Bicriteria Optimization In the Newsvendor Problem with Exponentially Distributed Demand

2016 ◽  
Vol 11 ◽  
pp. 20-35
Author(s):  
Milena Bieniek ◽  
Keyword(s):  
Newsvendor Problem ◽  
Bicriteria Optimization

Bicriteria optimization in the newsvendor problem with uniformly distributed demand

4OR ◽  
2012 ◽  
Vol 10 (3) ◽  
pp. 267-286 ◽  
Author(s):  
F. J. Arcelus ◽  
Satyendra Kumar ◽  
G. Srinivasan
Keyword(s):  
Newsvendor Problem ◽  
Bicriteria Optimization ◽  
Uniformly Distributed Demand

Loss-averse newsvendor problem with supply risk

2016 ◽  
Vol 67 (2) ◽  
pp. 214-228 ◽  
Author(s):  
Lijun Ma ◽  
Weili Xue ◽  
Yingxue Zhao ◽  
Qinghua Zeng
Keyword(s):  
Newsvendor Problem ◽  
Supply Risk

The risk-averse newsvendor problem with random capacity

2013 ◽  
Vol 231 (2) ◽  
pp. 328-336 ◽  
Author(s):  
Meng Wu ◽  
Stuart X. Zhu ◽  
Ruud H. Teunter
Keyword(s):  
Newsvendor Problem ◽  
Risk Averse ◽  
Random Capacity

A Monge Sequence-Based Approach to Characterize the Competitive Newsvendor Problem

2021 ◽  
Author(s):  
Saurabh Bansal ◽  
Mahesh Nagarajan
Keyword(s):  
Structural Properties ◽  
Transportation Problem ◽  
Cash Flows ◽  
Newsvendor Problem ◽  
Multiple Agents ◽  
Optimization Framework ◽  
Unmet Demand ◽  
Game Theoretic

Replicating cash flows of multiple agents in game-theoretic settings tends to be a challenging task. In this paper, we consider the competitive newsvendor game where multiple newsvendors choose inventory levels before demand arrival and the unmet demand of each newsvendor spills over to multiple other newsvendors. We show that this spillover behavior and the resulting cash flows of each newsvendor can be replicated within a transportation problem after assigning artificial costs on spillover behavior. This replication provides an opportunity to study structural properties of the problem, as well as determine the equilibrium of the game. This paradigm of using artificial costs within an optimization framework to replicate agents’ cash flows can be used in many other games as well.

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