Hybrid Model of Fuzzy Logic and Genetic Algorithm for Product Assembly Sequence Optimization

Abstract

The sequence of product assembly affects the efficiency and effectiveness of production because it reduces cycle time and production costs and reduces production errors. The application of artificial intelligence is growing to optimize the problem of assembly sequences of components or products, including genetic algorithms and fuzzy logic. These two models can complement each other to produce the best assembly sequence. This research consists of several stages: model formulation, model analysis, solution, and model verification and validation. A hybrid fuzzy and genetic algorithms model can optimize product assembly sequences more effectively and efficiently. Fuzzy logic can help determine the variables that must be optimized, while genetic algorithms can help find the optimal solution by combining these variables. Experiments using hybrid fuzzy logic and genetic algorithms to minimize assembly cycle time have resulted in product part assembly sequences that accommodate all geometric constraints, including assistive devices.