scholarly journals Applications of Nature-Inspired Intelligence in Finance

2007 ◽  
pp. 187-194 ◽  
Author(s):  
Vasilios Vasiliadis ◽  
Georgios Dounias
Keyword(s):  
Nature Inspired Intelligence

Computational Economics in the Era of Natural Computationalism

2018 ◽  
Author(s):  
Shu-Heng Chen ◽  
Mak Kaboudan ◽  
Ye-Rong Du
Keyword(s):  
General Equilibrium ◽  
Rational Expectations ◽  
Computational Economics ◽  
Algorithmic Trading ◽  
Neural Computing ◽  
Computing Systems ◽  
Agent Based ◽  
Numerical Aspect ◽  
Nature Inspired Intelligence

After a brief review of natural computationalism, this introductory chapter presents a new skeleton of computational economics and finance (CEF) along with an overview of the handbook. It begins with a conventional pursuit focusing on the algorithmic or numerical aspect of CEF such as computational efforts devoted to rational expectations, (dynamic) general equilibrium, and volatility. It then moves toward an automata- or organism-based perspective of CEF, involving nature-inspired intelligence, algorithmic trading, automated markets, network- and agent-based computing, and neural computing. As an alternative way to introduce this novel skeleton, the chapter starts with a view of computation or computing, addressing what computational economics intends to compute and what kinds of economics make computation so hard, and then it turns to a view of computing systems in which the Walrasian kind of computational economics is replaced by the Wolframian kind due to computational irreducibility.

Algorithms and Methods Inspired from Nature for Solving Supply Chain and Logistics Optimization Problems

2014 ◽  
Vol 4 (3) ◽  
pp. 26-51 ◽  
Author(s):  
Georgios Dounias ◽  
Vassilios Vassiliadis
Keyword(s):  
Supply Chain ◽  
Optimization Problems ◽  
Artificial Immune Systems ◽  
Supply Chain Optimization ◽  
Hybrid Schemes ◽  
Network Problems ◽  
Logistics Optimization ◽  
Team Orienteering ◽  
Nature Inspired Intelligence ◽  
High Degree

The current work surveys 245 papers and research reports related to algorithms and methods inspired from nature for solving supply chain and logistics optimization problems. Nature Inspired Intelligence (NII) is a challenging new subfield of artificial intelligence (AI) particularly capable of dealing with complex optimization problems. Related approaches are used either as stand-alone algorithms, or as hybrid schemes i.e. in combination to other AI techniques. Ant Colony Optimization (ACO), Particle Swarm Optimization, Artificial Bee Colonies, Artificial Immune Systems and DNA Computing are some of the most popular approaches belonging to nature inspired intelligence. On the other hand, supply chain management represents an interesting domain of OR applications, including a variety of hard optimization problems such as vehicle routing (VRP), travelling salesman (TSP), team orienteering, inventory, knapsack, supply network problems, etc. Nature inspired intelligent algorithms prove capable of identifying near optimal solutions for instances of those problems with high degree of complexity in a reasonable amount of time. Survey findings indicate that NII can cope successfully with almost any kind of supply chain optimization problem and tends to become a standard in related scientific literature during the last five years.

scholarly journals Special issue on developing nature-inspired intelligence by neural systems

2020 ◽  
Vol 32 (24) ◽  
pp. 17823-17824
Author(s):  
Carlos M. Travieso-González ◽  
Jesús B. Alonso-Hernández
Keyword(s):  
Neural Systems ◽  
Special Issue ◽  
Nature Inspired Intelligence

Nature-Inspired Intelligence in Supply Chain Management

2011 ◽  
pp. 1-31
Author(s):  
Vassilios Vassiliadis ◽  
Giorgos Dounias
Keyword(s):  
Artificial Intelligence ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Optimization Problems ◽  
Real Life ◽  
Ant Colonies ◽  
Chain Management ◽  
Complex Optimization ◽  
Nature Inspired Intelligence ◽  
Optimum Solution

Supply chain management is a vital process for the competitiveness and profitability of companies. Supply chain consists of a large and complex network of components such as suppliers, warehouses, customers etc. which are connected in almost every possible way. Companies’ main aim is to optimize the components of these complex networks to their benefit. This constitutes a challenging optimization problem and often, traditional mathematical approaches fail to overcome complexity and to converge to the optimum solution. More robust methods are required sometimes in order to yield to the optimal. The field of artificial intelligence offers a great variety of meta-heuristic techniques which specialize in solving such complex optimization problems, either accurately, or by obtaining a practically useful approximation, even if real time constraints are imposed. The aim of this chapter is to present a survey of the available literature, regarding the use of nature-inspired methodologies in supply chain management problems. Nature-inspired intelligence is a specific branch of artificial intelligence. Its unique characteristic is the algorithmic imitation of real life systems such as ant colonies, flock of birds etc. in order to solve complex problems.

Algorithms and Methods Inspired from Nature for Solving Supply Chain and Logistics Optimization Problems

2015 ◽  
pp. 245-275 ◽  
Author(s):  
Georgios Dounias ◽  
Vassilios Vassiliadis
Keyword(s):  
Supply Chain ◽  
Optimization Problems ◽  
Artificial Immune Systems ◽  
Supply Chain Optimization ◽  
Hybrid Schemes ◽  
Network Problems ◽  
Logistics Optimization ◽  
Nature Inspired Intelligence ◽  
High Degree ◽  
Bee Colonies

The current work surveys 245 papers and research reports related to algorithms and methods inspired from nature for solving supply chain and logistics optimization problems. Nature Inspired Intelligence (NII) is a challenging new subfield of artificial intelligence (AI) particularly capable of dealing with complex optimization problems. Related approaches are used either as stand-alone algorithms, or as hybrid schemes i.e. in combination to other AI techniques. Ant Colony Optimization (ACO), Particle Swarm Optimization, Artificial Bee Colonies, Artificial Immune Systems and DNA Computing are some of the most popular approaches belonging to nature inspired intelligence. On the other hand, supply chain management represents an interesting domain of OR applications, including a variety of hard optimization problems such as vehicle routing (VRP), travelling salesman (TSP), team orienteering, inventory, knapsack, supply network problems, etc. Nature inspired intelligent algorithms prove capable of identifying near optimal solutions for instances of those problems with high degree of complexity in a reasonable amount of time. Survey findings indicate that NII can cope successfully with almost any kind of supply chain optimization problem and tends to become a standard in related scientific literature during the last five years.

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