Simultaneous Decision Making for Stochastic Multi-echelon Inventory Optimization with Deep Neural Networks as Decision Makers

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.

Decision Making

C. H. Robinson Uses Heuristics to Solve Rich Vehicle Routing Problems

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.

Feature Engineering and Forecasting via Derivative-free Optimization and Ensemble of Sequence-to-sequence Networks with Applications in Renewable Energy

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.

Feature Engineering feature selection

Multivariate, Multistep Forecasting, Reconstruction and Feature Selection of Ocean Waves via Recurrent and Sequence-to-Sequence Networks

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.

feature selection Hyperparameter Optimization

Don't Forget Your Teacher: A Corrective Reinforcement Learning Framework

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.

reinforcement-learning Reinforcement Learning (RL) +1

Reinforcement Learning for Solving the Vehicle Routing Problem

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.

Combinatorial Optimization reinforcement-learning +1