scholarly journals Approximate Dynamic Programming for Large-Scale Resource Allocation Problems

2006 ◽  
pp. 123-147
Author(s):  
Warren B. Powell ◽  
Huseyin Topaloglu
Keyword(s):  
Resource Allocation ◽  
Dynamic Programming ◽  
Large Scale ◽  
Approximate Dynamic Programming ◽  
Allocation Problems

scholarly journals Approximate Dynamic Programming Based Control of Proppant Concentration in Hydraulic Fracturing

Mathematics ◽  
2018 ◽  
Vol 6 (8) ◽  
pp. 132 ◽  
Author(s):  
Harwinder Singh Sidhu ◽  
Prashanth Siddhamshetty ◽  
Joseph Kwon
Keyword(s):  
Mechanical Properties ◽  
Dynamic Programming ◽  
Hydraulic Fracturing ◽  
Young’S Modulus ◽  
Large Scale ◽  
Approximate Dynamic Programming ◽  
Young's Modulus ◽  
Computational Cost ◽  
Control Technique ◽  
Rock Mechanical Properties

Hydraulic fracturing has played a crucial role in enhancing the extraction of oil and gas from deep underground sources. The two main objectives of hydraulic fracturing are to produce fractures with a desired fracture geometry and to achieve the target proppant concentration inside the fracture. Recently, some efforts have been made to accomplish these objectives by the model predictive control (MPC) theory based on the assumption that the rock mechanical properties such as the Young’s modulus are known and spatially homogenous. However, this approach may not be optimal if there is an uncertainty in the rock mechanical properties. Furthermore, the computational requirements associated with the MPC approach to calculate the control moves at each sampling time can be significantly high when the underlying process dynamics is described by a nonlinear large-scale system. To address these issues, the current work proposes an approximate dynamic programming (ADP) based approach for the closed-loop control of hydraulic fracturing to achieve the target proppant concentration at the end of pumping. ADP is a model-based control technique which combines a high-fidelity simulation and function approximator to alleviate the “curse-of-dimensionality” associated with the traditional dynamic programming (DP) approach. A series of simulations results is provided to demonstrate the performance of the ADP-based controller in achieving the target proppant concentration at the end of pumping at a fraction of the computational cost required by MPC while handling the uncertainty in the Young’s modulus of the rock formation.

Modelling and solving resource allocation problems via a dynamic programming approach

2019 ◽  
pp. 1-12
Author(s):  
Ali Forootani ◽  
Massimo Tipaldi ◽  
Majid Ghaniee Zarch ◽  
Davide Liuzza ◽  
Luigi Glielmo
Keyword(s):  
Resource Allocation ◽  
Dynamic Programming ◽  
Programming Approach ◽  
Dynamic Programming Approach ◽  
Allocation Problems

scholarly journals Train Regulation Combined with Passenger Control Model Based on Approximate Dynamic Programming

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 303 ◽  
Author(s):  
Sijia Hao ◽  
Rui Song ◽  
Shiwei He ◽  
Zekang Lan
Keyword(s):  
Dynamic Programming ◽  
Large Scale ◽  
Approximate Dynamic Programming ◽  
Control Model ◽  
High Dimensions ◽  
Proposed Model ◽  
Markov Decision ◽  
Train Operation ◽  
Interactive Relationship ◽  
Passenger Flows

Rescheduling is often needed when trains stay in segments or stations longer than specified in the timetable due to disturbances. Under crowded situations, it is more challenging to return to normal with heavy passenger flow. Considering making a trade-off between passenger loss and operating costs, we present a train regulation combined with a passenger control model by analyzing the interactive relationship between passenger behaviors and train operation. In this paper, we convert the problem into a Markov decision process and then propose the management strategy of regulating the running time and controlling the number of boarding passengers. Owing to the high dimensions of the large-scale problem, we applied the Approximate Dynamic Programming (ADP) approach, which approximates the value function with state features to improve computational efficiency. Finally, we designed three experimental scenarios to verify the effectiveness of our proposed model and approach. The results show that both the proposed model and the approach have a good performance in the cases with different passenger flows and different disturbances.

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