A data-driven approach for a class of stochastic dynamic optimization problems.

Dynamic stochastic optimization models provide a powerful tool to represent sequential decision-making processes. Typically, these models use statistical predictive methods to capture the structure of the underlying stochastic process without taking into consideration estimation errors and model misspecification. In this context, we propose a data-driven prescriptive analytics framework aiming to integrate the machine learning and dynamic optimization machinery in a consistent and efficient way to build a bridge from data to decisions. The proposed framework tackles a relevant class of dynamic decision problems comprising many important practical applications. The basic building blocks of our proposed framework are: (1) a Hidden Markov Model as a predictive (machine learning) method to represent uncertainty; and (2) a distributionally robust dynamic optimization model as a prescriptive method that takes into account estimation errors associated with the predictive model and allows for control of the risk associated with decisions. Moreover, we present an evaluation framework to assess out-of-sample performance in rolling horizon schemes. A complete case study on dynamic asset allocation illustrates the proposed framework showing superior out-of-sample performance against selected benchmarks. The numerical results show the practical importance and applicability of the proposed framework since it extracts valuable information from data to obtain robustified decisions with an empirical certificate of out-of-sample performance evaluation.

Global warming impact to River Basin of Blue Nile and the optimum operation of its multi-reservoir system for hydropower production and irrigation.

The water resource of the Blue Nile River basin (BNRB) has been under pressure due to growing demands from many users, and the climate change impact. Potential impact of climate change for the maximum, median and minimum projected changes in the simulated streamflow of BNRB by a hydrologic model, VIC, driven by Representative Concentration Pathways climate scenarios, RCP4.5 and RCP8.5, of 4 GCMs (global climate models) downscaled dynamically by a regional climate model, WRF (Weather Research Forecasting) using a one-domain framework that covers the entire NRB for 2041-2070 and 2071-2100. These projected changes in streamflow were used to assess its future water allocations using a stochastic Dual Dynamic Programming (SDDP) algorithm and a hydro-economic model to optimize hydropower production and irrigated agriculture. Overall, it seems the Grand Ethiopian Renaissance Dam (GERD) reservoir will likely not operate at full storage level because the streamflow of BNRB is assumed to be regulated by three upstream reservoirs. The outflow from the reservoir of GERD or BNRB's annual flow at Khartoum is projected to increase under maximum, but is expected to decrease under minimum and median projected changes in streamflow for 2041-2070 and 2071-2100, respectively. Given the annual net benefit obtained from hydropower production and irrigated agriculture of the reservoir is projected to increase (decrease) under the maximum (median and minimum) projected changes in streamflow, the potential climate change impact should be considered in designing and developing the future water resources of BNRB.