Assembly and Disassembly Planning by using Fuzzy Logic & Genetic Algorithms

TL;DR

This paper introduces a hybrid Fuzzy Logic-Genetic Algorithm approach for automated assembly and disassembly planning, dynamically optimizing parameters and sequences to improve process efficiency.

Contribution

It presents a novel two-level hybrid methodology combining fuzzy control with genetic algorithms for assembly/disassembly sequence planning.

Findings

Effective dynamic adaptation of GA parameters using fuzzy control.

Successful application to two case studies demonstrating improved planning.

Enhanced control of technological knowledge in assembly/disassembly processes.

Abstract

The authors propose the implementation of hybrid Fuzzy Logic-Genetic Algorithm (FL-GA) methodology to plan the automatic assembly and disassembly sequence of products. The GA-Fuzzy Logic approach is implemented onto two levels. The first level of hybridization consists of the development of a Fuzzy controller for the parameters of an assembly or disassembly planner based on GAs. This controller acts on mutation probability and crossover rate in order to adapt their values dynamically while the algorithm runs. The second level consists of the identification of theoptimal assembly or disassembly sequence by a Fuzzy function, in order to obtain a closer control of the technological knowledge of the assembly/disassembly process. Two case studies were analyzed in order to test the efficiency of the Fuzzy-GA methodologies.