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Smart City

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.

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