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Risk-Aware Multi-Objective Optimization for AI Infrastructure Supply Allocation under Demand and Supplier Uncertainty

Suchuan Xing, Yihan Wang
World Scientific Research Journal, (2026), Vol.12, No.10, pp.48-64
Published: September 17, 2026
DOI: 10.6911/WSRJ.202610_12(10).0005
PDF: Download Full Text PDF
Abstract

Operators of AI infrastructure must commit accelerator procurement months before demand is known, from a supply base whose deliveries are themselves unreliable. Existing supply-allocation models optimize expected cost and treat service risk and environmental impact as constraints or afterthoughts, which hides the trade-off that actually drives capital decisions. This paper formulates AI-accelerator supply allocation as a two-stage risk-averse three-objective stochastic program that minimizes expected total cost, the conditional value-at-risk (CVaR) of the service-level shortfall, and expected carbon emissions, subject to minimum order quantities, all-units volume discounts, capital budgets and diversification limits, under jointly uncertain demand, fulfilment yield, correlated regional disruptions and lead-time slippage. Demand scenarios are generated from a moving-block bootstrap of a real two-month production GPU-cluster trace comprising 737,137 tasks. We solve the problem with RA-TGEA, a matheuristic that combines (i) an exact closed-form vectorized recourse evaluator, verified against linear programming to a maximum relative error of 1.9e-14, (ii) a direction-aware local search driven by an exact closed-form sub-gradient of the CVaR tail, and (iii) progressive scenario refinement with tail-preserving stratified reduction. On an exactly solvable instance, RA-TGEA attains 98.1% of the hypervolume of an epsilon-constraint MILP reference front while being roughly three orders of magnitude faster at producing a dense front. Against five state-of-the-art multi-objective evolutionary algorithms under an equal wall-clock budget, it improves hypervolume by 9.1% and 20.4% on the two larger instances (p = 0.014). Out of sample on 2,000 unseen scenarios, the risk-aware model reduces attainable tail shortfall by up to 36.1% relative to a deterministic expected-value plan at the same cost. A sensitivity study quantifies the price of resilience: halving the tail shortfall target from 0.10 to 0.05 costs 56 percentage points of additional expenditure.

Keywords: AI infrastructure, supply allocation, conditional value-at-risk, multi-objective optimization, stochastic programming, supply chain resilience, sustainable computing.
APA Citation: Suchuan Xing, Yihan Wang (2026). Risk-Aware Multi-Objective Optimization for AI Infrastructure Supply Allocation under Demand and Supplier Uncertainty. World Scientific Research Journal, 12(10), 48-64. https://doi.org/10.6911/WSRJ.202610_12(10).0005

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