Environmental constraints in hydropower systems serve to ensure sustainable use of water resources. Through accurate treatment scheduling, one seeks respect such the planning phase while optimizing utilization hydropower. However, many environmental introduce state-dependencies and even nonconvexities scheduling problem, making them challenging represent stochastic models. This paper describes ...

The long-term hydrothermal scheduling (LTHS) problem seeks to obtain an operational policy that optimizes water resource management. most employed strategy such a is stochastic dual dynamic programming (SDDP). primary source of uncertainty in predominant hydropower systems the reservoirs inflow, usually linear time series model (TSM) based on order-p periodic autoregressive [PAR(p)] model. Alth...

• A stagewise decomposition algorithm for large-scale stochastic program is proposed. The approximates value functions by predetermined parametric forms. fits the gradients of function to those function. Numerical examples show computational efficiency proposed algorithm. called “value gradient learning” (VFGL) multistage convex programs. VFGL finds parameter values that best fit within a given...

Even with recent enhancements, computation times for large-scale multistage problems with risk-averse objective functions can be very long. Therefore, preprocessing via scenario reduction could be considered as a way to significantly improve the overall performance. Stage-wise backward reduction of single scenarios applied to a fixed branching structure of the tree is a promising tool for effic...

We consider risk-averse formulations of multistage stochastic linear programs. For these formulations, based on convex combinations of spectral risk measures, risk-averse dynamic programming equations can be written. As a result, the Stochastic Dual Dynamic Programming (SDDP) algorithm can be used to obtain approximations of the corresponding risk-averse recourse functions. This allows us to de...

Multistage stochastic integer programming (MSIP) combines the difficulty of uncertainty, dynamics, and non-convexity, and constitutes a class of extremely challenging problems. A common formulation for these problems is a dynamic programming formulation involving nested cost-to-go functions. In the linear setting, the cost-to-go functions are convex polyhedral, and decomposition algorithms, suc...

This paper builds upon the theoretical foundations of Accountable eXplainable Artificial Intelligence (AXAI) capability framework presented in part one this paper. We demonstrate incorporation AXAI real time Affective State Assessment Module (ASAM) a robotic system. show that adhering to eXtreme Programming (XP) practices would help understanding user behavior and systematic Machine Learning (M...

Abstract We present a novel tool for generating speculative and hedging foreign exchange (FX) trading policies. Our solution provides schedule that determines trades in each rebalancing period based on future currency prices, net account positions, incoming (outgoing) flows from business operations. To obtain such policies, we construct multistage stochastic programming (MSP) model solve it usi...

This paper proposes a distributed solution for fault diagnosis in wireless sensor networks (WSN). Fault diagnosis is achieved by comparing the heartbeat sequence number generated by neighbouring nodes and dissemination of decision made at each node. The proposed protocol considers channel error where the channel is modelled as two state Markov model. Theoretical analysis and simulation results ...

Multistage stochastic integer programming (MSIP) combines the difficulty of uncertainty, dynamics, and non-convexity, and constitutes a class of extremely challenging problems. A common formulation for these problems is a dynamic programming formulation involving nested cost-to-go functions. In the linear setting, the cost-to-go functions are convex polyhedral, and decomposition algorithms, suc...