On Queues with a Random Capacity: Some Theory, and an Application

2019 ◽  
pp. 279-316 ◽  
Author(s):  
Rouba Ibrahim
Keyword(s):  
Random Capacity

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

ON COURNOT COMPETITION UNDER RANDOM YIELD

2013 ◽  
Vol 30 (04) ◽  
pp. 1350007 ◽  
Author(s):  
XIAOMING YAN ◽  
YONG WANG
Keyword(s):  
Nash Equilibrium ◽  
Production Cost ◽  
Pure Strategy ◽  
Cournot Competition ◽  
Random Yield ◽  
Cournot Model ◽  
Random Capacity ◽  
Strategy Nash Equilibrium

We look at a Cournot model in which each firm may be unreliable with random capacity, so the total quantity brought into market is uncertain. The Cournot model has a unique pure strategy Nash equilibrium (NE), in which the number of active firms is determined by each firm's production cost and reliability. Our results indicate the following effects of unreliability: the number of active firms in the NE is more than that each firm is completely reliable and the expected total quantity brought into market is less than that each firm is completely reliable. Whether a given firm joins in the game is independent of its reliability, but any given firm always hopes that the less-expensive firms' capacities are random and stochastically smaller.

scholarly journals Managing the Newsvendor Modeled Product System with Random Capacity and Capacity-Dependent Price

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Qingying Li ◽  
Ciwei Dong ◽  
Ruixin Zhuang
Keyword(s):  
Numerical Study ◽  
Market Price ◽  
Optimal Order ◽  
Selling Price ◽  
Newsvendor Model ◽  
Order Quantity ◽  
Product System ◽  
Random Capacity ◽  
Optimal Order Quantity ◽  
House Production

We consider a newsvendor modeled product system, where the firm provides products to the market. The supply capacity of the product is random, so the firm receives either the amount of order quantity or the realized capacity, whichever is smaller. The market price is capacity dependent. We consider two types of production cost structures: the procurement case and the in-house production case. The firm pays for the received quantity in the former case and for the ordered quantity in the latter case. We obtain the optimal order quantities for both cases. Comparing with the traditional newsvendor model, we find that the optimal order quantity in both the procurement case and the in-house production case are no greater than that in the traditional newsvendor model with a fixed selling price. We also find that the optimal order quantity for the procurement case is greater than that for the in-house production case. Numerical study is conducted to investigate the sensitivity of the optimal solution versus the distribution of the random capacity/demand.

Role of Random Capacity Risk and the Retailer in Decentralized Supply Chains with Competing Suppliers

2014 ◽  
Vol 45 (2) ◽  
pp. 255-279 ◽  
Author(s):  
Fei Qin ◽  
Uday S. Rao ◽  
Haresh Gurnani ◽  
Ramesh Bollapragada
Keyword(s):  
Supply Chains ◽  
Random Capacity

Supply process improvement decisions for a newsvendor with random capacity

2020 ◽  
Vol 141 ◽  
pp. 106289 ◽  
Author(s):  
Zhenyang Shi ◽  
Bo Li ◽  
Shaoxuan Liu
Keyword(s):  
Process Improvement ◽  
Random Capacity

scholarly journals Data-Driven Models for Capacity Allocation of Inpatient Beds in a Chinese Public Hospital

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Ting Zhu ◽  
Peng Liao ◽  
Li Luo ◽  
Heng-Qing Ye
Keyword(s):  
Optimal Allocation ◽  
Limited Resource ◽  
Optimization Method ◽  
Capacity Allocation ◽  
Substantial Improvement ◽  
Public Hospitals ◽  
Data Driven ◽  
Random Capacity ◽  
Bed Capacity ◽  
Elective Patients

Hospital beds are a critical but limited resource shared between distinct classes of elective patients. Urgent elective patients are more sensitive to delays and should be treated immediately, whereas regular patients can wait for an extended time. Public hospitals in countries like China need to maximize their revenue and at the same time equitably allocate their limited bed capacity between distinct patient classes. Consequently, hospital bed managers are under great pressure to optimally allocate the available bed capacity to all classes of patients, particularly considering random patient arrivals and the length of patient stay. To address the difficulties, we propose data-driven stochastic optimization models that can directly utilize historical observations and feature data of capacity and demand. First, we propose a single-period model assuming known capacity; since it recovers and improves the current decision-making process, it may be deployed immediately. We develop a nonparametric kernel optimization method and demonstrate that an optimal allocation can be effectively obtained with one year’s data. Next, we consider the dynamic transition of system state and extend the study to a multiperiod model that allows random capacity; this further brings in substantial improvement. Sensitivity analysis also offers interesting managerial insights. For example, it is optimal to allocate more beds to urgent patients on Mondays and Thursdays than on other weekdays; this is in sharp contrast to the current myopic practice.

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