Production scheduling is a crucial task in the manufacturing process. In this way, the managers must decide
the job's production schedule. However, this task is not simple, often requiring complex software tools and
specialized algorithms to find the optimal solution. In this work, a multi-objective optimization model was
developed to explore production scheduling performance measures to help managers in decision-making
related to job attribution under three simulations of parallel machine scenarios. Five important production
scheduling performance measures were considered (makespan, tardiness and earliness times, number of
tardy and early jobs), and combined into three objective functions. To solve the scheduling problem, three
multi-objective evolutionary algorithms are considered (Multi-objective Particle Swarm Optimization,
Multi-objective Grey Wolf Algorithm, and, Non-dominated Sorting Genetic Algorithm II), and the set of
optimum solutions named Pareto Front, provided by each one is compared in terms of dominance, generating
a new Pareto Front, denoted as Final Pareto Front. Furthermore, this Final Pareto Front is analysed through
an automatic bio-inspired clustering algorithm based on the Genetic Algorithm. The results demonstrated
that the proposed approach efficiently solves the scheduling problem considered. In addition, the proposed
methodology provided more robust solutions by combining different bio-inspired multi-objective
techniques. Furthermore, the cluster analysis proved fundamental for a better understanding of the results
and support for choosing the final optimum solution.
