8 citations · 9 across the 6 of their papers we have counts for
8 papers · 1 filter
Combinatorial Optimization and Machine Learning for Dynamic Inventory Routing
Toni Greif, Louis Bouvier, Christoph M. Flath +3
We introduce a combinatorial optimization-enriched machine learning pipeline and a novel learning paradigm to solve inventory routing problems with stochastic demand and dynamic in…
Future memories are not needed for large classes of POMDPs
Victor Cohen, Axel Parmentier
Optimal policies for partially observed Markov decision processes (POMDPs) are history-dependent: Decisions are made based on the entire history of observation. Memoryless policies…
Learning to solve the single machine scheduling problem with release times and sum of completion times
Axel Parmentier, Vincent T'Kindt
In this paper, we focus on the solution of a hard single machine scheduling problem by new heuristic algorithms embedding techniques from machine learning field and scheduling theo…
Integer programming for weakly coupled stochastic dynamic programs with partial information
Victor Cohen, Axel Parmentier
This paper introduces algorithms for problems where a decision maker has to control a system composed of several components and has access to only partial information on the state…
Integer programming on the junction tree polytope for influence diagrams
Axel Parmentier, Victor Cohen, Vincent Leclère +2
Influence Diagrams (ID) are a flexible tool to represent discrete stochastic optimization problems, including Markov Decision Process (MDP) and Partially Observable MDP as standard…
Linear Programming for Decision Processes with Partial Information
Victor Cohen, Axel Parmentier
Markov Decision Processes (MDPs) are stochastic optimization problems that model situations where a decision maker controls a system based on its state. Partially observed Markov d…