Cplex Software Provides Better

This configuration offers the opportunity to improve the computational time and the fitness function.

Cplex Software Provides Better

Download View publication Copy reference Copy caption Embed figure Capability Comparison among CPLEX, GuRoBi, and Xpress Source publication A Comparative Analysis of Optimization Solvers Article Full-text available Jul 2017 Rimmi Anand Divya Aggarwal Vijay Chahar Optimization software provides better design and development of optimization solutions for real-life problems.

The software generates different solutions under different constraints.

An attempt has been made to compare three well-known optimization solvers.

The comparisons have been done in terms of capabilities and problem domain.

The results reve.

Cite Download full-text Context in source publication Context 1.

The capabilities of optimization solvers are described in Table 5..

View in full-text Similar publications 2 Proposed system for analyzing the location of preschools: a Brazilian case study Article Full-text available Dec 2017 Kellen Dayelle Endler Cassius Tadeu Scarpin Maria Teresinha A.

Steiner Early infant education and care have become priority issues in Brazil.

The present article evaluates the spatial distribution of public preschools in the municipality of Curitiba, in Paran State, Brazil.

This problem is an important aspect of school administration and can relieve problems of inequality in public education, such as accessibility an.

View A Criterion Space Search Algorithm for Mixed Integer Linear Maximum Multiplicative Programs: A Multi-objective Optimization Approach Preprint Jan 2019 Payman Ghasemi Saghand Hadi Charkhgard We study a class of mixed integer optimization problems with linear constraints and a multi-linear objective function, the so-called mixed integer linear maximum multiplicative programs (MIL-MMPs).

Several studies have compared performance among different solvers for mixed-integer linear and non- linear mathematical optimization problems and included Gurobi in the analysis.

It was calculated using Jupyter in a MacBook Pro notebook with 2.9 GHz Intel Core i9 and 32 GB 2400 MHz DDR4..

A Linear Programming Model for Optimal Check Airmen Allocation Article Nov 2020 Joao S D Garcia Christian K Jdicke Dothang Truong Across the globe, civil aviation authorities (CAA) require pilots to be examined upon completion of their flight training and at regular intervals to uphold their pilots license.

These flight examinations, or checkrides, are conducted by designated flight examiners and CAA pilots.

While government employees are dispatched to different locations to conduct such exams, designated check airmen may only conduct checkrides that have limited coverage in the geographic area in which those exams are allowed.

Thus, if the demand for checkrides at a given location is higher than the number of available designated flight examiners, those employed by the CAA may have to travel to satisfy the need for checkrides, incurring additional costs to these organizations.

This paper aims at developing an optimization model using linear programming to find the optimal number of checkrides at different locations that minimizes the total travel cost of government check airmen (GCA) conducting checkrides, considering specific travel costs between locations.

Based on a realistic set of initialization parameters, the optimal solution showed a minimal travel cost of 35,827.30 for six months.

This model could be applied to other areas that may face a similar decision-making process.

View. Regarding the data, in our simulations, the m inputs and s outputs of each of the n DMUs are generated randomly but take into account that the production function that governs the production situation is the Cobb-Douglas function 24, which is well-known in microeconomics.

A Parallel Algorithm for Matheuristics: A Comparison of Optimization Solvers Article Full-text available Sep 2020 Martn Gonzlez Jose J.

Lopez-Espin Juan Aparicio Metaheuristic and exact methods are one of the most common tools to solve Mixed-Integer optimization Problems (MIP).

Most of these problems are NP-hard problems, being intractable to obtain optimal solutions in a reasonable time when the size of the problem is huge.

In this paper, a hybrid parallel optimization algorithm for matheuristics is studied.

In this algorithm, exact and metaheuristic methods work together to solve an MILP problem which is divided into two different subproblems, one of which is linear (and easier to solve by exact methods) and the other discrete (and is solved using metaheuristic methods).

The algorithm proposed follows an efficient decomposition which is based on the nature of the decision variables (continuous versus discrete).

Because of the high cost of the algorithm, as this kind of problem belongs to NP-hard problems, parallelism techniques have been incorporated.

The matheuristic has been optimized both at the level of the problem division and internally.

Cplex For Mac