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 the optimal 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.
Highlights
The rapid development of new products has shortened product time-to-market and shelf-life, increasing the quantity of wasted used goods
In this paper the authors propose a hybrid Fuzzy Logic– Genetic Algorithm approach to implement the automatic generation of optimal assembly and disassembly sequences
The aim of assembly planning is the identification of the optimal assembly sequences of products constituted of several parts, whereas disassembly planning is very important during product maintenance or end-of-life applications
Summary
The rapid development of new products has shortened product time-to-market and shelf-life, increasing the quantity of wasted used goods. In this context, all these factors must be considered during the design stage of a product. In this paper the authors propose a hybrid Fuzzy Logic– Genetic Algorithm approach to implement the automatic generation of optimal assembly and disassembly sequences. The aim of this methodology is the efficient generation of these sequences while preserving the flexibility to operate with a great variety of industrial products and assembly/disassembly environments
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