A genetic algorithm based approach for simultaneously solving U-shape mixed-model assembly line balancing and sequencing problem

Two important problems occur routinely on mixed-model production lines, regardless of whether the lines are traditional or U-shaped. The first one is the problem of how to assign tasks to stations on the line and the second one is the problem of selecting the order or sequence in which different models will be produced.In this thesis, a Priority-Based Genetic Algorithm (PGA) based solution approach is proposed in order to overcome implementation difficulties of the mixed-model U-shape assembly line balancing/sequencing problem (MMUL/BS) simultaneously. In proposed algorithm, Simulated Annealing (SA) algorithm based fitness evaluation approach is developed for being able to make fitness function calculations easily and effectively. In proposed approach, new neighborhood generation logic is developed in order to handle line balancing and model sequencing problems simultaneously. The proposed PGA based algorithm is able to address some particular features of the assembly process very common in real mixed-model assembly lines such as use of parallel workstations, zoning constraints. Parallel work stations and zoning constraints have not been used together in MMUL/BS solution so far.Moreover, new fitness function is developed. New fitness function minimizes the number of stations as primary objective, and ensures the workload balance within and between workstations at the end of cycles as secondary objective.Eventually, performance of the proposed approach is tested through a set of test problems with generated minimum part sets.