scholarly journals APPLICATION OF STOCHASTIC DYNAMIC PROGRAMMING METHOD WHILE ORGANIZING THE CONTROL OF ORGANIZATIONAL AND TECHNICAL SYSTEMS

2021 ◽  
pp. 23-27
Author(s):  
V.F. Volkov ◽  
A.S. Ponomaryov
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Programming Method ◽  
Dynamic Programming Method ◽  
Stochastic Dynamic ◽  
Technical Systems

Feeder Ship Size Optimization in Maritime Markets with Uncertainty

2021 ◽  
Vol 38 (03) ◽  
pp. 2040014
Author(s):  
Hongtao Hu ◽  
Jiao Mo ◽  
Lufei Huang
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Numerical Experiments ◽  
Optimization Problem ◽  
Planning Horizon ◽  
Dynamic Programming Method ◽  
Size Optimization ◽  
Stochastic Dynamic ◽  
Shipping Company ◽  
Decision Behaviors

This paper considers the ship size optimization problem for a liner shipping company that provides feeder service between one hub port and one feeder port. In the maritime market with uncertainty, this problem becomes more challenging. This research first analyzes the decision behaviors of the shipping company. Then, a stochastic dynamic programming method is proposed to calculate the expected total volume of containers transported within the planning horizon. Using the calculated volumes as input parameters calculate the profit of each ship sizes and then determine the suitable ship size for the feeder route. Numerical experiments are performed to validate the effectiveness of the proposed method and the efficiency of the proposed algorithm.

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.

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