Solving mixed-model two-sided u-type assembly line balancing problem using a hybrid GA-VNS

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DELİCE Y.

International Journal of Industrial Engineering Computations, cilt.17, sa.3, ss.1149-1162, 2026 (SCI-Expanded, Scopus)

Özet

This study investigates the mixed-model two-sided U-type assembly line balancing (MMTsUtALB) problem and proposes a hybrid solution approach based on Genetic Algorithm (GA) and Variable Neighborhood Search (VNS). Unlike existing studies on two-sided U-type assembly line balancing (TsUtALB), the mixed-model structure is explicitly considered. In the proposed approach, GA is used to explore the solution space through evolutionary operators, while VNS is applied as a local improvement procedure to refine promising solutions and improve convergence. A problem-oriented priority rule–based encoding method is adopted to represent solutions, which are transformed into feasible two-sided U-type assembly line configurations using a decoding-based task assignment procedure. This structure allows the algorithm to balance diversification and intensification during the search process. The effectiveness of the GA–VNS hybrid algorithm is evaluated using benchmark test problems. Since this study represents the first attempt to solve the MMTsUtALB problem, the obtained results are compared with closely related mixed-model two-sided assembly line balancing studies. Computational results show that GA– VNS achieves competitive performance, particularly for larger instances, by reducing the number of stations and positions with lower computational times than SA and PSO.