Operations Research

Solutions Benchmarking for
Human-Robot Co-Dispatch
Facility Location Problem

A secure, reproducible platform for benchmarking optimization algorithms and solution results, mathematical models, and research methodologies for the Human-Robot Co-Dispatch Facility Location Problem.

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Multi-Paradigm Support

Four Ways to Contribute

Our platform accepts diverse research contributions, from solution results to formal mathematical models.

Solution Results
Submit your optimization solution as a JSON file with facility selections and assignments.
JSON Format
Exact/Heuristic
Submit Solution →
Research Reports
Share methodology papers and technical reports.
PDF
Markdown
Submit Report →
Coming Soon
Source Code
Python/C++ heuristics running in isolated sandboxes.
Python
C++
Coming Soon
Mathematical Models
MiniZinc or AMPL formulations with solver evaluation.
MiniZinc
AMPL
Workflow

How It Works

A streamlined process from submission to ranked evaluation

1

Submit

Upload your code, mathematical model, solution, or research report through our secure interface.

2

Evaluate

Submissions run in gVisor-isolated containers against hidden benchmark instances.

3

Compare

Compare your results with other submissions on the leaderboard.

Research Context

The HRCD-FLP Challenge

The Human-Robot Co-Dispatch Facility Location Problem addresses critical infrastructure optimization for emergency response systems with hybrid human-robot fleets.

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Infrastructure Selection

Optimal Command Center placement with tiered levels (High/Medium/Low)

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Fleet Deployment

Heterogeneous human and robot resource allocation

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Supervision Constraints

Human-in-the-loop ratios (e.g., 1 human per 5 robots)

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SLA Compliance

Criticality-tiered response time thresholds

Supervision Constraintzi,Human ≥ α · zi,Robot
Coverage Requirement∀j ∈ D : covered(j) = true
Objective Functionmin Σ(Facility + Resource Costs)