Network


Latest external collaboration on country level. Dive into details by clicking on the dots.

Hotspot


Dive into the research topics where Abebe Geletu is active.

Publication


Featured researches published by Abebe Geletu.


International Journal of Systems Science | 2013

Advances and applications of chance-constrained approaches to systems optimisation under uncertainty

Abebe Geletu; Michael Klöppel; Hui Zhang; Pu Li

A chance-constrained optimisation (CCOPT) model has a dual goal: guaranteeing performance as well as system reliability under uncertainty. The beginning of CCOPT methods dates back in the 1950s. Recently, CCOPT approaches are gaining momentum as modern engineering and finance applications are forced to consider reliability and risk metrics at the design and planning stages. Although theoretical development and practical applications have been made, many open problems remain to be addressed in this area. This article attempts to provide a brief survey of major application areas, structure properties, challenges and solution approaches to CCOPT. In particular, we present our research results achieved in the past few years.


European Journal of Operational Research | 2004

A conceptual method for solving generalized semi-infinite programming problems via global optimization by exact discontinuous penalization

Abebe Geletu; Armin Hoffmann

Abstract We consider a generalized semi-infinite programming problem (GSIP) with one semi-infinite constraint where the index set depends on the variable to be minimized. Keeping in mind the integral global optimization method of Zheng and Chew and its modifications we would like to outline theoretical considerations for determining coarse approximations of a solution of (GSIP) via global optimization of an exact discontinuous penalty approach. We consider an auxiliary parametric semi-infinite programming problem and the behavior of its marginal functional. In so doing we extend the theory of robust analysis to study robustness of marginal functions and robustness of set valued mappings with given structures.


Engineering Optimization | 2011

Monotony analysis and sparse-grid integration for nonlinear chance constrained process optimization

Abebe Geletu; Armin Hoffmann; Michael Klöppel; Pu Li

The numerical solution of a nonlinear chance constrained optimization problem poses a major challenge. The idea of back-mapping as introduced by M. Wendt, P. Li and G. Wozny in 2002 is a viable approach for transforming chance constraints on output variables (of unknown distribution) into chance constraints on uncertain input variables (of known distribution) based on a monotony relation. Once transformation of chance constraints has been accomplished, the resulting optimization problem can be solved by using a gradient-based algorithm. However, the computation of values and gradients of chance constraints and the objective function involves the evaluation of multi-dimensional integrals, which is computationally very expensive. This study proposes an easy-to-use method for analysing monotonic relations between constrained outputs and uncertain inputs. In addition, sparse-grid integration techniques are used to reduce the computational time decisively. Two examples from process optimization under uncertainty demonstrate the performance of the proposed approach.


Engineering Optimization | 2015

A tractable approximation of non-convex chance constrained optimization with non-Gaussian uncertainties

Abebe Geletu; Michael Klöppel; Armin Hoffmann; Pu Li

Chance constrained optimization problems in engineering applications possess highly nonlinear process models and non-convex structures. As a result, solving a nonlinear non-convex chance constrained optimization (CCOPT) problem remains as a challenging task. The major difficulty lies in the evaluation of probability values and gradients of inequality constraints which are nonlinear functions of stochastic variables. This article proposes a novel analytic approximation to improve the tractability of smooth non-convex chance constraints. The approximation uses a smooth parametric function to define a sequence of smooth nonlinear programs (NLPs). The sequence of optimal solutions of these NLPs remains always feasible and converges to the solution set of the CCOPT problem. Furthermore, Karush–Kuhn–Tucker (KKT) points of the approximating problems converge to a subset of KKT points of the CCOPT problem. Another feature of this approach is that it can handle uncertainties with both Gaussian and/or non-Gaussian distributions.


international conference on environment and electrical engineering | 2013

Chance constrained optimal power flow with non-Gaussian distributed uncertain wind power generation

