Dynamic yield pricing for perishable equipment hours

Arkham · Equipment Rental Yield Management

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Zi Humana Research presents Arkham — a dynamic pricing and yield engine that brings airline-style revenue management to equipment leasing. Reinforcement learning, stochastic dynamic programming, and real-time auctions clear the market for every machine-hour.

10.5281/zenodo.21873050Zi R&D CenterArkham 1.0
Fleet revenue lift
+29.6%
Projected annual revenue vs static rates
Utilisation
+16 pp
62% → 78% on a 50-machine fleet
Pricing engines
3
RL elasticity · SDP leases · spot auction
Quote TTL
~90 s
Non-transferable quotes to block arbitrage

Our work

Dynamic Yield Pricing for Equipment Rentals: Arkham Architecture, Operator Surfaces, and Laser Cleaning Fleet Case Study

Research Paper
Zi R&D Center · Zi Humana · AE Quantum Research Division

Equipment leasing suffers from fixed rates that ignore real-time demand. Arkham continuously processes supply and demand signals, combining RL agents for elasticity, SDP for long-term lease pricing, and a real-time auction for spot clearing. Projected results for a 50-machine laser-cleaning fleet show a 29.6% revenue increase and a 16-point utilisation gain without additional capital.

Dynamic PricingYield ManagementRL · SDP

Business overview — revenue, partners, and platform modules

Figure 1
Arkham research program overview
BusinessModel

Program architecture — quote path through RL, SDP, and auction

Architecture
Market-clearing price · revenue & audit path
APIEngines
Open access archive on ZenodoRead more research on our hub

Results & figures

Table 1. Projected fleet performance with Arkham versus static pricing (50-machine portfolio)
MetricStaticArkhamImprovement
Average utilisation62%78%+16 pp
Avg. revenue per machine-hour$18.50$21.30+15.1%
Total annual fleet revenue$1,620,000$2,100,000+29.6%
Booking satisfaction rate74%91%+17 pp
Table 2. Three pricing engines
EngineRoleUse case
Reinforcement learningDemand elasticity by verticalHeritage vs emergency industrial repair
Stochastic DPRisk-adjusted forward pricingLong-term lease lock-in with opportunity cost
Real-time auctionMarket clearing for spot rentals~90 s personalised, non-transferable quotes
Arkham business overview — revenue streams, partners, and platform modules
Figure 1. Business overview used in the research program — revenue streams, strategic partners, platform modules, and operating cost classes.
Arkham program architecture with RL, SDP, and auction pricing engines
Figure 2. Program architecture — quote request, pricing engines (RL, SDP, auction), market-clearing price, and revenue/audit path.
Arkham operator dashboard surfaces — quoting, partners, and API docs
Figure 3. Operator surfaces — sign-in, customer estimator, partners console, API docs, internal ops, and architecture/business views.
Arkham business model overview
Figure 4. Business model overview — revenue streams, partner channels, platform modules, and cost classes (infrastructure-agnostic).
Full Arkham business model diagram
Figure 5. Extended business model surface used in the operator documentation set.
Detailed Arkham program flow diagram
Figure 6. Detailed program flow — demand/supply signals through pricing and booking completion.
Detailed Arkham business and partner diagram
Figure 7. Detailed business and partner channel diagram from the research process set.

Open access · Arkham Research Initiative

Zi R&D Center · Zi Humana · doi:10.5281/zenodo.21873050 · Dynamic Yield Pricing · Borel Sigma Inc. venture

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Tools + Code

Research Paper

Arkham engineering report — economic rationale, RL/SDP/auction engines, operator surfaces, and laser-cleaning fleet case study.

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Arkham 1.0

Live dynamic pricing & yield console — quotes, fleet control, partner surfaces, and API documentation.

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GitHub · DYNPRIX

Open research codebase for Dynamic Yield Pricing — FastAPI fleet, quotes, bookings, tracking, and RL pricing.

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Zenodo Archive

Open-access deposit doi:10.5281/zenodo.21873050 — Zi R&D Center · Zi Humana Research.

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