Mixed contracts for the newsvendor problem with real options

In this paper we consider the newsvendor model with real options. We consider a mixed contract where the retailer can order a combination of q units subject to the conditions in a classical newsvendor contract and Q real options on the same items. We provide a closed form solution to this mixed contract when the demand is discrete and study some of its properties. We also offer an explicit solution for the continuous case. In particular we demonstrate that a mixed contract may be superior to a real option contract when a manufacturer has a bound on how much variance she is willing to accept.Newsvendor model; real options; discrete demand; mixed contract

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

Finding and identifying optimal inventory levels for systems with common components

In this article, we consider the problem of finding the optimal inventory level for components in an assembly system where multiple products share common components in the presence of random demand. Previously, solution procedures that identify the optimal inventory levels for components in a component commonality problem have been considered for two product or one common component systems. We will here extend this to a three products system considering any number of common components. The inventory problem considered is modeled as a two stage stochastic recourse problem where the first stage is to set the inventory levels to maximize expected profit while the second stage is to allocate components to products after observing demand. Our main contribution, and the main focus of this paper, is the outline of a procedure that finds the gradient for the stochastic problem, such that an optimal solution can be identified and a gradient based search method can be used to find the optimal solution.Inventory Stochastic programming Linear programming

An improved decision support model for scheduling production in an engineer-to-order manufacturer

International audienc

Transfer of risk in the newsvendor model with discrete demand

In this paper we consider the transfer of risk in a newsvendor model with discrete demand. We view the newsvendor model as a leader/follower problem where the manufacturer (leader) decides the wholesale price and the retailer (follower) decides the quantity ordered. Taking a Pareto-optimal contract as a starting point, the manufacturer wishes to design a real option contract to enhance profits. A new real option contract is said to be feasible if both parties' expected profit is at least as great as in the original contract. When demand is discrete, there are usually infinite feasible contracts that yield maximum expected profits to the manufacturer. In the paper we show that either all, some or none of these real option contracts offer an improved position for the retailer

Transfer of risk in the newsvendor model with discrete demand

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Omega : The International Journal of Management Science, following peer review. The final publication Omega : The International Journal of Management Science 2012, 40(3):404-414 is available at Elsevier via DOI: 10.1016/j.omega.2011.07.001 .In this paper we consider the transfer of risk in a newsvendor model with discrete demand. We view the newsvendor model as a leader/follower problem where the manufacturer (leader) decides the wholesale price and the retailer (follower) decides the quantity ordered. Taking a Pareto-optimal contract as a starting point, the manufacturer wishes to design a real option contract to enhance profits. A new real option contract is said to be feasible if both parties' expected profit is at least as great as in the original contract. When demand is discrete, there are usually infinite feasible contracts that yield maximum expected profits to the manufacturer. In the paper we show that either all, some or none of these real option contracts offer an improved position for the retailer

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

A maximum entropy approach to the newsvendor problem with partial information

“NOTICE: this is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research 2013, 228(1):190-200,doi:10.1016/j.ejor.2013.01.031¨ Copyright © 2013 Elsevier B.V. All rights reservedIn 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 result