Otimização com aprendizado autônomo para programação da produção com sequências de instâncias heterogêneas

Abstract

Many scheduling problems, but also in Advanced Planning and Scheduling (APS), are NP-Hard. This project addresses a scheduling problem in a single machine environment with sequence dependent setup times. It is possible to partially outsource the demand restricting that there are no delays for delivering of the orders, aiming to minimize the cost for outsourcing. Evolutionary Algorithms (EA) represent a fast solution strategy for NP-Hard problems. However, researchers in the field of evolutionary computation say that EAs depend significantly on the configuration of their parameters. This work investigates in the scheduling and APS literature, how the authors who develop EAs determines the parameterization and evaluation of their algorithms, also presenting which strategies are suggested as state of the art for automatic tuning of EAs. This thesis states an innovative strategy for automated configuration of EAs, including a new paradigm for optimization in streams of heterogeneous instances called ALO (Autonomous Learning Optimization). This new paradigm aims to solve integratelly an optimization problem and parameterization of the configurable algorithm with an autonomous decision process for detecting heterogeneities within the sequence of instances.