Abstract
This chapter provides an introduction to Computational Intelligence (CI). Artificial Intelligence (AI) and CI are briefly compared. CI by itself is an umbrella term covering different branches of methods most of them following the paradigm “nature-inspired”. While CI and AI partly overlap, the methods applied in CI benefit from nature-inspired strategies and implement them as computational algorithms. Mathematical optimization is shortly explained. CI comprises five main branches: Evolutionary Computation (EC), Swarm Intelligence (SI), Neural Networks, Fuzzy Logic, and Artificial Immune Systems. A focus is laid on EC and SI as the most prominent CI methods used in logistics and supply chain management. EC is coupled with Evolutionary Algorithms (EA). Methods belonging to EC respectively EA are Evolution Strategy, Genetic Algorithm (GA), Genetic and Evolutionary Programming, the multiobjective variants Non-dominated Sorting GA (NSGA) and Strength Pareto EA (SPEA), Memetic Algorithms, and further methods. The most important methods belonging to SI are Particle Swarm Optimization (PSO), Discrete PSO and Ant Colony Optimization. EA and SI approaches are also attributed to the class of metaheuristics which use general problem-solving concepts for problem solution during their search for better solutions in a wide range of application domains.
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Hanne, T., Dornberger, R. (2017). Computational Intelligence. In: Computational Intelligence in Logistics and Supply Chain Management. International Series in Operations Research & Management Science, vol 244. Springer, Cham. https://doi.org/10.1007/978-3-319-40722-7_2
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