A practical end-to-end inventory management model with deep learning
We investigate a data-driven multiperiod inventory replenishment problem with uncertain
demand and vendor lead time (VLT) with accessibility to a large quantity of historical data …
demand and vendor lead time (VLT) with accessibility to a large quantity of historical data …
[HTML][HTML] A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality
Y Tadayonrad, AB Ndiaye - Supply Chain Analytics, 2023 - Elsevier
Forecasting demand and determining safety stocks are key aspects of supply chain
planning. Demand forecasting involves predicting future demand for a product or service …
planning. Demand forecasting involves predicting future demand for a product or service …
Vcnet and functional targeted regularization for learning causal effects of continuous treatments
Motivated by the rising abundance of observational data with continuous treatments, we
investigate the problem of estimating the average dose-response curve (ADRF). Available …
investigate the problem of estimating the average dose-response curve (ADRF). Available …
Conditional density estimation with neural networks: Best practices and benchmarks
J Rothfuss, F Ferreira, S Walther, M Ulrich - arXiv preprint arXiv …, 2019 - arxiv.org
Given a set of empirical observations, conditional density estimation aims to capture the
statistical relationship between a conditional variable $\mathbf {x} $ and a dependent …
statistical relationship between a conditional variable $\mathbf {x} $ and a dependent …
On the representation and learning of monotone triangular transport maps
Transportation of measure provides a versatile approach for modeling complex probability
distributions, with applications in density estimation, Bayesian inference, generative …
distributions, with applications in density estimation, Bayesian inference, generative …
[HTML][HTML] Statistical postprocessing of wind speed forecasts using convolutional neural networks
Current statistical postprocessing methods for probabilistic weather forecasting are not
capable of using full spatial patterns from the numerical weather prediction (NWP) model. In …
capable of using full spatial patterns from the numerical weather prediction (NWP) model. In …
Efficient neural network approaches for conditional optimal transport with applications in bayesian inference
We present two neural network approaches that approximate the solutions of static and
dynamic conditional optimal transport (COT) problems, respectively. Both approaches …
dynamic conditional optimal transport (COT) problems, respectively. Both approaches …
Bayesian dependent mixture models: A predictive comparison and survey
For exchangeable data, mixture models are an extremely useful tool for density estimation
due to their attractive balance between smoothness and flexibility. When additional …
due to their attractive balance between smoothness and flexibility. When additional …
Modelling stochastic behaviour in simulation digital twins through neural nets
S Reed, M Löfstrand, J Andrews - Journal of Simulation, 2022 - Taylor & Francis
In discrete event simulation (DES) models, stochastic behaviour is modelled by sampling
random variates from probability distributions to determine event outcomes. However, the …
random variates from probability distributions to determine event outcomes. However, the …
Noise regularization for conditional density estimation
Modelling statistical relationships beyond the conditional mean is crucial in many settings.
Conditional density estimation (CDE) aims to learn the full conditional probability density …
Conditional density estimation (CDE) aims to learn the full conditional probability density …