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Found 5 papers, 1 papers with code
Interactive Multi Interest Process Pattern Discovery
Process pattern discovery methods (PPDMs) aim at identifying patterns of interest to users.
Action-Evolution Petri Nets: a Framework for Modeling and Solving Dynamic Task Assignment Problems
While there exist separate formalisms, specifically Markov Decision Processes and (Colored) Petri Nets, to model, execute, and solve different aspects of the problem, there is no integrated modeling technique.
Online Multimodal Transportation Planning using Deep Reinforcement Learning
In this paper we propose a Deep Reinforcement Learning approach to solve a multimodal transportation planning problem, in which containers must be assigned to a truck or to trains that will transport them to their destination.
Deep Controlled Learning for Inventory Control
Problem Definition: Are traditional deep reinforcement learning (DRL) algorithms, developed for a broad range of purposes including game-play and robotics, the most suitable machine learning algorithms for applications in inventory control?
Solving the Order Batching and Sequencing Problem using Deep Reinforcement Learning
In this paper, we present a Deep Reinforcement Learning (DRL) approach for deciding how and when orders should be batched and picked in a warehouse to minimize the number of tardy orders.
