Solving the Job Sequencing and Tool Switching Problem with an Iterated Local Search Based on Random Keys

Solving practical problems in combinatorial optimization often encounters computational intractability, requiring the use of meta-heuristics to find solutions close to the optimum within feasible time frames. Among these, there are those based on biological processes in nature, such as the genetic algorithm. In a genetic algorithm, one way to encode the problem solution is to use random keys, allowing the search for the solution to be indirectly conducted in the space of random keys. This approach enables the implementation to be divided into parts that are dependent and independent of the problem. Recently, this promising approach has been extended to other meta-heuristics. In this context, the objective of this work is to solve the Job Sequencing and Tool Switching Problem with an iterated local search based on random keys. The results showed the effectiveness of this alternative approach compared to the usual one in terms of computational time and robustness.