Helios Vega Interplanetary Logistics operates 15 nodes across Earth, orbit, and the lunar surface — for a baseline of €156M in annual fixed cost. The Board wants to know which to keep open under genuine uncertainty. This is your analysis dashboard.
Eight files describe the system. Click any card to inspect the raw rows — distances, demand distributions, scenario probabilities, cost rates.
Designing an interplanetary logistics network under genuine uncertainty. Our recommendation: operate — of 15 nodes at a probability-weighted expected annual cost of —. The network sustains all 9 disruption scenarios and holds under Monte Carlo stress testing.
The dashboard is not just a visualization of an answer. It documents the rules that were applied so the Board can see why the recommendation is robust: single-source quarterly flows, no flexible capacity, disruption reassignment, explicit workforce/treaty clauses, and Monte Carlo gravity-factor risk.
Each candidate node is treated as an open/closed binary decision across all 15 nodes. The dashboard recomputes the open/closed set live; off-world facilities are not locked open, but any closure triggers the relevant treaty cost.
For every customer-quarter, demand is assigned using the briefing-card heuristic: nearest feasible open node with remaining quarterly capacity. Quarterly capacity is exactly annual capacity divided by four; if capacity is insufficient, the served part touches only one node and the remainder is unserved and penalised. This is disclosed as a greedy heuristic, not a full min-cost-flow optimiser.
All nine disruption scenarios are evaluated and probability-weighted. A disrupted node is not closed: it still pays fixed cost, but its affected-quarter capacity is zero and its customers must be reassigned.
Workforce agreements are not hidden footnotes. The model detects every explicit DC closure clause: WR01 German dual-closure, WR02 Spanish dual-closure, WR03 Lyon retraining, WR04 Endurance deorbit, and WR05 Brand Base dormancy/repatriation. WR06 is treated as a hard guardrail rather than a cost item: terrestrial closures may not exceed six, and the financial impact remains zero.
After the network is fixed, Monte Carlo samples 1,000 futures from Gamma demand distributions. For flows touching distant off-world nodes, the model preserves mean demand and multiplies CV by the assigned node's demand_variance_factor before sampling. This follows the README wording: uncertainty grows with lead time, not baseline demand.
The output includes three hand-check transport calculations and a compliance checklist. This is designed to make the model auditable: a reviewer can recompute a sample route and trace the six cost components.
The Helios Vega network spans four operating volumes. Cargo committed to distant nodes locks in weeks or months ahead — time dilates with distance, and uncertainty grows with it. This is the conceptual centre of the problem.
This is where the dashboard becomes a sandbox. Toggle any of the 15 nodes on or off, fire a disruption scenario, drag the cost sliders — everything recomputes live against the same engine. Hit ★ Recommended any time to restore the full SA-optimal network and explore from there.
Recommended node configuration across terrestrial, orbital, and lunar operating volumes. Open nodes serve customers under the single-source assignment rule, by quarter.
Fixed, transport, SLA penalty, carbon, workforce/treaty, and unserved demand. Computed across all 9 scenarios, weighted by probability. The number reconciles.
Performance across the 9 official disruption scenarios plus two custom stress tests we designed. Only the 9 official scenarios are probability-weighted into the headline expected-cost metric; custom stress tests are shown separately and do not contaminate the recommendation number.
| ID | Scenario | Probability | Nodes Offline | Quarters | Cost (EUR) | Unserved Units / Carbon Δ |
|---|
Official rows S0–S8 are the only rows used in the probability-weighted expected-cost headline. Custom rows are separate stress tests for analytical depth and are not assigned probabilities.
One thousand Monte Carlo trials with Gamma-distributed demand, scenario sampling, and the gravity factor inflating variance for off-world flows. The histogram is the full distribution. The SA-optimised probability-weighted cost shown in the hero is recomputed live in the browser from the embedded data. The stochastic MC mean may differ because it samples demand uncertainty and disruption scenarios; gravity is implemented as mean-preserving CV inflation by assigned node.
Two techniques, executed and validated. Simulated Annealing to search the 32,768-option space, and Monte Carlo to stress-test the answer under genuine uncertainty.
SA explores network configurations by making random open/closed changes. It accepts improvements, and occasionally accepts worse moves (controlled by a "temperature" that cools over time) to escape local optima.
T_start = 3,000,000 cooling = 0.997 iterations = 2,500
Defence: The search uses the challenge-card neighbour rule: one node is flipped per iteration. When the user clicks Run Analysis, the browser actually recomputes SA from the embedded CSV data, then recomputes the selected network across all 9 disruption scenarios and runs Monte Carlo. No hidden precomputed answer is rendered as the final result.
Once SA delivers the recommended network, MC stress-tests it 1,000 times with random demand and random disruptions, producing a full probability distribution of cost outcomes — not just a single number.
trials = 1,000 Gamma: Marsaglia-Tsang gravity factor applied
Defence: MC samples demand from the supplied Gamma parameters, applies mean-preserving CV inflation using the assigned node's variance factor, samples one of the 9 official scenarios by probability, and reports mean, P95, and worst-10% tail risk. The custom stress tests are deterministic extras, not inputs to the official expected-cost metric.
Three network configurations explored: SA-recommended optimum, a cheaper-but-riskier minimal network, and a greener-but-pricier configuration. Plus a cost-vs-resilience frontier.
The model gives a cost-minimising answer under the rules we specified. The partner-level work is making the assumptions visible: where treaty cost is counted, where social risk remains, and what the Board should not mistake for certainty.
The chosen network closes DC11 Prague, DC12 Lisbon, DC13 Endurance Station, and DC15 Brand Base, while keeping DC14 Cooper Relay as the off-world operating node. That answer is only defensible because the cost model explicitly counts the closure consequences rather than silently treating closures as free.
Closing Endurance Station triggers WR04: €4.5M for deorbit/safe-disposal under the Outer Space Treaty Annex VII. Closing Brand Base triggers WR05: €2.8M for crew rotation, repatriation, and dormancy maintenance under the Artemis Operations Compact. The recommendation keeps these costs visible in the Workforce/Treaty component, which is exactly the kind of hidden constraint the brief warned teams not to miss.
The model enforces the Pan-European Works Council rule that terrestrial closures may not exceed six in the exercise. This recommendation closes only two terrestrial hubs, so it remains feasible. It also avoids the German dual-closure settlement and Spanish dual-closure settlement by keeping one or both relevant hubs open.
1. Assignment is greedy, not globally exact. This follows the challenge recipe and is displayed transparently, but another assignment heuristic could shift marginal flows. 2. Treaty cost is assumed fixed. A real negotiation could make DC13/DC15 closure more political or more expensive. 3. Gravity factor captures demand drift, not operational failure. It inflates demand CV but does not model launch failure or longer-than-planned lead times. 4. Scenario dependence is simplified. The nine scenarios are probability-weighted independently; a real energy crisis, labour action, and cosmic disruption could correlate.
Run more SA restarts, test alternative assignment heuristics, create a full cost-vs-tail-risk frontier, add multi-region concurrent disruptions, stress treaty exit costs, and validate customer relationship risks with commercial leadership. The current answer is a strong analytical baseline; the Board decision should also weigh customer, labour, and treaty judgement.