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• W4286566636 endingPage "109341" @default.
• W4286566636 startingPage "109341" @default.
• W4286566636 abstract "Considering preventive maintenance (PM) scenarios in assembly line balancing (ALB) problem has been proven to be an effective way to promote line efficiency. However, previous research only focused on the single-model assembly line and ignored the mixed-model assembly line, resulting in the line cannot meet the growing customization needs of customers. This paper addresses the mixed-model ALB considering PM scenarios (MALB_PMs). A mixed-integer mathematical formulation is proposed to optimize the cycle time and task alteration. The cooperative co-evolutionary algorithm is introduced to simplify the large-scaled cases via the powerful divide-and-conquer architecture and is enhanced with four improvements. An archive is generated to save complete solutions with better performance and evaluate the fitness of solutions. A mixed-model variable step-size decoding method is designed to speed up the decoding process of the algorithm. One inter-population crossover operator is designed to enhance the communication among sub-problems. Four objective-oriented neighbor search operators are proposed to promote the convergence performance of the algorithm. Finally, three small-scale instances are employed to illustrate the application of the mathematical model, and 39 cases are designed to test the performance of the developed algorithm. Experiment results demonstrate that the proposed mathematical formulation can obtain the Pareto solutions of small-scaled instances; The developed algorithm outperforms other algorithms on three criteria; The Pareto front obtained by this algorithm is closer to the True Pareto front." @default.
• W4286566636 created "2022-07-22" @default.
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• W4286566636 date "2022-09-01" @default.
• W4286566636 modified "2023-10-14" @default.
• W4286566636 title "Mixed-model assembly line balancing problem considering preventive maintenance scenarios: MILP model and cooperative co-evolutionary algorithm" @default.
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• W4286566636 doi "https://doi.org/10.1016/j.asoc.2022.109341" @default.
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