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Deterministic Optimization
Published in Juan Gabriel Segovia-Hernández, Fernando Israel Gómez-Castro, ®, 2017
Juan Gabriel Segovia-Hernández, Fernando Israel Gómez-Castro
In this chapter, some of the basic concepts of deterministic optimization have been presented. In particular, methods for the solution of nonlinear optimization problems are presented. Nevertheless, deterministic optimization embraces several other types of problems, such as the mixed-integer optimization problems or the general disjunctive optimization problems. Furthermore, process engineering models usually involve a great number of constraints, which make finding solutions for the models difficult. Because of that, the use of software for the solution of such models, and the associated optimization problems, is mandatory. Deterministic optimization software, such as GAMS and LINDO, are available in the market. These software use an equation-based approach. They are based on the use of solvers to determine optimal solutions for the objective function subject to a set of constraints. Solvers are basically routines to optimize, and most of them are based on gradient methods. Both GAMS and LINDO have the capacity to deal with different optimization problems, such as linear programming (LP), nonlinear programming (NLP), mixed-integer linear programming (MILP), and mixed-integer nonlinear programming (MINLP), using local or global solvers. The user is required to write the model to be solved, and the software uses a given method to optimize it in terms of the objective function. In fact, although the software uses default solvers for each type of optimization problem, the user must be careful to properly select the solver.
Different Methods for Frequency, Angle, and Voltage Monitoring in Deregulated Power for Smart Grids
Published in Baseem Khan, Om Prakash Mahela, Hassan Haes Alhelou, Sanjeevikumar Padmanaban, Deregulated Electricity Market, 2023
Rajesh Kumar, Kusum Lata Agarwal, Avdhesh Sharma
GAMS is a high-level modeling system for the mathematical programming and optimization. It has an input file, the input file is used to get compiled, and then there is a need to select a solver for optimization. The solver solves the function and gives back the result and generates an output file. Here, the important point is how to give the input file, how to select the solver, and how to write the output. The conventions for naming the extensions of the files are as follows37: Input file: FILENAME.GMSOutput file: FILENAME.LST
Introduction to Operations Research
Published in Michael W. Carter, Camille C. Price, Ghaith Rabadi, Operations Research, 2018
Michael W. Carter, Camille C. Price, Ghaith Rabadi
GAMS, a general algebraic modeling system, was one of the earliest developed modeling languages, and is now among the most well known and widely used modeling systems for large scale optimization. GAMS links to libraries and programming languages, databases and spreadsheet files, and runs on Windows, Macintosh, Linux, and IBM platforms. GAMS is best known for its sophisticated solvers for the full range of optimization problems and for its graphical interface generator. More information on this system may be found at www.gams.com.
Optimization of water distribution networks using a deterministic approach
Published in Engineering Optimization, 2021
Gustavo Cassiolato, Esdras P. Carvalho, Jose A. Caballero, Mauro A. S. S. Ravagnani
In the present work, the main objective is to achieve the optimal configuration of commercial piping diameters for the WDN. The single-pipe approach is used to avoid solutions with a large number of diameters in the pipe length. Besides, additional pressure losses can exist at the pipe junctions and would be significant if a large number of different diameters existed in the pipe length. An MINLP optimization model is proposed and the General Algebraic Modeling System (GAMS) environment is used to solve the problem, without the use of hydraulic simulators to calculate pressure drops and velocities. GAMS is a high-level modelling system, and is very appropriate in solving mathematical programming and optimization problems. It consists of a language compiler and a set of high-performance solvers. In GAMS it is possible to model linear, nonlinear and mixed-integer optimization problems.
Optimal sizing of grid-connected hybrid renewable energy systems without storage: a generalized optimization model
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020
Ozan Capraz, Askiner Gungor, Ozcan Mutlu, Aysun Sagbas
Since the aim of the study is to reduce the negative environmental effects of the system and to promote RES with the acceptable cost, we do not consider SB and focus only on the grid-connected HRES-WS in the proposed model. This study contributes to the related literature by introducing a generalized optimization model that holistically combines the constraints that are particularly addressed in the grid-connected HRES-WS. The problem is formulated as an WMO-MILP model by incorporating the operational, technical, physical and/or capacity constraints. Investment (discrete) and operational (continuous) decisions are optimized simultaneously. The HRES-WS model is solved using the General Algebraic Modeling System (GAMS) software for a case study. GAMS is a programming language that is designed for modeling and solving linear, nonlinear, and mixed integer optimization problems. In this study, GAMS software is preferred because it has almost all types of available solvers such as CPLEX, Gurobi, etc., and it allows switching different solvers without changing the model formulation. A disadvantage of GAMS is that it requires expertise while formulating different optimization problems (GAMS 2020).
Travel Plans in Public Transit Networks Using Artificial Intelligence Planning Models
Published in Applied Artificial Intelligence, 2019
Fernando Elizalde-Ramírez, Romeo Sanchez Nigenda, Iris A. Martínez-Salazar, Yasmín Á. Ríos-Solís
To optimally solve our MILP model, we used the General Algebraic Modeling System (GAMS) version 24.2.3 with CPLEX 12.2. GAMS is a high-level modeling system for mathematical programming and optimization. All experiments were conducted on a Lenovo Thinkpad W540 with an Intel Core I7 4740 MQ processor, and 32 GB of RAM running Linux. In the next subsection, we present first our analysis for the full network models, and then our results on the reduced networks generated with the meganodes procedure.