Decentralized Robust Optimization for Real-time Dispatch of Power System Based on Approximate Dynamic Programming

2018 ◽  
Author(s):  
Chenxi Wang ◽  
Jianquan Zhu ◽  
Tao Zhu
Keyword(s):  
Dynamic Programming ◽  
Power System ◽  
Robust Optimization ◽  
Real Time ◽  
Approximate Dynamic Programming

scholarly journals Neural Approximate Dynamic Programming for On-Demand Ride-Pooling

2020 ◽  
Vol 34 (01) ◽  
pp. 507-515
Author(s):  
Sanket Shah ◽  
Meghna Lowalekar ◽  
Pradeep Varakantham
Keyword(s):  
Dynamic Programming ◽  
Real Time ◽  
Value Function ◽  
Approximate Dynamic Programming ◽  
Linear Program ◽  
Combinatorial Complexity ◽  
Pooling Problem ◽  
Time Step ◽  
Current Time ◽  
On Demand

On-demand ride-pooling (e.g., UberPool, LyftLine, GrabShare) has recently become popular because of its ability to lower costs for passengers while simultaneously increasing revenue for drivers and aggregation companies (e.g., Uber). Unlike in Taxi on Demand (ToD) services – where a vehicle is assigned one passenger at a time – in on-demand ride-pooling, each vehicle must simultaneously serve multiple passengers with heterogeneous origin and destination pairs without violating any quality constraints. To ensure near real-time response, existing solutions to the real-time ride-pooling problem are myopic in that they optimise the objective (e.g., maximise the number of passengers served) for the current time step without considering the effect such an assignment could have on assignments in future time steps. However, considering the future effects of an assignment that also has to consider what combinations of passenger requests can be assigned to vehicles adds a layer of combinatorial complexity to the already challenging problem of considering future effects in the ToD case.A popular approach that addresses the limitations of myopic assignments in ToD problems is Approximate Dynamic Programming (ADP). Existing ADP methods for ToD can only handle Linear Program (LP) based assignments, however, as the value update relies on dual values from the LP. The assignment problem in ride pooling requires an Integer Linear Program (ILP) that has bad LP relaxations. Therefore, our key technical contribution is in providing a general ADP method that can learn from the ILP based assignment found in ride-pooling. Additionally, we handle the extra combinatorial complexity from combinations of passenger requests by using a Neural Network based approximate value function and show a connection to Deep Reinforcement Learning that allows us to learn this value-function with increased stability and sample-efficiency. We show that our approach easily outperforms leading approaches for on-demand ride-pooling on a real-world dataset by up to 16%, a significant improvement in city-scale transportation problems.

scholarly journals Strategic, Tactical and Real-Time Planning of Locomotives at Norfolk Southern Using Approximate Dynamic Programming

2012 ◽  
Author(s):  
Warren B. Powell ◽  
Belgacem Bouzaiene-Ayari ◽  
Clark Cheng ◽  
Ricardo Fiorillo ◽  
Sourav Das ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Real Time ◽  
Approximate Dynamic Programming ◽  
Planning System ◽  
Success Stories ◽  
Transit Times ◽  
Feedback Learning ◽  
Algorithmic Strategy ◽  
High Level ◽  
Time Planning

Locomotive planning has been a popular application of classical optimization models for decades, but with very few success stories. There are a host of complex rules governing how locomotives should be used. In addition, it is necessary to simultaneously manage locomotive inventories by balancing the need for holding power against the need for power at other yards. At the same time, we have to plan the need to return foreign power, and move power to maintenance facilities for scheduled FRA appointments. An additional complication arises as a result of the high level of uncertainty in transit times and delays due to yard processing, and as a result we may have to plan additional inventories in order to move outbound trains on time despite inbound delays. We describe a novel modeling and algorithmic strategy known as approximate dynamic programming, which can also be described as a form of “optimizing simulator” which uses feedback learning to plan locomotive movements in a way that closely mimics how humans plan real-world operations. This strategy can be used for strategic and tactical planning, and can also be adapted to real-time operations. We describe the strategy, and summarize experiences at Norfolk Southern with a strategic planning system.

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