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2 days ago · This study enhances existing frameworks with three key propositions, integrating deep learning and optimization to create a three-step revenue optimization ...
Missing: Optimising Stochastic
4 days ago · In this paper, we analyze the merits of reactive route reoptimization and anticipation for a dynamic vehicle routing problem with stochastic requests. To this ...
Jun 29, 2024 · The framework addresses both offline planning based on stochastic demand distributions and cancellations, as well as online routing considering real-time orders ...
Jun 21, 2024 · We propose a three-phase decision making model that efficiently assigns vehicles to passengers, determines flight levels to reduce collision risk, and resolves ...
Missing: Optimising | Show results with:Optimising
Jun 14, 2024 · Therefore, we establish a trust framework that provides a better understanding of trust and its concerns in the context of PI. This paper aims to reason about ...
Jun 11, 2024 · This study proposes an innovative model-based modular approach (MMA) to dynamically optimize order matching and vehicle relocation in a ride-hailing platform.
Missing: Optimising | Show results with:Optimising
Jun 28, 2024 · Specifically, in sequential decision-making problems, a RL approach incorporates uncertainty into the learning process by modeling the environment as a Markov ...
Missing: Optimising Stochastic
6 days ago · programming library for parametric model-based optimal control, Gekko provides optimization and machine learning methods for rigorous nonlinear model.
5 days ago · ... routing, matching and dispatching of micro-delivery fleet. https ... Machine Learning and Behavioural/Enocometric models can tackle complex problems.
Jun 18, 2024 · Recently, with Markov decision processes (MDPs) and reinforcement learning ... Our model captures the stochastic dynamics of the marketplace, the pricing ...