1 code implementation • 10 Jun 2020 • Mohammad Pirhooshyaran, Lawrence V. Snyder
We propose a framework that uses deep neural networks (DNN) to optimize inventory decisions in complex multi-echelon supply chains.
no code implementations • 31 Dec 2019 • Ehsan Khodabandeh, Lawrence V. Snyder, John Dennis, Joshua Hammond, Cody Wanless
We consider a wide family of vehicle routing problem variants with many complex and practical constraints, known as rich vehicle routing problems, which are faced on a daily basis by C. H.
1 code implementation • 12 Sep 2019 • Mohammad Pirhooshyaran, Katya Scheinberg, Lawrence V. Snyder
This study introduces a framework for the forecasting, reconstruction and feature engineering of multivariate processes along with its renewable energy applications.
1 code implementation • 1 Jun 2019 • Mohammad Pirhooshyaran, Lawrence V. Snyder
For the case of significant wave height reconstruction, we compare the proposed methods with alternatives on a well-studied dataset.
no code implementations • 30 May 2019 • Mohammadreza Nazari, Majid Jahani, Lawrence V. Snyder, Martin Takáč
Therefore, we propose a student-teacher RL mechanism in which the RL (the "student") learns to maximize its reward, subject to a constraint that bounds the difference between the RL policy and the "teacher" policy.
4 code implementations • NeurIPS 2018 • Mohammadreza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takáč
Our model represents a parameterized stochastic policy, and by applying a policy gradient algorithm to optimize its parameters, the trained model produces the solution as a sequence of consecutive actions in real time, without the need to re-train for every new problem instance.