Vooban optimizes waste collection with Hexaly, improving fill rate from 50% to over 80%
Vooban is a Canadian leader in applied artificial intelligence. It helps organizations design and deploy AI-driven solutions that accelerate digital transformation. Its expertise spans supply chain optimization, predictive maintenance, demand forecasting, and custom AI development.
In partnership with OSCAL.AI, a Montréal-based company specializing in waste collection and recycling optimization, Vooban set out to modernize planning for commercial and industrial waste streams. The goal was clear: reduce costs, improve fleet utilization, and design smarter, more sustainable collection strategies.
OSCAL.AI’s waste collection challenge
OSCAL.AI manages diverse waste streams (commercial waste, oils, and other recyclable materials) with a fleet serving thousands of collection points. Despite strong data and expertise, their waste collection planning faced critical limitations:
- Rule-based planning: routes were based on static simulations rather than mathematical optimization.
- Manual prioritization: contract requirements, customer-specific preferences, and punctual service calls were managed manually, resulting in inefficiencies.
- Suboptimal multi-day coordination: although routes were simulated across multiple periods, the day-by-day optimization approach limited the system’s ability to balance workload over the whole horizon properly.
As a result, trucks were underutilized (average 50% fill rate) and productivity was low (too many stops per week per resource). OSCAL.AI required an advanced solution to manage hybrid priorities (inventory-driven versus contract-based pickups), scale across thousands of locations, and integrate seamlessly with existing waste management systems.
A complex waste collection routing problem
To optimize waste collection, OSCAL.AI needed to address three key dimensions:
- Vehicles and routes
- Truck capacity and fill levels
- Route duration and working hours
- Depot preferences and sector restrictions
- Multi-period planning (routes spanning several days)
- Clients and collection sites
- Inventory dynamics and waste generation rates
- Time windows and service times
- Contractual requirements (visit frequencies, fixed pickup periods)
- Priority levels across customer clusters
- Business rules
- Mixed prioritization (strict schedules, frequency-based pickups, inventory-driven triggers)
- Penalties for deviations from business rules
These requirements combined hard constraints (truck capacity, route limits, time windows, forbidden sectors) with soft constraints (preferred depots, balanced workloads, minimizing unnecessary distance).
With Hexaly, Vooban enabled OSCAL.AI to balance these complex needs and create waste collection plans that were both efficient and sustainable.
Why hexaly for waste collection optimization?
Vooban selected Hexaly for OSCAL.AI’s waste collection project because of its:
- Scalability: able to manage thousands of decisions, constraints, and objectives across large planning horizons.
- Flexibility: business rules and contractual obligations modeled directly into the optimization engine.
- Time-to-market: rapid deployment of a robust solution without sacrificing quality.
- Transparency: explainable decision layer that stakeholders can validate and trust.
- Intuitive modeling: a powerful modeling interface with high-level declarative constructs.
The impact: smarter waste collection
The preliminary results showed clear operational gains:
- Stops per week per resource reduced by 15%: fewer stops while more waste collected.
- Average load per stop increased from 76 kg to 89 kg.
- Truck fill rate rose from 50% to over 80%.
- Smarter routing reduced the kilometers traveled while maintaining a better service quality.
By integrating Hexaly, Vooban delivered OSCAL.AI a scalable and explainable optimization solution that transforms waste collection into a smarter, greener, and more cost-effective operation.
With Hexaly, we turned a complex waste collection challenge into a smarter, greener, and more efficient solution, raising truck fill rate from 50% to 80%.
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