How to cite this paper
Xu, G., Guan, Z., Peng, K & Yue, L. (2023). Collaborative scheduling of machining-assembly in complex multiple parallel production lines environment considering kitting constraints.International Journal of Industrial Engineering Computations , 14(4), 749-766.
Refrences
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Kuo, I. H., Horng, S. J., Kao, T. W., Lin, T. L., & Pan, Y. (2007). An efficient flow-shop scheduling algorithm based on a hybrid particle swarm optimization model. Expert Systems with Applications, 36(3), 7027-7032.
Li, H., & Zhang, Q. (2008). Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE transactions on evolutionary computation, 13(2), 284-302.
Lu, C., Gao, L., Li, X., & Xiao, S. (2017). A hybrid multi-objective grey wolf optimizer for dynamic scheduling in a real-world welding industry. Engineering Applications of Artificial Intelligence, 57, 61-79.
Mazdeh, M. M., Sarhadi, M., & Hindi, K. S. (2008). A branch-and-bound algorithm for single-machine scheduling with batch delivery and job release times. Computers & Operations Research, 35(4), 1099-1111.
Meng, R., Rao, Y., Zheng, Y., & Qi, D. (2017). Modelling and solving algorithm for two-stage scheduling of construction component manufacturing with machining and welding process. International Journal of Production Research(10), 1-13.
Pan, Q. K., Wang, L., Gao, L., & Li, W. D. (2011). An effective hybrid discrete differential evolution algorithm for the flow shop scheduling with intermediate buffers. Information Sciences, 181(3), 668-685.
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Sun, B., Zhai, G., Li, S., & Pei, B. (2023). Multi-resource collaborative scheduling problem of automated terminal considering the AGV charging effect under COVID-19. Ocean & coastal management, 232, 106422. ://MEDLINE:36407122
Ullrich, & Christian, A. (2013). Integrated machine scheduling and vehicle routing with time windows. European Journal of Operational Research, 227(1), 152-165.
Wang, L., Pan, Q. K., & Tasgetiren, M. F. (2011). A hybrid harmony search algorithm for the blocking permutation flow shop scheduling problem. Computers & Industrial Engineering, 61(1), 76-83.
Yang, Z., Ma, Z., & Wu, S. (2016). Optimized flowshop scheduling of multiple production lines for precast production. Automation in Construction, 72, 321-329.
Yue, L., Guan, Z., Zhang, L., Ullah, S., & Cui, Y. (2019). Multi objective lotsizing and scheduling with material constraints in flexible parallel lines using a Pareto based guided artificial bee colony algorithm. Computers & Industrial Engineering, 128(FEB.), 659-680.
Zhang, Q., & Hui, L. (2008). MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712-731.
Zhang, Q., Hui, L., Maringer, D., & Tsang, E. (2010). MOEA/D with NBI-style Tchebycheff approach for portfolio management. Paper presented at the Evolutionary Computation.
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., & da Fonseca, V. G. (2003). Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions On Evolutionary Computation, 7(2), 117-132.
Bhatnagar, R., Chandra, P., & Goyal, S. K. (1993). Models for multi-plant coordination. European Journal of Operational Research, 67(2), 141-160.
Chao, Lu, Liang, Gao, Xinyu, Li, et al. (2018). A multi-objective approach to welding shop scheduling for makespan, noise pollution and energy consumption. Journal of Cleaner Production.
Coello, C., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256-279.
Deb, K., & Jain, H. (2014). An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577-601.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.
Deng, J., & Wang, L. (2017). A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem. Swarm and Evolutionary Computation, 32, 121-131.
Hall, N. G., & Potts, C. N. (2003). Supply chain scheduling: batching and delivery. Operations Research, 51(4).
Hall, N. G., & Potts, C. N. (2005). The Coordination of Scheduling and Batch Deliveries. Annals of operations research, 135.
Ham, J. I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega.
Hao, L., Bing, D., Huang, G. Q., Chen, H., & Li, X. (2013). Hybrid flow shop scheduling considering machine electricity consumption cost. International Journal Of Production Economics, 146(2), 423–439.
Jiang, S., & Yang, S. (2017). A strength pareto evolutionary algorithm based on reference direction for multi-objective and many-objective optimization. IEEE Transactions on Evolutionary Computation, 329-346.
Jiang, S. W., Ong, Y. S., Zhang, J., & Feng, L. (2014). Consistencies and Contradictions of Performance Metrics in Multiobjective Optimization. IEEE Transactions on Cybernetics, 44(12), 2391-2404.
Kuo, I. H., Horng, S. J., Kao, T. W., Lin, T. L., & Pan, Y. (2007). An efficient flow-shop scheduling algorithm based on a hybrid particle swarm optimization model. Expert Systems with Applications, 36(3), 7027-7032.
Li, H., & Zhang, Q. (2008). Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE transactions on evolutionary computation, 13(2), 284-302.
Lu, C., Gao, L., Li, X., & Xiao, S. (2017). A hybrid multi-objective grey wolf optimizer for dynamic scheduling in a real-world welding industry. Engineering Applications of Artificial Intelligence, 57, 61-79.
Mazdeh, M. M., Sarhadi, M., & Hindi, K. S. (2008). A branch-and-bound algorithm for single-machine scheduling with batch delivery and job release times. Computers & Operations Research, 35(4), 1099-1111.
Meng, R., Rao, Y., Zheng, Y., & Qi, D. (2017). Modelling and solving algorithm for two-stage scheduling of construction component manufacturing with machining and welding process. International Journal of Production Research(10), 1-13.
Pan, Q. K., Wang, L., Gao, L., & Li, W. D. (2011). An effective hybrid discrete differential evolution algorithm for the flow shop scheduling with intermediate buffers. Information Sciences, 181(3), 668-685.
Rahnamayan, S., Tizhoosh, H. R., & Salama, M. (2007). A novel population initialization method for accelerating evolutionary algorithms. Computers & Mathematics with Applications, 53(10), 1605-1614.
Sun, B., Zhai, G., Li, S., & Pei, B. (2023). Multi-resource collaborative scheduling problem of automated terminal considering the AGV charging effect under COVID-19. Ocean & coastal management, 232, 106422.
Ullrich, & Christian, A. (2013). Integrated machine scheduling and vehicle routing with time windows. European Journal of Operational Research, 227(1), 152-165.
Wang, L., Pan, Q. K., & Tasgetiren, M. F. (2011). A hybrid harmony search algorithm for the blocking permutation flow shop scheduling problem. Computers & Industrial Engineering, 61(1), 76-83.
Yang, Z., Ma, Z., & Wu, S. (2016). Optimized flowshop scheduling of multiple production lines for precast production. Automation in Construction, 72, 321-329.
Yue, L., Guan, Z., Zhang, L., Ullah, S., & Cui, Y. (2019). Multi objective lotsizing and scheduling with material constraints in flexible parallel lines using a Pareto based guided artificial bee colony algorithm. Computers & Industrial Engineering, 128(FEB.), 659-680.
Zhang, Q., & Hui, L. (2008). MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712-731.
Zhang, Q., Hui, L., Maringer, D., & Tsang, E. (2010). MOEA/D with NBI-style Tchebycheff approach for portfolio management. Paper presented at the Evolutionary Computation.
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., & da Fonseca, V. G. (2003). Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions On Evolutionary Computation, 7(2), 117-132.