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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.
Linear Programming
Published in Michael W. Carter, Camille C. Price, Ghaith Rabadi, Operations Research, 2018
Michael W. Carter, Camille C. Price, Ghaith Rabadi
LINDO (Linear INteractive and Discrete Optimizer), originally developed by Linus Schrage (1991), is one of the oldest and now among the most popular commercial systems for solving linear programming problems. LINDO API and the LINGO modeling system offer powerful solvers for linear programs, based on methods including primal and dual simplex for speed and robust computations.
Optimization of Organizational Design
Published in Journal of Computer Information Systems, 2022
Dyin Mabe, Guilherme Esmael, Manoel Burg, Patricia Soares, Leila Halawi
After generating this initial output (Table 8), the researchers developed a mathematical formula inserted into the Lindo software. The calculation was made through linear programming to distribute the Project Department’s best allocation at the Airline. The optimization model’s software is Lindo, a mathematical optimization system defined by Schrage29 as software for linear programming, integer programming, nonlinear programming, stochastic programming, and global optimization. Several organizations have been used to maximize profit and reduce cost on decisions involving many variables. The system is often used in models to optimize production planning, transportation, finance, resource allocation, and more. The researchers were looking for ways to find the best allocation at the least cost (employee score 1) in total staff to deliver a certain task that would not be the most productive. Mathematically, clustering can also be achieved using several optimization functions (OFs).1 With Lindo, the researchers were able to identify the “optimal” point between the costs of personnel versus the need for productivity. The mathematical function, which helps us to explain the final calculation, is listed as:
A comprehensive review for power production and economic feasibility on hybrid energy systems for remote communities
Published in International Journal of Ambient Energy, 2022
M. Edwin, M. Saranya Nair, S. Joseph Sekhar
HOMER and LINDO software are used to improve the hybrid energy system and limit the capital and running costs of the framework. LINDO Systems have built up an accumulation of programming bundles that encourage building and solving optimisation models identified with benefit augmentation, minimum cost, production planning, transmission, investment, portfolio distribution, capital planning, unification, development, inventory and resource distribution (Thakur et al. 2012).
Learning-based dynamic ticket pricing for passenger railway service providers
Published in Engineering Optimization, 2023
Keyvan Kamandanipour, Siamak Haji Yakhchali, Reza Tavakkoli-Moghaddam
To evaluate the proposed RM methodology, the optimization module is run for a numerical example under special conditions, and then a sensitivity analysis is carried out. Lastly, some special departure dates are selected to evaluate the results of the proposed method. The optimization model is solved with LINGO 14, an optimization software program from Lindo Systems.