Retrospective on optimization

Biegler, Lorenz T.; Grossmann, Ignacio E.

doi:10.1016/j.compchemeng.2003.11.003

Cited by 504 publications

(284 citation statements)

References 87 publications

Supporting

Mentioning

276

Contrasting

Unclassified

Order By: Relevance

“…The set J is the index set of inequality constraints, and x and y are the continuous and discrete/integer variables, respectively. If the objective function f and the constraint functions g are convex, the problem is known as convex, otherwise the problem is a nonconvex MINLP [3]. A great variety of problems in the areas of engineering design, computational chemistry, computational biology, communications and finance can be represented by MINLP models.…”

Section: Introductionmentioning

confidence: 99%

Numerical Experiments with Nonconvex MINLP Problems

Fernandes¹,

Costa²,

Fernandes³

2010

AIP Conference Proceedings

View full text Add to dashboard Cite

Abstract. We present a methodology to solve nonconvex Mixed-Integer Nonlinear Programming problems, that combines the Branch-and-Bound and simulated annealing type methods, which was implemented in MATLAB. A set of benchmark functions with simple bounds and different dimensions was used to analyze its practical behaviour. We exhibit computational results showing the good performance of the method.

show abstract

Section: Introductionmentioning

confidence: 99%

Numerical Experiments with Nonconvex MINLP Problems

Fernandes¹,

Costa²,

Fernandes³

2010

AIP Conference Proceedings

View full text Add to dashboard Cite

show abstract

“…Over the last few decades, various methods have been developed for simulating the distribution of energy in a time-dependent manner [27]. These methods are based on mathematical models, and various optimization algorithms are applied [28,29] to investigate the optimal distributed energy system, considering electricity, heat and gas as energy carriers [30]. All these methods have proven highly useful for planning the overall energy system on a national and international basis.…”

Section: Non-gis-based Approaches (Non-geospatial Methods)mentioning

confidence: 99%

GIS-Based Planning and Modeling for Renewable Energy: Challenges and Future Research Avenues

Resch

Sagl

Törnros

et al. 2014

IJGI

124

View full text Add to dashboard Cite

Abstract:In the face of the broad political call for an -energy turnaround‖, we are currently witnessing three essential trends with regard to energy infrastructure planning, energy generation and storage: from planned production towards fluctuating production on the basis of renewable energy sources, from centralized generation towards decentralized generation and from expensive energy carriers towards cost-free energy carriers. These changes necessitate considerable modifications of the energy infrastructure. Even though most of these modifications are inherently motivated by geospatial questions and challenges, the integration of energy system models and Geographic Information Systems (GIS) is still in its infancy. This paper analyzes the shortcomings of previous approaches in using GIS in renewable energy-related projects, extracts distinct challenges from these previous efforts and, finally, defines a set of core future research avenues for GIS-based energy infrastructure planning with a focus on the use of renewable energy. These future OPEN ACCESS ISPRS Int. J. Geo-Inf. 2014, 3 663 research avenues comprise the availability base data and their -geospatial awareness‖, the development of a generic and unified data model, the usage of volunteered geographic information (VGI) and crowdsourced data in analysis processes, the integration of 3D building models and 3D data analysis, the incorporation of network topologies into GIS, the harmonization of the heterogeneous views on aggregation issues in the fields of energy and GIS, fine-grained energy demand estimation from freely-available data sources, decentralized storage facility planning, the investigation of GIS-based public participation mechanisms, the transition from purely structural to operational planning, data privacy aspects and, finally, the development of a new dynamic power market design.Keywords: integration of GIS and energy system models; GIS and renewable energy; GIS-based energy infrastructure planning; future research challenges; fluctuating renewables; structural planning of local energy systems; operation optimization

show abstract

“…The objective function is a least squares like functional. See Biegler and Grossmann (2004) for a review on numerical methods applied to problems of this sort.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Some Epidemic Models. The Case of Recurrent Epidemics Caused by Respiratory Syncytial Virus

2009

View full text Add to dashboard Cite

The research presented in this paper addresses the problem of fitting a mathematical model to epidemic data. We propose an implementation of the Landweber iteration to solve locally the arising parameter estimation problem. The epidemic models considered consist of suitable systems of ordinary differential equations. The results presented suggest that the inverse problem approach is a reliable method to solve the fitting problem. The predictive capabilities of this approach are demonstrated by comparing simulations based on estimation of parameters against real data sets for the case of recurrent epidemics caused by the respiratory syncytial virus in children.

show abstract

Retrospective on optimization

Cited by 504 publications

References 87 publications

Numerical Experiments with Nonconvex MINLP Problems

Numerical Experiments with Nonconvex MINLP Problems

GIS-Based Planning and Modeling for Renewable Energy: Challenges and Future Research Avenues

Parameter Estimation of Some Epidemic Models. The Case of Recurrent Epidemics Caused by Respiratory Syncytial Virus

Contact Info

Product

Resources

About