Mixed-model parallel two-sided assembly line balancing problem: A flexible agent-based ant colony optimization approach

• Mixed-model parallel two-sided assembly line balancing problem (MPTALBP) is studied.
• Agent based ant colony approach enhanced with 10 heuristics is developed.
• Ants have opportunity to randomly select one of those heuristic search behaviors.
• A new modified lower bound formulation is proposed for MPTALBP.
• Statistical tests prove the benefits of the proposed system and the algorithm.

Assembly lines are frequently used as a production method to assemble complex products. Two-sided assembly lines are utilized to assemble large-sized products (e.g., cars, buses, trucks). Locating two lines in parallel helps improve line efficiency by enabling collaboration between the line workers. This paper proposes a mixed-model parallel two-sided assembly line system that can be utilized to produce large-sized items in an inter-mixed sequence. The mixed-model parallel two-sided line balancing problem is defined and the advantages of utilizing multi-line stations across the lines are discussed. A flexible agent-based ant colony optimization algorithm is developed to solve the problem and a numerical example is given to explain the method systematically. The proposed algorithm builds flexible balancing solutions suitable for any model sequence launched. The dynamically changing workloads of workstations (based on specific product models during the production process) are also explored. A comprehensive experimental study is conducted and the results are statistically analyzed using the well-known paired sample t-test. The test results indicate that the mixed-model parallel two-sided assembly line system reduces the workforce need in comparison with separately balanced mixed-model two-sided lines. It is also shown that the proposed algorithm outperforms the tabu search algorithm and six heuristics often used in the assembly line balancing domain.

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