Soft computing Application in Supply Chain Optimization

In recent years, increasing competitiveness that is caused by market globalization has gained importance the supply chain optimization. So different modeling and solving methods have been introduced by scholars to optimize supply chain problems and find optimized solutions. As a result, all possible combinations of the decisions and variables must be explored to find the optimum solution. The time required to solve the problem becomes extremely long as the number of variables increase to more than hundreds. Soft computing algorithms have been developed to overcome the problem that most of them are inspired from nature. Some of the algorithms have been inspired from natural generation (called evolutionary algorithms), some them inspired from swarm behavior (called swarm intelligence algorithms), and others simulate natural processes. In this paper, soft computing algorithms that they are widely being recognized as one of the best approaches for solving combinatorial problems and models will be presented. Then, soft computing basic and their applications in supply chain's complicated optimization problem will be discussed.

کلیدواژه ها:

soft computing ، Meta-heuristic algorithms ، supply chain optimization ، NP-hard problems