Hybrid method with CS and BRKGA applied to the minimization of tool switches problem

• The CS+BRKGA is a hybrid method that detects promising areas and applies local search in these areas.
• We simplify the clustering process of the CS based on the concept of random keys.
• The results show that the CS+BRKGA is competitive for solving the MTSP.

The minimization of tool switches problem (MTSP) seeks a sequence to process a set of jobs so that the number of tool switches required is minimized. The MTSP is well known to be NP-hard. This paper presents a new hybrid heuristic based on the Biased Random Key Genetic Algorithm (BRKGA) and the Clustering Search (CS). The main idea of CS is to identify promising regions of the search space by generating solutions with a metaheuristic, such as BRKGA, and clustering them to be further explored with local search heuristics. The distinctive feature of the proposed method is to simplify this clustering process. Computational results for the MTSP considering instances available in the literature are presented to demonstrate the efficacy of the CS with BRKGA.