Project 1.3: Resource & Logistics Planner (RPL)

A feasibility enforcement system that tracks materials, energy, and labor to ensure colony designs are both buildable and sustainable under extreme constraints.

Problem

Autonomous design systems often fall into the "Infinite Resource" fallacy—generating infrastructure that looks perfect in a simulation but ignores the brutal reality of supply chains, transport mass limits, and construction timelines.

The RLP matters because space is the ultimate resource-constrained environment. Without a Feasibility Enforcement layer, an AI might design a base that requires more oxygen than a rocket can carry or more power than a local grid can provide during the lunar night.

Solution

The RLP acts as the Operational Gatekeeper of the STC. It provides the "Reality Check" before any design is approved for execution.

  • Consumes: Final loadouts from the STC Simulation Engine validator and other characteristics from the Module data files.
  • Produces: Resource depletion timelines, supply chain requirements, and construction sequences.
  • Interfaces: Rejects designs from the Layout Engine that exceed transport or energy capacities.

Method

The system utilizes a dual-layer approach to logistics:

  • Multi-Objective Integer Linear Programming (MOILP): Used to solve the "Loadout Problem"—finding the minimum number of modules required to meet mission goals without exceeding mass budgets.
  • Discrete Time-Step Simulation: An hourly resource-tracking engine that monitors resource levels across a defined mission duration within the STC Simulation Engine.
  • Constraint Enforcement: A logic layer that triggers a "Mission Failure" if any resource dips below zero or if any objectives aren't met.

Tools & Technologies

Python, PuLP (Optimization Library), YAML, JSON Schema, NumPy

Diagrams / Visuals

[Logic Flow: Module, Environment, Agent, and Mission Data → Optimization → STC Simulation Engine]

Results & Outcomes

⚠️ STATUS: IN DEVELOPMENT

The Linear Programming Solver is currently functional, allowing the system to automatically choose between different modules and agents based on target goals and requirements.

Current Credibility: Successfully integrated a number of 72-hour tests including the "Mars Establishment" test case where the system identified the correct combination of modules and agents to meet parameters.

Next Steps

  • Labour Logic: Implementation of accurate agent and labour logic.
  • Transport Logic: Adding "Transport Capacity" constraints—limiting the total mass/volume the RLP can approve per launch.
  • Module Customization: Allow optimizer to customize the size, level of automation, and level of AI input to achieve more efficient combinations.