Quantum Monte Carlo methods for Newsvendor problem with Multiple Unreliable Suppliers

TL;DR

This paper introduces a quantum Monte Carlo approach combined with quantum amplitude estimation to improve inventory management under risk in supply chains with unreliable suppliers, offering faster probability estimations.

Contribution

It applies quantum algorithms to the newsvendor model, incorporating decision-makers' risk preferences, and demonstrates efficiency gains over classical methods in supply chain risk management.

Findings

Quantum Monte Carlo with QAE speeds up probability estimation.

Risk-aware decision-making impacts inventory strategies.

Enhanced supply chain resilience insights derived.

Abstract

In the post-pandemic world, manufacturing enterprises face increasing uncertainties, especially with vulnerabilities in global supply chains. Although supply chain management has been extensively studied, the critical influence of decision-makers (DMs) in these systems remains underexplored. This study studies the inventory management problem under risk using the newsvendor model by incorporating DMs risk preferences. By employing the Quantum Monte Carlo (QMC) combined with Quantum Amplitude Estimation (QAE) algorithm, the estimation of probabilities or expectation values can be done more efficiently. This offers near-quadratic speedup compared to classical Monte Carlo methods. Our findings illuminate the intricate relationship between risk-aware decision-making and inventory management, providing essential insights for enhancing supply chain resilience and adaptability in uncertain conditions