scholarly journals Evaluation of the Capacity of Risk Carriers by Means of Stochastic-Dynamic Programming

1981 ◽  
Vol 12 (1) ◽  
pp. 1-21 ◽  
Author(s):  
T. Pentikäinen ◽  
J. Rantala
Keyword(s):  
Dynamic Programming ◽  
Dynamic Model ◽  
Working Group ◽  
Stochastic Dynamic Programming ◽  
Numerical Data ◽  
Stochastic Dynamic ◽  
Political Interference ◽  
Near Future ◽  
Special Working ◽  
Special Working Group

The Ministry of Social Affairs and Health, being the Supervising Office of Insurance in Finland, has established a special working group to investigate the problems involved with the solvency of insurers. A report will be compiled in a near future. The capacity of risk carriers is one of the problems dealt with, and it will be preliminarily reviewed in this paper.The problem was treated by the working group parallelly by means of1. an empirical approach observing actual fluctuations in underwriting gains of insurers, and2. a theoretical approach, constructing a stochastic-dynamic model and studying its behaviour, especially its sensitivity to numerous background factors.First the methods of investigation are described and their application is then demonstrated using some numerical data. Because a comprehensive report will be published by the working group separately, only the main schedule is given. For the same reason the consideration is limited here to stochastic risks, omitting the fact that the solvency of an insurer is also jeopardized by numerous “non-stochastic” risks such as failure in investments, political interference of the authorities, mismanagement of the company, or misappropriation of its property.

scholarly journals Combined Aggregated Sampling Stochastic Dynamic Programming and Simulation-Optimization to Derive Operation Rules for Large-Scale Hydropower System

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 625
Author(s):  
Xinyu Wu ◽  
Rui Guo ◽  
Xilong Cheng ◽  
Chuntian Cheng
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Large Scale ◽  
Simulation Optimization ◽  
Optimal Operation ◽  
Stochastic Dynamic ◽  
Operation Model ◽  
Two Stage ◽  
Operation Rules ◽  
Reservoir Systems

Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interconnected variables need to be optimized, and the objective functions need to be computed through simulation in many periods. Since global solutions are seldom obtained, the initial solutions are important to the solution quality. In this paper, a two-stage method is proposed to derive operation rules for large-scale hydropower systems. In the first stage, the optimal operation model is simplified and solved using sampling stochastic dynamic programming (SSDP). In the second stage, the optimal operation model is solved by using a genetic algorithm, taking the SSDP solution as an individual in the initial population. The proposed method is applied to a hydropower system in Southwest China, composed of cascaded reservoir systems of Hongshui River, Lancang River, and Wu River. The numerical result shows that the two-stage method can significantly improve the solution in an acceptable solution time.

scholarly journals MDPtoolbox: a multi-platform toolbox to solve stochastic dynamic programming problems

Ecography ◽  
2014 ◽  
Vol 37 (9) ◽  
pp. 916-920 ◽  
Author(s):  
Iadine Chadès ◽  
Guillaume Chapron ◽  
Marie-Josée Cros ◽  
Frédérick Garcia ◽  
Régis Sabbadin
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Stochastic Dynamic

Analysis of Inventory Management in Reverse Supply Chain Using Stochastic Dynamic Programming Model

2008 ◽  
Author(s):  
Badr O. Johar ◽  
Surendra M. Gupta
Keyword(s):  
Dynamic Programming ◽  
Inventory Management ◽  
Reverse Logistics ◽  
Stochastic Dynamic Programming ◽  
Public Awareness ◽  
Financial Burden ◽  
Probabilistic Approach ◽  
Complex Nature ◽  
Stochastic Dynamic

Reverse logistics is a critical topic that has captured the attention of government, private entities and researchers in recent years. This increase in the concern was driven by current set of government regulations, increase of public awareness, and the attractive economic opportunities. Also, environmentalists have always demanded Original Equipment Manufacturers (OEMs) to be more involved and be responsible of their products at the end of its life cycle. However, the uncertainty in quality of items returned, and its quantity discourage OEMs from participating in such programs. Because of the unique problems associated and the complex nature of the reverse logistics activities, numerous studies have been carried out in this field. One of those crucial areas is inventory management of End-of-Life (EOL) products. The take back program could possibly bring financial burden to OEM if it is not managed well. Thus, an efficient yet cost effective system should be implemented to appropriately manage the overwhelming number of returns. Previously, we have analyzed the problem based on the assumption that the number of core products returned and disassembled parts and subassemblies are known in advance. In this paper, we introduce a probabilistic approach where different quality levels of for every component disassembled are considered and different probabilities of these qualities given the quality of the returned product. The model utilizes a multi-period stochastic dynamic programming in a disassembly line context to solve the problem, and generate the best option that will maximize the system total profit. A numerical example is given to illustrate the approach. Finally, directions for future research are suggested.

Limits to stochastic dynamic programming

1991 ◽  
Vol 14 (1) ◽  
pp. 101-101
Author(s):  
Ruth H. Mace ◽  
William J. Sutherland
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Stochastic Dynamic
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