Management Strategies for Controlling Soybean Cyst Nematode: An Application of Stochastic Dynamic Programming

1986 ◽  
Vol 8 (2) ◽  
pp. 175 ◽  
Author(s):  
Thomas P. Zacharias ◽  
Judith S. Liebman ◽  
Gregory R. Noel
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Soybean Cyst Nematode ◽  
Management Strategies ◽  
Cyst Nematode ◽  
Stochastic Dynamic

scholarly journals Dynamic Programming, An Approach for Analysing Competition Strategies

1979 ◽  
Vol 10 (2) ◽  
pp. 183-194
Author(s):  
T. Pentikäinen
Keyword(s):  
Dynamic Programming ◽  
Future Development ◽  
Stochastic Dynamic Programming ◽  
Management Strategies ◽  
Stochastic Dynamic ◽  
The Future ◽  
New Business ◽  
Insurance Business ◽  
Simulated Market

Stochastic-dynamic programming provides a technique for forecasting limits within which the insurance business will flow by a prefixed probability. The future development depends, among numerous other things, on management strategies, especially resources, which are planned for allocation in the acquisition of new business and for competition. This technique can be used to analyse different market situations. Various competitive measures and eventual counteractions by competitors can be assumed and simulated for the purpose. In this way the consequences of different strategies can be studied in order to find the most appropriate one. Our approach is similar to the well-known business games where teams play business in a simulated market. The idea of applying dynamic programming to business games was suggested by Esa Hovinen (discussion at the Astin Colloquium in Washington in 1977).

scholarly journals Simultaneous Optimization of Supervisory Control and Gear Shift Logic for a Parallel Hydraulic Hybrid Refuse Truck Using Stochastic Dynamic Programming

2011 ◽  
Author(s):  
Rajit Johri ◽  
Simon Baseley ◽  
Zoran Filipi
Keyword(s):  
Dynamic Programming ◽  
Power Management ◽  
Stochastic Dynamic Programming ◽  
Management Strategies ◽  
Infinite Horizon ◽  
Simultaneous Optimization ◽  
System Level ◽  
Stochastic Dynamic ◽  
Gear Shift ◽  
Hydraulic Hybrid

The power management controller of a hybrid vehicle orchestrates the operation of onboard energy sources, namely engine and auxiliary power source with the goal of maximizing performance objectives such as the fuel economy. The paper focuses on optimization of the power management strategy of the refuse truck with parallel hydraulic hybrid powertrain. The high power density of hydraulic components and high charging/discharging efficiency of accumulator with no power constraint make hydraulic hybrid an excellent choice for heavy-duty stop and go application. Two power management strategies for a parallel hydraulic hybrid refuse truck are compared; heuristic and stochastic dynamic programming based optimal controller. For designing a SDP based controller, an infinite horizon problem is setup with power demand from driver modeled as random Markov process. The objective is to maximize system level efficiency by optimizing (i) the power split between engine and hydraulic propulsion unit, and (ii) gear shift schedule. This combines the optimization of powertrain parameters with power management design.

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
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