PTV Logistics Product Forum

How to handle dynamic workload changes such as seasonal peaks, e.g. Black Friday, Christmas, “snowbirds invading Florida in winter”

Dynamic workload changes - This can mean multiple things, in my experience.

For daily planning:
For day-to-day operations, variance will be reflected in the data you provide to OptiFlow, of course.
The question is how you want to deal with it – OptiFlow can handle a wide variety of scenarios, such as:

• Relaxing constraints (such as timewindows) using cost-based penalties
• Adding additional vehicles with specific rules and cost-settings – OptiFlow will determine the best vehicle for the job based on the costs.
• Deciding which orders are more interesting to outsource based on revenue / outsourcing costs.
• The ability to pass pre-constructed routes, which OptiFlow can re-optimize in case violations are detected.
  (note: this is not a means to create “as-is” or violated routes)

For tactical planning:

OptiFlow is great tool for scenario generation, especially now that we have PTV Mira to facilitate setup and analysis.
When it comes to modeling scenarios based on long-term data, much of it of course comes down to data analysis and pre-processing.
But some techniques that we often use include:

• Setting loads that reflect (average) volumes on each weekday, to create robust plans that account for variance across the week
  Naturally we can manipulate these volumes to account for certain percentiles instead, rather than straight averages.
  And here the overloads feature can again be used to handle outliers.
• “Risk pooling” – we can assign risk values as capacities (loads) to certain orders, and use this to limit the amount of “volatility” that can go on each route.

Dynamic Workload Changes (Seasonal Peaks)
This is a scenario modeling question rather than a single feature. The approach:

• Demand side — Growth Simulation: Use the geographic growth simulation pattern to add realistic orders in specific regions (e.g., +30% deliveries in Florida for winter). Orders can be generated at real business locations using the Places API, clustered in the affected geography.
• Supply side — Fleet Adjustment: Add vehicles, extend operating hours (earliestStartTime / latestEndTime), or configure overtime to allow drivers to work beyond their normal shift at a premium cost. You can also use fleet distribution to reassign vehicles across depots — e.g., shift capacity from northern to southern depots for the snowbird scenario.
• What-if comparison: Run the baseline planning and the peak-demand planning side by side to quantify the impact: how many additional vehicles are needed, how much overtime, how many orders go unplanned.

There's no "seasonal mode" toggle — you model the changed reality (more orders, shifted geography, adjusted fleet) and let the optimizer solve it. The power is in being able to quickly create these scenario variants and compare outcomes.

Well-structured split between daily and tactical. That distinction is valuable and something I glossed over. The daily planning points (relaxing constraints, adding vehicles, outsourcing decisions, pre-constructed routes) are all correct and practical.
Using a load dimension to represent volatility/risk and capping it per route is a modeling pattern. It's not a standard API feature but a use of the capacity mechanism — worth noting that it's a modeling technique rather than a built-in feature, so the audience understands they'd need to compute those risk values externally.
The weekday-volume approach for tactical planning uses loads that reflect average (or percentile-based) volumes per weekday, combined with overloads for outlier handling, as a practical pattern for robust planning.
One thing I'd add: The scenario comparison angle. OptiFlow's strength for seasonal planning isn't just running one scenario — it's running baseline vs. peak side by side and quantifying the delta (extra vehicles needed, overtime cost, unplanned orders). That's where the real decision support comes in.