Water Need Models and Irrigation Decision Systems

Irrigation decision systems and water need models have been important research topics in agriculture since 90s. They improve the efficiency of crop yields, provide an appropriate use of water on the earth and so, prevent the water scarcity in some regions. In this paper, a comprehensive survey on water need models depending on crop growth and irrigation decision systems has been conducted based on mathematical maodelling. The following outcomes and solutions are the main contributions. Crop growth models and correspondingly water need models are suffer from un-modeled dynamics of the environment and lack of sensory devices. Literature review with the latest developments on water need models, irrigation decision systems, applied control methods and discussions are expected to be useful for the future strategies.Comment: 10 pages, 0 figures, 1 table, survey pape

A Unified Framework for Adjustable Robust Optimization with Endogenous Uncertainty

This work proposes a framework for multistage adjustable robust optimization that unifies the treatment of three different types of endogenous uncertainty, where decisions, respectively, (i) alter the uncertainty set, (ii) affect the materialization of uncertain parameters, and (iii) determine the time when the true values of uncertain parameters are observed. We provide a systematic analysis of the different types of endogenous uncertainty and highlight the connection between optimization under endogenous uncertainty and active learning. We consider decision-dependent polyhedral uncertainty sets and propose a decision rule approach that incorporates both continuous and binary recourse, including recourse decisions that affect the uncertainty set. The proposed method enables the modeling of decision-dependent nonanticipativity and results in a tractable reformulation of the problem. We demonstrate the effectiveness of the approach in computational experiments that cover a range of applications, including plant redesign, maintenance planning with inspections, optimizing revision points in capacity planning, and production scheduling with active parameter estimation. The results show significant benefits from the proper modeling of endogenous uncertainty and active learning