Sebastian Hörl

21 October 2026

PAAMS 2026

Iterative LLM-based construction of on-demand
fleet dispatching algorithms

Introduction

• On-demand mobility services
  • centrally operate a fleet of (autonomous) vehicles
  • give instructions to pick up / drop off passengers or relocate
  • try to serve the incoming customer demand under constraints (max. wait time, ...)

Fleet dispatching

• Fleet dispatching means defining which vehicle should pick up and drop off which requests and in which order
   
• Assignments need to adhere to constraints (latest pickup time, latest dropoff time, maximum detour, ...)
   
• Rigorous formulation: Vehicle Routing Problem
  • Too complex for large-scale applications
     
• Various heuristics exist
  • Insertion algorithms
  • Re-optimization algorithms

Idea

• Algorithms are often generic: they work on any city and in any context
   
• But we do have contextual knowledge (geography, demand patterns, special events, ...)
   
• How to integrate them into an algorithm?
  • Person 1 has the local knowledge
  • Person 2 knows how to formalize this algorithmically
  • Person 3 knows how to implement this in the specific base algorithm
  • Person 4 knows how to test this in simulation (and tweak the parameters)

• Can persons ons 2-4 be automated through intelligent code generation?

Test environment

• Using MATSim (a commonly used open-source agent-based transport simulation framework)
• Already provides functionality to implement (in Java) dispatching algorithms
   
• We added a remote dispatching interface (RDI) that let's users connect external algorithms via a networked API
   

matsim-org/matsim-libs

Hörl, S. (2026). Benchmarking on-demand mobility algorithms using a new remote dispatching interface for MATSim. MATSim User Meeting 2026 (MUM 2026). https://hal.science/hal-05739922

Test environment

• A couple of example algorithms in Python
  • FIFO assignment
  • Global Bipartite Matching
  • ...
     
• Allows using anything that is available in Python (scikit-learn, networkx, ...)
   
• Thorough documentation of the communication primitives

Test environment

Road network

• Extraction of Paris from OpenStreetMap
  (14k nodes, 24k edges)

Fleet

• Configurable number of vehicles generated on random initial links inside the network
• All have four seats

Customer requests

• Definition of N attractors / emitters with morning / evening factors
• Requests are generated from / to those points depending on time of day
• Represents directionality by time of day and spatial imbalance

Example benchmark

Test environment

• Containerized execution environment
  • One dispatching algorithm (Python file)
  • One MATSim instance
  • Inside a Docker container
     
• Writing a new algorithm using an LLM
   
• Evaluate the performance of the algorithm inside the container

Repeat

Prompt construction

You are designing a fleet dispatching algorithm. Some general explanations about fleet dispatching. You are part of an iterative loop. I present you the documentation, some examples, and the (R= 3) last versions that you have tested, plus the so far best one. You may used the standard output to gain information. Give each version a name, and explain features.

Three example algorithms

Documentation

Algorithm code

Objective value: ??? [if successful] / There have been errors! [if unsuccessful]

Standard output (last 100 lines)

Error output [if unsuccessful or timeout of 600s]  (last 100 lines)

"

3x

Backlog section

"

Best algorithm code

Objective value: ???

Standard output

"

R x

Fixed section

Elitist guidance

Test configuration

Simulation case

• 25 vehicles
• 2,000 requests

Profit-optimization objective

• Cost = 25 EUR per vehicle + 0.10 EUR per driven kilometre + 0.35 EUR per request
• Revenue = 0.50 EUR base fare per request + 0.3 EUR per passenger kilometre
• Profit = Revenue - Cost
   
• Additional payback of 0.5 EUR for each minute of delay upon arrival

Experiments

• Qwen3-Next-80B-A3B-Instruct on institute-local vllm infrastructure
   
• We see a steady increase of the objective over multiple algorithm versions
   
• Overtaking the Euclidean Bipartite Matching and Insertion (default MATSim algorithm) benchmarks
   
• Seemingly, making a trade-off between exploitation vs. exploration after about 40 versions

Experiments

• LLM time increases steadily with more information
   
• About 120k tokens for the largest cases

Experiments

The best algorithm

• Highest profit in comparison
• But lowest number of rides
   
• Effectively maximizes the revenue per request by rejecting non-lucrative customers

Profit-Optimized Dispatching with Cluster-
Specific Profit-Weighted Momentum and Adaptive Rejection Thresholding

Next steps

Ablations

• Backlog length
• Standard ouput length
• Different LLMs
• Different time budgets
• Different demand / fleet cases

More features

• Analysis step after each run with a summary output
• Reflection step to analyse observed demand patterns during each iteration

More directed test cases

• Artificial cases with varying density
  • Algorithm should overfit to one case
  • Perform worst on other cases
     
• Multiple (French) cities with different topologies
  • Can the LLM figure out geographical constraints and bottlenecks?

Interested?

Run these experiments out-of-the-box:

sebhoerl/llm-fleet-dispatching

Thank you!

sebastian.horl@irt-systemx.fr

Icons throughout the presentation: https://fontawesome.com

https://sebhoerl.github.io/

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