The management of a water reservoir can be improved thanks to the use of stochastic dynamic programming (SDP) to generate management policies which are efficient with respect to the management objectives (flood protection, water supply for irrigation and hydropower generation, respect of minimum...

In this paper we study a Markov decision process with a non-linear discount function. Our approach is in spirit of the von Neumann-Morgenstern concept and is based on the notion of expectation. First, we define a utility on the space of trajectories of the process in the finite and infinite time...

The combination of electric vehicles and renewable energy is taking shape as a potential driver for a future free of fossil fuels. However, the efficient management of the electric vehicle fleet is not exempt from challenges. It calls for the involvement of all actors directly or indirectly...

Global change in climate and consequent large impacts on regional hydrologic systems have, in recent years, motivated significant research efforts in water resources modeling under climate change. In an integrated future hydrologic scenario, it is likely that water availability and demands will...

A genetic algorithm (GA) and a backward moving stochastic dynamic programming (SDP) model has been developed for derivation of operational policies for a multi-reservoir system in Kodaiyar River Basin, Tamil Nadu, India. The model was developed with the objective of minimizing the annual sum of...

In this study, a factorial-based fuzzy-stochastic dynamic programming (FFS-DP) method is developed for tackling multiple uncertainties including fuzziness, randomness and their interaction in reservoir operation management (ROM). FFS-DP is framed on the integration of stochastic dynamic...

A comprehensive Genetic Algorithm (GA) model has been developed and applied to derive optimal operational strategies of a multi-purpose reservoir, namely Perunchani Reservoir, in Kodaiyar Basin in Tamil Nadu, India. Most of the water resources problem involves uncertainty, in order to see that...

This study addresses water resources system planning problems with capacity expansion in an uncertain environment. An interval stochastic dynamic programming (SDP) model is presented, which is a hybrid of interval-number optimization and SDP. Besides the dynamic features of the model, it can...

The study focuses on the dynamic multi-site capacity planning problem in the thin film transistor liquid crystal display (TFT-LCD) industry under stochastic demand. Capacity planning refers to the process of simultaneously implementing a robust capacity allocation plan and capacity expansion...