Distance based NSGA-II for earliness and tardiness minimization in Parallel Machine Scheduling

This work investigates the conjecture that the employment of geometry-based operators can be worthy for the construction of efficient and stable evolutionary algorithms for scheduling problems in parallel machines. The effort reported here is an instance of a broader study which investigates the role of geometric concepts in combinatorial problems. This work considers multiobjective versions of the scheduling problem, which are solved using a variation of the classical NSGA-II with the geometric operators. The achieved results are encouraging, showing a repeatable good performance in several problem instances of different sizes, for both identical and unrelated machines. It is also shown some empirical evidence that the proposed geometric operators are able to regularize the space, which is a possible explanation for the algorithm good performance.