Mean, variance, and probabilistic criteria in finite Markov decision processes: a review
Journal of Optimization Theory and Applications
Variance-penalized Markov decision processes
Mathematics of Operations Research
Inventory control with an exponential utility criterion
Operations Research
Variability sensitive Markov decision processes
Mathematics of Operations Research
The risk-averse (and prudent) newsboy
Management Science
Impact of Uncertainty and Risk Aversion on Price and Order Quantity in the Newsvendor Problem
Manufacturing & Service Operations Management
Risk-Sensitive Optimal Control for Markov Decision Processes with Monotone Cost
Mathematics of Operations Research
The Benefits of Advance Booking Discount Programs: Model and Analysis
Management Science
The Effects of Financial Risks on Inventory Policy
Management Science
Firefighter Staffing Including Temporary Absences and Wastage
Operations Research
Optimal Lot Sizing Policies For Sequential Online Auctions
IEEE Transactions on Knowledge and Data Engineering
Risk Aversion in Inventory Management
Operations Research
Inventory Management of a Fast-Fashion Retail Network
Operations Research
Risk-averse dynamic programming for Markov decision processes
Mathematical Programming: Series A and B - 20th International Symposium on Mathematical Programming – ISMP 2009
A production-inventory model of imperfect quality products in a three-layer supply chain
Decision Support Systems
Mean–Variance Analysis for the Newsvendor Problem
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On a time consistency concept in risk averse multistage stochastic programming
Operations Research Letters
Optimization of a stochastic remanufacturing network with an exchange option
Decision Support Systems
A decision support system for procurement risk management in the presence of spot market
Decision Support Systems
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Traditionally inventory management models have focused on risk-neutral decision making with the objective of maximizing the expected rewards or minimizing costs over a specified time horizon. However, for items marked by high demand volatility such as fashion goods and technology products, this objective needs to be balanced against the risk associated with the decision. Depending on how the product performs vis-a-vis the seller's original forecast, the seller could end up with losses due to either short or surplus supply. Unfortunately, traditional models do not address this issue. Stochastic dynamic programming models have been extensively used for sequential decision making in the context of multi-period inventory management, but in the traditional way where one either minimizes costs or maximizes profits. Risk is implicitly considered by accounting for stock-out costs. Considering risk and reward simultaneously and explicitly in a stochastic dynamic setting is a cumbersome task and often difficult to implement for practical purposes, since dynamic programming is designed to optimize on one variable, not two. In this paper we develop an algorithm, Variance-Retentive Stochastic Dynamic Programming that tracks variance as well as expected reward in a stochastic dynamic programming model for inventory control. We use the mean-variance solutions in a heuristic, RiskTrackr, to construct efficient frontiers which could be an ideal decision support tool for risk-reward analysis.