A comparison of Monte Carlo tree search and rolling horizon optimization for large-scale dynamic resource allocation problems

DOI10.1016/j.ejor.2017.05.032zbMath1380.91089arXiv1405.5498OpenAlexW2614119990MaRDI QIDQ1694949

J. Daniel Griffith, Velibor V. Mišić, Mykel J. Kochenderfer, Vishal Gupta, Dimitris J. Bertsimas

Publication date: 6 February 2018

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1405.5498

zbMATH Keywords

dynamic resource allocationMonte Carlo tree searchqueueing controlrolling horizon optimizationwildfire management

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Stochastic programming (90C15) Approximation methods and heuristics in mathematical programming (90C59) Queues and service in operations research (90B22) Resource and cost allocation (including fair division, apportionment, etc.) (91B32)

Related Items (6)

Optimal production ramp‐up in the smartphone manufacturing industry ⋮ Optimistic Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds ⋮ A robust decision-support method based on optimization and simulation for wildfire resilience in highly renewable power systems ⋮ Modelling and solving resource allocation problems via a dynamic programming approach ⋮ An approximate dynamic programming approach to project scheduling with uncertain resource availabilities ⋮ Allocating resources via price management systems: a dynamic programming-based approach

Uses Software

• Gurobi
• Prometheus
• Algorithm 97

Cites Work

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• Safety Stock versus Safety Time in MRP Controlled Production Systems
• Introduction to Queueing
• Finite-time analysis of the multiarmed bandit problem

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