#### Title

Applying Deep Learning to the Ice Cream Vendor Problem: An Extension of the Newsvendor Problem

#### Degree Name

MS (Master of Science)

#### Program

Mathematical Sciences

#### Date of Award

8-2021

#### Committee Chair or Co-Chairs

Jeff Knisley

#### Committee Members

Michele Joyner, JeanMarie Hendrickson

#### Abstract

The Newsvendor problem is a classical supply chain problem used to develop strategies for inventory optimization. The goal of the newsvendor problem is to predict the optimal order quantity of a product to meet an uncertain demand in the future, given that the demand distribution itself is known. The Ice Cream Vendor Problem extends the classical newsvendor problem to an uncertain demand with unknown distribution, albeit a distribution that is known to depend on exogenous features. The goal is thus to estimate the order quantity that minimizes the total cost when demand does not follow any known statistical distribution. The problem is formulated as a mathematical programming problem and solved using a Deep Neural network approach. The feature-dependent demand data used to train and test the deep neural network is produced by a discrete event simulation based on actual daily temperature data, among other features.

#### Document Type

Thesis - unrestricted

#### Recommended Citation

Solihu, Gaffar, "Applying Deep Learning to the Ice Cream Vendor Problem: An Extension of the Newsvendor Problem" (2021). *Electronic Theses and Dissertations.* Paper 3945. https://dc.etsu.edu/etd/3945

#### Copyright

Copyright by the authors.

#### Included in

Applied Mathematics Commons, Artificial Intelligence and Robotics Commons, Data Science Commons, Mathematics Commons, Statistics and Probability Commons