scholarly journals The Loss-Averse Newsvendor Problem with Random Yield and Reference Dependence

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1231
Author(s):  
Wei Liu ◽  
Shiji Song ◽  
Ying Qiao ◽  
Han Zhao ◽  
Huachang Wang
Keyword(s):  
Loss Aversion ◽  
Reference Point ◽  
Newsvendor Problem ◽  
Optimal Order ◽  
Reference Dependence ◽  
Random Yield ◽  
Order Quantity ◽  
Ordering Policy ◽  
Optimal Ordering Policy ◽  
Optimal Ordering

This paper studies a loss-averse newsvendor problem with reference dependence, where both demand and yield rate are stochastic. We obtain the loss-averse newsvendor’s optimal ordering policy and analyze the effects of loss aversion, reference dependence, random demand and yield on it. It is shown that the loss-averse newsvendor’s optimal order quantity and expected utility decreases in loss aversion level and reference point. Then, that this order quantity may be larger than the risk-neutral one’s if the reference point is less than a negative threshold. In addition, although the effect of random yield leads to an increase in the order quantity, the loss-averse newsvendor may order more than, equal to or less than the classical one, which significantly depends on loss aversion level and reference point. Numerical experiments were conducted to demonstrate our theoretical results.

Retailer's Optimal Ordering Policy under Conditions of Allowable Shortage and Two-Level Trade Credit Derived without Derivatives

2013 ◽  
Vol 694-697 ◽  
pp. 3428-3433
Author(s):  
Fei Hu
Keyword(s):  
Differential Calculus ◽  
Trade Credit ◽  
Algebraic Method ◽  
Optimal Order ◽  
Order Quantity ◽  
Numerical Examples ◽  
Ordering Policy ◽  
Optimal Order Quantity ◽  
Optimal Ordering Policy ◽  
Optimal Ordering

An inventory model was developed to determine an ordering policy for the retailer under conditions of allowable shortage and two levels of delay permitted. We present a simple algebraic method to replace the use of differential calculus for determining the retailer's optimal ordering policy. A theorem is presented to obtain the optimal order quantity, and numerical examples are given to illustrate the results obtained in this paper.

Optimal Ordering Policy With Inventory Classification Using Data Mining Techniques

2018 ◽  
pp. 305-326 ◽  
Author(s):  
Reshu Agarwal ◽  
Mandeep Mittal
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Optimal Order ◽  
Order Quantity ◽  
Data Mining Techniques ◽  
Ordering Policy ◽  
Frequent Items ◽  
Optimal Ordering Policy ◽  
Using Data ◽  
Research Studies

Data mining is a technique to identify valid novel, potentially useful, and understandable correlations and patterns in existing data. Data mining techniques, such as clustering, association rule mining, classification, and sequential pattern mining, have attracted a great deal of attention in the information industry and in society as a whole in recent years. Some research studies have also extended the usage of this concept in inventory management. Yet, not many research studies have considered the application of data mining approach on determining both optimal order quantity and loss profit of frequent items. This helps inventory manager to determine optimum order quantity of frequent items together with the most profitable item for optimal inventory control. In this chapter, two different cases for determining ordering policy and inventory classification based on loss rule are presented. An example is illustrated to validate the results.

scholarly journals Coordination of a Random Yield Supply Chain with a Loss-Averse Supplier

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Jiarong Luo ◽  
Xu Chen
Keyword(s):  
Supply Chain ◽  
Loss Aversion ◽  
Optimal Order ◽  
Random Yield ◽  
Order Quantity ◽  
Optimal Production ◽  
Numerical Examples ◽  
Risk Neutral ◽  
Production Quantity ◽  
Optimal Order Quantity

This paper investigates the coordination of a supply chain consisting of a loss-averse supplier and a risk-neutral buyer who orders products from the supplier who suffers from random yield to meet a deterministic demand. We derive the risk-neutral buyer’s optimal order policy and the loss-averse supplier’s optimal production policy under shortage-penalty-surplus-subsidy (SPSS) contracts. We also analyze the impacts of loss aversion on the loss-averse supplier’s production decision making and find that the loss-averse supplier may produce less than, equal to, or more than the risk-neutral supplier. Then, we provide explicit conditions on which the random yield supply chain with a loss-averse supplier can be coordinated under SPSS contracts. Finally, adopting numerical examples, we find that when the shortage penalty is low, the buyer’s optimal order quantity will increase, while the supplier’s optimal production quantity will first decrease and then increase as the loss aversion level increases. When the shortage penalty is high, the buyer’s optimal order quantity will decrease but the supplier’s optimal production quantity will always increase as the loss aversion level increases. Furthermore, the numerical examples provide strong evidence for the view that SPSS contracts can effectively improve the performance of the whole supply chain.

scholarly journals Coping with Loss Aversion in the Newsvendor Model

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Jianwu Sun ◽  
Xinsheng Xu
Keyword(s):  
Expected Utility ◽  
Loss Aversion ◽  
Confidence Level ◽  
Profit Maximization ◽  
Optimal Order ◽  
Market Demand ◽  
Newsvendor Model ◽  
Order Quantity ◽  
Decision Bias ◽  
Optimal Order Quantity

We introduce loss aversion into the decision framework of the newsvendor model. By introducing the loss aversion coefficientλ, we propose a novel utility function for the loss-averse newsvendor. First, we obtain the optimal order quantity to maximize the expected utility for the loss-averse newsvendor who is risk-neutral. It is found that this optimal order quantity is smaller than the expected profit maximization order quantity in the classical newsvendor model, which may help to explain the decision bias in the classical newsvendor model. Then, to reduce the risk which originates from the fluctuation in the market demand, we achieve the optimal order quantity to maximize CVaR about utility for the loss-averse newsvendor who is risk-averse. We find that this optimal order quantity is smaller than the optimal order quantity to maximize the expected utility above and is decreasing in the confidence levelα. Further, it is proved that the expected utility under this optimal order quantity is decreasing in the confidence levelα, which verifies that low risk implies low return. Finally, a numerical example is given to illustrate the obtained results and some management insights are suggested for the loss-averse newsvendor model.

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