Metaheuristic methods for solving Operations Management Problems

The present work introduces the reader to the basic concepts behind the metaheuristics methods and tries to demonstrate the importance of these techniques to the field of Operations Management. The first chapter presents a description of ten of the most popular metaheuristics methods: Tabu Search, Simulated Annealing, Iterated Local Search, Greedy Randomized Adaptive Search Procedure, Variable Neighborhood Search, Genetic Algorithms, Scatter Search, Ant Colony Optimization, Guided Local Search and Honey Bee Swarms Algorithm. It also presents several successful practical applications for each of these techniques. The second chapter discusses how the metaheuristics have been classified in the literature, making the distinction between Pure Metaheuristics and Hybrid Metaheuristics. The third section presents a practical experiment where the Artificial Bee Colony algorithm has been modified to address binary problems and used to solve the Knapsack Problem. The proposed algorithm was tested on 54 randomly generated problems divided into six instances presenting promising results. Further directions are presented to continue with the development of the algorithm