A rollout algorithm framework for heuristic solutions to finite-horizon stochastic dynamic programs
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Publication:1698902
DOI10.1016/j.ejor.2016.09.040zbMath1380.90276OpenAlexW2528374652MaRDI QIDQ1698902
Justin C. Goodson, Jeffrey W. Ohlmann, Barrett W. Thomas
Publication date: 16 February 2018
Published in: European Journal of Operational Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ejor.2016.09.040
Stochastic programming (90C15) Approximation methods and heuristics in mathematical programming (90C59) Dynamic programming (90C39)
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Cites Work
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- Minimum and worst-case performance ratios of rollout algorithms
- A rollout metaheuristic for job shop scheduling problems
- Parallelization strategies for rollout algorithms
- Looking ahead with the pilot method
- Hybrid rollout approaches for the job shop scheduling problem
- An approximate dynamic programming approach for the vehicle routing problem with stochastic demands
- Analysis of a rollout approach to sequencing problems with stochastic routing applications
- Comparing neuro-dynamic programming algorithms for the vehicle routing problem with stochastic demands
- Rollout algorithms for stochastic scheduling problems
- Simulation-based algorithms for Markov decision processes
- Average-case performance of rollout algorithms for knapsack problems
- Dynamic programming and suboptimal control: a survey from ADP to MPC
- Rollout algorithms for combinatorial optimization
- The pilot method: A strategy for heuristic repetition with application to the Steiner problem in graphs
- Approximate Dynamic Programming
- An Approximate Dynamic Programming Approach to Multidimensional Knapsack Problems
- A Rollout Policy for the Vehicle Routing Problem with Stochastic Demands
- The Dynamic and Stochastic Knapsack Problem with Deadlines
- New Rollout Algorithms for Combinatorial Optimization Problems
- Computational Approaches to Stochastic Vehicle Routing Problems
- A branch‐and‐regret heuristic for stochastic and dynamic vehicle routing problems
- Rollout Policies for Dynamic Solutions to the Multivehicle Routing Problem with Stochastic Demand and Duration Limits