Intelligent supply chains

Firms are seeking competitive advantages through supply chain management to stay competitive in today’s global market. Computational intelligence approaches can offer effective tools for both modeling and managing operations in the uncertain environment of the supply chain, especially since the associated computational techniques are capable of handling complex interdependencies.

However, traditional mathematical methods have proven insufficient in tackling the requirements rising from the development of market competition. Nature-inspired intelligent techniques are considered to be quite efficient in handling hard problems.

Some examples of nature-inspired algorithms are:
*Ant Colony Optimization (ACO)
*Particle Swarm Optimization (PSO)
*Genetic Algorithms
*Genetic Programming
*Memetic Algorithms
*Artificial Immune Systems
*DNA Computing

Ant Colony Optimization (ACO) was proposed by Marco Dorigo in the early 1990. In ACO, the individuals are named ‘ants’, which try to find the shortest way from their colony, the starting point, to a so-called food source through a graph of possible ways in the search space.

The Particle Swarm Optimization algorithm was proposed by Kennedy and Eberhart to simulate the social behavior of social organism such as bird flocking and fish schooling. The implementation is easy and to gives good results, especially in problems with continuous variables.
Intelligent supply chains