Stochastic Dynamic Programming Solution of a Risk-Adjusted Disaster Preparedness and Relief Distribution Problem

2016 ◽  
pp. 9-15
Author(s):  
Ebru Angün
Keyword(s):  
Dynamic Programming ◽  
Disaster Preparedness ◽  
Stochastic Dynamic Programming ◽  
Stochastic Dynamic ◽  
Distribution Problem ◽  
Relief Distribution ◽  
Programming Solution ◽  
Dynamic Programming Solution

scholarly journals Optimal Exploitation of Multiple Stocks by a Common Fishery: A New Methodology

1976 ◽  
Vol 33 (1) ◽  
pp. 1-5 ◽  
Author(s):  
R. Hilborn
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Stochastic Dynamic ◽  
Recruitment Curve ◽  
Optimal Exploitation ◽  
Optimal Harvest ◽  
Stock Recruitment ◽  
Programming Solution ◽  
Parameter Values ◽  
Dynamic Programming Solution

Optimal harvest rates for mixed stocks of fish are calculated using stochastic dynamic programming. This technique is shown to be superior to the best methods currently described in the literature. The Ricker stock recruitment curve is assumed for two stocks harvested by the same fishery. The optimal harvest rates are calculated as a function of the size of each stock, for a series of possible parameter values. The dynamic programming solution is similar to the fixed escapement policy only when the two stocks have similar Ricker parameters, or when the two stocks are of equal size. Normally, one should harvest harder than calculated from fixed escapement analysis.

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.

Stochastic Dynamic Programming with Localized Cost-to-go Approximators

2005 ◽  
Vol 83 (6) ◽  
pp. 752-758 ◽  
Author(s):  
J. Choi ◽  
M.J. Realff ◽  
J.H. Lee
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Stochastic Dynamic

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.

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