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

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

Initial inventory levels for a book publishing firm = Kezdőkészletek egy könyvkiadó vállalatnál

Egy könyvkiadó vállalatot vizsgálunk. A kiadó kiadványait a szokásos értékesítési láncon (kis- és nagykereskedelem) keresztül értékesíti. A kérdés az, hogy egy új könyv példányait hogyan allokálja az értékesítési láncban. Feltételezzük, hogy a kereslet ismert, Poisson-eloszlású. A készletezés költségeit szintén ismertnek tételezzük fel. Cél a költségek minimalizálása. = The aim of the paper is to analyze a practical real world problem. A publishing house is given. The publishing firm has contacts to a number of wholesaler / retailer enterprises and direct contact to customers to satisfy the market demand. The book publishers work in a project industry. The publisher faces with the problem to allocate the stocks of a given, newly published book to the wholesaler and retailer, and to hold some copies to satisfy the customers direct from the publisher. The distribution of the demand is unknown, but it can be estimated. The costs consist of inventory holding and shortage, backorder costs. The decision maker wants to minimize these relevant costs. The problem can be modeled as a one-warehouse and N-retailer supply chain with not identical demand distribution. The problem structure is similar that of a newsvendor model. It is assumed that the demand distribution follows a Poisson distribution

A maximum entropy approach to the newsvendor problem with partial information

In this paper, we consider the newsvendor model under partial information, i.e., where the demand distribution D is partly unknown. We focus on the classical case where the retailer only knows the expectation and variance of D. The standard approach is then to determine the order quantity using conservative rules such as minimax regret or Scarf's rule. We compute instead the most likely demand distribution in the sense of maximum entropy. We then compare the performance of the maximum entropy approach with minimax regret and Scarf's rule on large samples of randomly drawn demand distributions. We show that the average performance of the maximum entropy approach is considerably better than either alternative, and more surprisingly, that it is in most cases a better hedge against bad results.Newsvendor model; entropy; partial information