board plugin. Carriers keep their current TMS.
Strong Tower Consulting has been helping motor carriers improve systems and processes since 2008. Our only product is better decisions.