Can existing software handle industrial optimization problems? optimization of the kemira's (B) ammonia synthesis loop

1994 ◽  
Vol 18 ◽  
pp. S165-S169 ◽  
Author(s):  
A.J. Kontopoulos ◽  
B. Kalitventzeff ◽  
J. Rennotte ◽  
J.L. Bovens
Keyword(s):  
Ammonia Synthesis ◽  
Optimization Problems ◽  
Industrial Optimization

scholarly journals On decomposition and multiobjective-based column and disjunctive cut generation for MINLP

2020 ◽  
Author(s):  
Pavlo Muts ◽  
Ivo Nowak ◽  
Eligius M. T. Hendrix
Keyword(s):  
Nonlinear Programming ◽  
Optimization Problems ◽  
Mixed Integer ◽  
Resource Constrained ◽  
Linear Coupling ◽  
Integer Nonlinear Programming ◽  
Coupling Constraints ◽  
Cut Generation ◽  
Low Dimensional ◽  
Industrial Optimization

Abstract Most industrial optimization problems are sparse and can be formulated as block-separable mixed-integer nonlinear programming (MINLP) problems, defined by linking low-dimensional sub-problems by (linear) coupling constraints. This paper investigates the potential of using decomposition and a novel multiobjective-based column and cut generation approach for solving nonconvex block-separable MINLPs, based on the so-called resource-constrained reformulation. Based on this approach, two decomposition-based inner- and outer-refinement algorithms are presented and preliminary numerical results with nonconvex MINLP instances are reported.

The Motivation, Development and Applications of a Commercial Optimization Software Package Using DOE/RSM and NLP Methods

1998 ◽  
Author(s):  
Nikolas Tzannetakis ◽  
Erwin Glassée ◽  
Rudi Cartuyvels ◽  
Jean-Louis Migeot
Keyword(s):  
Automotive Industry ◽  
Design Space ◽  
Optimization Problems ◽  
Essential Elements ◽  
Simulation Software ◽  
Parallel Execution ◽  
Non Linear Programming ◽  
Programming Techniques ◽  
Stochastic Interpolation ◽  
Industrial Optimization

Abstract This paper presents the motivation development and an application of a unique methodology to solve Industrial Optimization problems, using existing simulation software programs. In order to make this a reality, (1) a unique way of communicating with simulation programs, (2) parallel execution of multiple Designs dictated by an extensive library of Design of Experiment plans - sampling the Design Space with the minimum amount of function evaluations while capturing the Design behavior - (3) a Stochastic Interpolation Response Surface Modeling technique and (4) Non-Linear Programming techniques are the essential elements of this methodology. Finally an application from the automotive industry demonstrate the methodology.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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