Abstract: There is a wide range of problems in energy systems that require making decisions in the presence of different forms of uncertainty. The fields that address sequential, stochastic decision ...
Many Optimization problems in engineering and economic involve the challenging task of pondering both conflicting goals and random data. In this paper, we give an up-to-date overview of how important ...
Multistage stochastic mixed-integer linear problems (MSMILPs) are non-convex, usually of large scale, and hard to solve. This calls for decomposition approaches to keep the solution process ...
Abstract: Dynamic Programming (DP) provides a powerful framework for modeling complex decision problems where uncertainty is resolved and decisions are made over time. But it is difficult to scale to ...
ABSTRACT: The study of unit commitment (UC) aims to find reasonable schedules for generators to optimize power systems’ operation. Many papers have been published that solve UC through different ...
This study proposes a two-stage stochastic programming model of a transportation network with arc capacities. Our objective is to transport a supply of raw materials from one location (node) to ...
In this tutorial, three examples of stochastic systems are considered: a strongly damped oscillator, a weakly damped oscillator and an undamped oscillator (integrator) driven by noise. The evolution ...
1 Department of Statistics and Mathematics, Bindura University of Science Education, Bindura, Zimbabwe 2 Department of Mathematics, University of Botswana, Gaborone, Botswana This study develops a ...
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