scholarly journals Modeling of Biological Intelligence for SCM System Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Shengyong Chen ◽  
Yujun Zheng ◽  
Carlo Cattani ◽  
Wanliang Wang
Keyword(s):  
Genetic Algorithms ◽  
Supply Chain ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
System Optimization ◽  
Evolutionary Programming ◽  
Artificial Immune ◽  
Chain Management ◽  
Design And Optimization ◽  
Information Materials

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

scholarly journals EVOLUTIONARY ALORITHMS AT CHOICE: FROM GA TO GP

2015 ◽  
Vol 2 ◽  
pp. 185 ◽  
Author(s):  
Peter Grabusts
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Evolutionary Programming ◽  
Stochastic Search ◽  
Evolution Strategies ◽  
Natural Evolution ◽  
Search Methods ◽  
The World ◽  
Classification Tasks

Nowadays the possibilities of evolutionary algorithms are widely used in many optimization and classification tasks. Evolutionary algorithms are stochastic search methods that try to emulate Darwin’s principle of natural evolution. There are (at least) four paradigms in the world of evolutionary algorithms: evolutionary programming, evolution strategies, genetic algorithms and genetic programming. This paper analyzes present-day approaches of genetic algorithms and genetic programming and examines the possibilities of genetic programming that will be used in further research. The paper presents implementation examples that show the working principles of evolutionary algorithms.

Research on Management Platform of Electronic Commerce Supply Chain Based on Programming Theory and XML Technology

2013 ◽  
Vol 427-429 ◽  
pp. 2946-2950
Author(s):  
Jie Chen
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Business Processes ◽  
Business Environment ◽  
Management Tool ◽  
Chain Management ◽  
Design And Optimization ◽  
Management Platform ◽  
Management Concepts ◽  
The Difference

In the era of Information technology, supply chain management is as. It not only brings us a new management tool,but also is important to bring the updating and re-planning of management concepts, design and optimization of the business processes means. To the end, it is combined with a modern e-commerce IT environment and combined with the characteristics of e-commerce environment to optimize the structure of the supply chain management, as well as in comparative analysis on the difference with supply chain management under traditional business environment. Based on the e-commerce environment, how to optimize the mode of supply chain management is focused in this paper. By using XML technical support methods, the author optimized warehouse management, economies scale and so on. Finally, Based on the optimization of the supply chain management model, the author established the platform of supply chain management based on e-commerce environment, as well as described a detailed modeling and program design and developed the module of e-commerce supply chain management platform.

Specific Evolutionary Algorithms

1996 ◽  
Author(s):  
Thomas Bäck
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Selection Process ◽  
Evolutionary Programming ◽  
Main Stream ◽  
Genetic Operators ◽  
Optimization Task ◽  
Main Components ◽  
High Level

In this chapter, an outline of an Evolutionary Algorithm is formulated that is sufficiently general to cover at least the three different main stream algorithms mentioned before, namely, Evolution Strategies, Genetic Algorithms, and Evolutionary Programming. As in the previous chapter, algorithms are formulated in a language obtained by mixing pseudocode and mathematical notations, thus allowing for a high-level description which concentrates on the main components. These are: A population of individuals which is manipulated by genetic operators — especially mutation and recombination, but others may also be incorporated — and undergoes a fitness-based selection process, where fitness of an individual depends on its quality with respect to the optimization task.

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