From network design to operational management, smart city services must be agile and data-centric. Decisions on where to locate resources and how to operate a system can be solved with mathematical optimization technologies.
Smart City
Challenges
- Interoperability of the system, secured and intelligent complementarity of data: descriptive, predictive and prescriptive.
- Environment-friendly infrastructures and transportation networks.
- Inclusiveness of innovative services.
Benefits
- Systemic and high-level data-driven decision support
- Optimized resource utilisation
- Fair and shared use of infrastructures
- Agile solutions that can be re-optimized dynamically
- Increased mobility efficiency
- Reduced negative externalities
- Services made profitable
Use cases
Multi-passenger On-Demand Transportation
Planning of shared on-demand transportation with multiple pickup and drop-off points.
Mobility-As-A-Service
Multimodal planning and synchronisation with stochastic optimization.
Shared-Bikes Positioning
Network design and dynamic positioning optimization on several stations.
In practice
Vehicle Routing
Inventory Routing
Pickup-and-Delivery Planning
Multimodal Routing
Multi-passenger Consolidation
Network Design
Location Design
Inventory Positioning
On-Demand Planning
Construction & Deconstruction Planning