Production scheduling using multi-objective optimization and cluster approaches

Abstract

Production scheduling is a crucial task in the manufacturing process. In this way, the managers need to make decisions about the jobs production schedule. However, this task is not simple to perform, often requiring complex software tools and specialized algorithms to find the optimal solution. This work considers a multi-objective optimization algorithm to explore the production scheduling performance measure in order to help managers in decision making related to jobs attribution in a set of parallel machines. For this, five important production scheduling performance measures (makespan, tardiness and earliness time, number of tardy and early jobs) were combined into three objective functions and the Pareto front generated was analyzed by cluster techniques. The results presented different combinations to optimize the production process, providing to the manager different possibilities to prioritize the objectives considered.