Michael Klöppel; Aouss Gabash; Abebe Geletu; Pu Li

Chance constrained optimization (CCOPT) turns out to be a valuable approach to optimal power flow under uncertainty. The aim of this study is to develop a novel computation framework to solve CCOPT problems with non-Gaussian distributed uncertain variables and apply it to optimal power flow (OPF) under uncertain wind power penetration with Beta distribution. Results of OPF of a real distribution system with embedded wind power generation show the effectiveness of the proposed approach.


ieee international symposium on computer aided control system design | 2010

Evaluating integration approaches to robust process optimization and control using chance constrained programming

Michael Klöppel; Abebe Geletu; Armin Hoffmann; Pu Li

This contribution concerns robust optimization and control of nonlinear steady-state and dynamic processes under uncertain disturbances or parameters. We use chance constrained programming to solve such stochastic optimization and control problems. While steady-state processes always possess time-independent uncertain parameters, in dynamic processes there may be both time-independent and time-dependent uncertain parameters. For problems with time-dependent uncertain parameters these parameters will be discretized in the time horizon and thus the total number of uncertain variables to be treated will be high, which leads to difficulties to compute probabilities and their gradients. A new approach to an efficient computation is developed by using the sparse grid technique with which CPU-time can be significantly reduced. Two application examples from process engineering are taken to demonstrate the effectiveness of our computational framework. The performance of sparse grid integration is verified through numerical experiments in comparison to Monte-Carlo and Quasi-Monte-Carlo techniques.


Siam Journal on Optimization | 2017

An Inner-Outer Approximation Approach to Chance Constrained Optimization

Abebe Geletu; Armin Hoffmann; Michael Klöppel; Pu Li

Nonlinear chance constrained optimization (CCOPT) problems are known to be difficult to solve. This work proposes a smooth approximation approach consisting of an inner and an outer analytic approximation of chance constraints. In this way, CCOPT is approximated by two parametric nonlinear programming (NLP) problems which can be readily solved by an NLP solver. Any optimal solution of the inner approximation problem is a priori feasible to the CCOPT. The solutions of the inner and outer problems, respectively, converge asymptotically to the optimal solution of the CCOPT.


Industrial & Engineering Chemistry Research | 2011

Using Sparse-Grid Methods To Improve Computation Efficiency in Solving Dynamic Nonlinear Chance-Constrained Optimization Problems

Michael Klöppel; Abebe Geletu; Armin Hoffmann; Pu Li


Industrial & Engineering Chemistry Research | 2015

An Analytical Hessian and Parallel-Computing Approach for Efficient Dynamic Optimization Based on Control-Variable Correlation Analysis

Evgeny Lazutkin; Abebe Geletu; Siegbert Hopfgarten; Pu Li


ChemBioEng Reviews | 2014

Recent Developments in Computational Approaches to Optimization under Uncertainty and Application in Process Systems Engineering

Abebe Geletu; Pu Li

Collaboration


Dive into the Abebe Geletu's collaboration.

Top Co-Authors

Avatar

Pu Li

Technische Universität Ilmenau

View shared research outputs
Top Co-Authors

Avatar

Armin Hoffmann

Technische Universität Ilmenau

View shared research outputs
Top Co-Authors

Avatar

Michael Klöppel

Dresden University of Technology

View shared research outputs
Top Co-Authors

Avatar

Evgeny Lazutkin

Technische Universität Ilmenau

View shared research outputs
Top Co-Authors

Avatar

Siegbert Hopfgarten

Technische Universität Ilmenau

View shared research outputs
Top Co-Authors

Avatar

Aouss Gabash

Technische Universität Ilmenau

View shared research outputs
Top Co-Authors

Avatar

Erfan Mohagheghi

Technische Universität Ilmenau

View shared research outputs
Top Co-Authors

Avatar

Jörg Wensch

Dresden University of Technology

View shared research outputs
Top Co-Authors

Avatar

Xujiang Huang

Technische Universität Ilmenau

View shared research outputs
Researchain Logo
Decentralizing Knowledge