Research on distributed scheduling of mechanical job shop based on hybrid differential evolution

[1] Zandieh, M., Khatami, A. R., & Rahmati, S. (2017). Flexible job shop scheduling under condition-based maintenance: improved version of imperialist competitive algorithm. Applied Soft Computing, S1568494617302429. Search in Google Scholar

[2] Zhang, W., Wen, J. B., Zhu, Y. C., & Hu, Y. (2017). Multi-objective scheduling simulation of flexible job-shop based on multi-population genetic algorithm. International Journal of Simulation Modelling, 16(2), 313-321. Search in Google Scholar

[3] Gholami, Omid, Sotskov, Yuri, & N. (2017). Mixed graph model and algorithms for parallel-machine job-shop scheduling problems. International Journal of Production Research. Search in Google Scholar

[4] Sang, Y., & Tan, J. (2022). Many-objective flexible job shop scheduling problem with green consideration. Energies, 15. Search in Google Scholar

[5] Xie, J., Li, X., Gao, L., & Gui, L. (2022). A hybrid algorithm with a new neighborhood structure for job shop scheduling problems. Computers & Industrial Engineering (Pt.2), 169. Search in Google Scholar

[6] Hu, R., Wu, X., Qian, B., Mao, J., & Jin, H. (2022). Differential evolution algorithm combined with uncertainty handling techniques for stochastic reentrant job shop scheduling problem. Complexity. Search in Google Scholar

[7] Ahmadian, Mohammad MahdiSalehipour, AmirCheng, T. C. E. (2021). A meta-heuristic to solve the justin-time job-shop scheduling problem. European Journal of Operational Research, 288(1). Search in Google Scholar

[8] Zhang, F., Mei, Y., Su, N., Zhang, M., & Tan, K. C. (2021). Surrogate-assisted evolutionary multitask genetic programming for dynamic flexible job shop scheduling. IEEE Transactions on Evolutionary Computation, PP (99), 1-1. Search in Google Scholar

[9] Zhang, M., Tao, F., & Nee, A. (2020). Digital twin enhanced dynamic job-shop scheduling. Journal of Manufacturing Systems. Search in Google Scholar

[10] Zhang, X., Ji, Z., & Wang, Y. (2019). An improved sfla for flexible job shop scheduling problem considering energy consumption. Modern Physics Letters B. Search in Google Scholar

[11] Wu, X., & Wu, S. (2017). An elitist quantum-inspired evolutionary algorithm for the flexible job-shop scheduling problem. Journal of Intelligent Manufacturing, 28(6), 1441-1457. Search in Google Scholar

[12] Bierwirth, C., & Kuhpfahl, J. (2017). Extended grasp for the job shop scheduling problem with total weighted tardiness objective. European Journal of Operational Research, S0377221717302345. Search in Google Scholar

[13] Laarhoven, P. V., Aarts, E., & Lenstra, J. K. (2017). Job shop scheduling by simulated annealing. Operations Research, 40(1), 113-125. Search in Google Scholar

[14] Vaessens, R., Aarts, E., & Lenstra, J. K. (2017). Job shop scheduling by local search. Informs Journal on Computing, 8(10), 302-317. Search in Google Scholar

[15] P., & Mellor. (2017). A review of job shop scheduling. Journal of the Operational Research Society, 17(2), 161-171. Search in Google Scholar

[16] Rui, Z., & Cheng, W. (2017). A simulated annealing algorithm based on block properties for the job shop scheduling problem with total weighted tardinessobjective. Computers & Operations Research, 38(5), 854-867. Search in Google Scholar

[17] Chao, L., Li, X., Liang, G., Wei, L., & Jin, Y. (2017). An effective multi-objective discrete virus optimization algorithm for flexible job-shop scheduling problem with controllable processing times. Computers & Industrial Engineering, 104(FEB.), 156-174. Search in Google Scholar

[18] Lei, Deming, Zheng, Youlian, Guo, & Xiuping. (2017). A shuffled frog-leaping algorithm for flexible job shop scheduling with the consideration of energy consumption. International Journal of Production Research. Search in Google Scholar

[19] Escamilla, J., Salido, M. A., Giret, A., & Barber, F. (2017). A metaheuristic technique for energy-efficiency in job-shop scheduling. The Knowledge Engineering Review, 31(5), 1-11. Search in Google Scholar