An efficient grouping genetic algorithm for U-shaped assembly line balancing problems with maximizing production rate

U-type assembly line is one of the important tools that may increase companies’ production efficiency. In this study, two different modeling approaches proposed for the assembly line balancing problems have been used in modeling type-II U-line balancing problems, and the performances of these models have been compared with each other. It has been shown that using mathematical formulations to solve medium and large size problem instances is impractical since the problem is NP-hard. Therefore, a grouping genetic and simulated annealing algorithms have been developed, and a particle swarm optimization algorithm is adapted to compare with the proposed methods. A special crossover operator that always obtains feasible offspring has been suggested for the proposed grouping genetic algorithm. Furthermore, a local search procedure based on problem-specific knowledge was applied to increase the intensification of the algorithm. A set of well-known benchmark instances was solved to evaluate the effectiveness of the proposed and existing methods. Results showed that while the mathematical formulations can only be used to solve small size instances, metaheuristics can obtain high quality solutions for all size problem instances within acceptable CPU times. Moreover, grouping genetic algorithm has been found to be superior to the other methods according to the number of optimal solutions, or deviations from the lower bound values. © 2017, Springer-Verlag GmbH Germany

Ergonomic risk and cycle time minimization for the U-shaped worker assignment assembly line balancing problem: A multi-objective approach

[EN] Workers still perform the bulk of operations in the manufacturing industry. The consideration of the assignment of workers and the reduction of ergonomic risks in U-shaped assembly lines is of paramount importance. However, the objectives of efficient task and worker assignment and a reduction in ergonomic risks are not usually correlated. Moreover, there is limited research in the existing literature into multi-objective approaches in U-shaped assembly lines. We formulate a U-shaped assembly worker assignment and balancing problem to simultaneously minimize cycle times and ergonomic risks. In addition, and due to its simplicity and successful results in flow shop scheduling problems, a Restarted Iterated Pareto Greedy algorithm is designed to optimize both objectives. In this algorithm, a problem-specific heuristic-based initialization is extended to improve the initial solution. Two precedence-based greedy and local search phases are developed to exploit the space around the current solution. Finally, a restart mechanism is proposed to help the algorithm escape from local optima. Comprehensive computational results, supported by detailed statistical analyses, suggest that the proposed multi-objective algorithm outperforms existing methods on a large number of benchmark instances.The authors would like to thank the anonymous reviewers for their helpful comments and constructive suggestions. This work is supported by National Natural Science Foundation of China (No. 51875421, No. 51875420). Ruben Ruiz is partly supported by the Spanish Ministry of Science, Innovation, and Universities, under the project "OPTEP-Port Terminal Operations Optimization"(No.\ RTI2018-094940-B-I00) financed with FEDER funds.Zhang, Z.; Tang, Q.; Ruiz García, R.; Zhang, L. (2020). 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