Sample Article Review on Genetic Algorithm for Flow shop Sequencing

Summary of the Article a Genetic Algorithm for Flow shop Sequencing

The paper explores the use of genetic algorithm in solving sequence problems. This is where the researcher transposes through various means that are used to arrive at a lasting solution. Use of chromosomal representation, selection mechanism, and mutation are genetic defining mechanism that tries to come up with a particular direction to be used in categorizing chromosomes. Heuristics generation of results by the use of these programs has encountered some problems in the past; this is the reason behind the investigators research on the feasibility of the use of machine-sequencing problems.

Significant and Important From a Publications Point of View

The significant of the paper is that it explains the faults manifested by the current machines in solving flow shop sequence problems. This gives the opportunity for the reader and practitioner to familiarize themselves to several issues concerning sequence problems and their solution.

Key Arguments Made By the Article

The article is structured along the genetic algorithm for solving flow shop sequencing problems that are applied when choosing chromosomes. Some of these problems require simple intervention instead of sophisticated machine sequencing method being used to arrive at a solution (Gao 34). This inhibits the attainment of success due to delays and the formation of heuristics generation. Heuristics are programmed computer applications that run on S82 computer. Their programming is in Pascal and is applied in sequence that presents a difficult time to the users when it comes to their Implementation.

Research Model of the Paper

The diagram below shows the model that was used in the paper to determine the maximum optimization of genetic algorithms.

Machine sequencing problem


Figure 1: Represents research model used in the paper.

Genetic algorithm used by the researchers to generate the best chromosomes from parents to have healthy offspring’s. Chromosomes representation on the other hand concentrates on the composition of x and y-chromosomes to produce a strong offspring’s. Permutation refers to models developed to help in the selection process for chromosomes from male and female. These factors determine the success of machine solving a sequence problem. In most cases, these machines are observed to lack the natural stop when solving a sequence problem. Thus, they cannot be relied upon as a perfect solution to the decision problem.

Independent and Dependent Variables in the Model

From the diagram above it is clear that the solution to the machine sequence problem depends on the generic algorithm used by the researcher. This means that the independent variable is the generic algorithm that is to be used while the dependent variable is the machine sequence problem. Selection of chromosomes is very crucial in this process because it determines the quality of the offspring’s. It implies that an accurate method should be implemented in selecting chromosomes.

Use by Researchers and Practitioners

This paper is extremely helpful to a researcher in the sense that they use it in conducting further investigation and study of the selection of chromosomes. They will make intensive and extensive study of chromosomes to ensure that they come up with a formidable program that addresses these problems in the best way possible. To the practitioners, the paper acts as a guiding tool to conduct their business. In addition, it sensitize on the use of improper approaches in solving sequence problems.








Works Cited

Gao, Sally. Machine Design and Manufacturing Engineering: Selected, Peer Reviewed Papers from the 2012 International Conference on Machine Design and Manufacturing Engineering (icmdme 2012) May 11-12, 2012, Jeju Island, South Korea. Durnten-Zurich: Trans Tech Publications, 2012. Print.





















  1. Figure 1: Represents research model used in the paper………………………………………2