Comparing extended neighborhood search techniques applied to production scheduling

Abstract

Extended Neighborhood Search Techniques (ENST) are meta-heuristics that are adequate tools for solving optimization problems, through the selection of the best solution among a finite number of possible solutions. They are particularly attractive techniques to solve optimization problems, namely scheduling problems, because they allow finding close to optimal solutions, without big computational effort, which in real-world environments is usually good-enough. Moreover, they are relatively simple to implement and manipulate. In this paper, some interesting features of these kind of methods are enhanced, including an application example of implemented algorithms, which are described and illustrated. Among several advantages the possibility of repeating a huge number of experiences is referred. This feature facilitates a comparative analysis of the results obtained for different program executions for an analyzed problem. Furthermore, the parameters that control the algorithms are also discussed to show how easily they can be implemented and manipulated in order to more closely adapt for satisfying more specific requirements of problems arising on different and quite complex manufacturing scenarios.