[1] Chen Y, Jiang Z (2022) Multi-AGVs scheduling with vehicle conflict consideration in ship outfitting Items warehouse. Journal of Shanghai Jiao Tong University (Science) 22: 1–15. https://doi.org/10.1007/s12204-022-2561-z
[2] Chiu Y, Shih CJ (2012) Rescheduling strategies for integrating rush orders with preventive maintenance in a two-machine flow shop. International Journal of Production Research 50(20): 5783–5794. https://doi.org/10.1080/00207543.2011.627887
[3] Custodio L, Machado R (2020) Flexible automated warehouse: a literature review and an innovative framework. International Journal of Advanced Manufacturing Technology 106: 533–558. https://doi.org/10.1007/s00170-019-04588-z
[4] Dang QV, Singh N, Adan I, Martagan T, Sande D (2021) Scheduling heterogeneous multi-load AGVs with battery constraints. Computers & Operations Research 136: 105517. https://doi.org/10.1016/j.cor.2021.105517
[5] Deng Y, Chen Y, Zhang Y, Mahadevan S (2012) Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment. Applied Soft Computing 12(3): 1231–1237. https://doi.org/10.1016/j.asoc.2011.11.011
[6] Fazlollahtabar H, Saidi-Mehrabad M, Balakrishnan J (2015) Integrated Markov-neural reliability computation method: A case for multiple automated guided vehicle system. Reliability Engineering & System Safety 135: 34–44. https://doi.org/10.1016/j.ress.2014.11.004
[7] Fu JL, Zhang HZ, Zhang J, Jiang LK (2020) Review on AGV scheduling optimization. Journal of System Simulation 32(9): 1664–1675. https://doi.org/10.16182/j.issn1004731x.joss.19-0042
[8] Giglio D (2014) Task scheduling for multiple forklift AGVs in distribution warehouses. Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA), Barcelona, 1–6. https://doi.org/10.1109/ETFA.2014.7005360
[9] Li MW, Xu DY, Geng J, Hong WC (2022a) A hybrid approach for forecasting ship motion using CNN-GRU-AM and GCWOA. Applied Soft Computing 114: 108084. https://doi.org/10.1016/j.asoc.2021.108084
[10] Li MW, Xu DY, Geng J, Hong WC (2022b) A ship motion forecasting approach based on empirical mode decomposition method hybrid deep learning network and quantum butterfly optimization algorithm. Nonlinear Dynamics 107(3): 2447–2467. https://doi.org/10.1007/s11071-021-07139-y
[11] Li R, Liu YJ, Hamada K (2010) Research on the ITOC based scheduling system for ship piping production. Journal of Marine Science and Application 9(4): 355–362. https://doi.org/10.1007/s11804-010-1020-7
[12] Majdzik P, Witczak M, Lipiec B, Banaszak Z (2022) Integrated fault-tolerant control of assembly and automated guided vehicle-based transportation layers. International Journal of Computer Integrated Manufacturing, 35(4–5), 409–426. https://doi.org/10.1080/0951192X.2021.1872103
[13] Mousavi M, Yap HJ, Musa SN, Dawal SZM (2017) A fuzzy hybrid GA-PSO algorithm for multi-objective AGV scheduling in FMS. International Journal of Simulation Modelling 16(1): 58–71. https://doi.org/10.2507/IJSIMM16(1)5.368
[14] Umar UA, Ariffin MK, Ismail N, Tang SH (2015) Hybrid multi-objective genetic algorithms for integrated dynamic scheduling and routing of jobs and automated-guided vehicle (AGV) in flexible manufacturing systems (FMS) environment. The International Journal of Advanced Manufacturing Technology 81(9–12): 2123–2141. https://doi.org/10.1007/s00170-015-7329-2
[15] Vivaldini K, Rocha LF, Martarelli NJ, Becker M, Moreira AP (2016) Integrated tasks assignment and routing for the estimation of the optimal number of AGVS. International Journal of Advanced Manufacturing Technology 82: 719–736. https://doi.org/10.1007/s00170-015-7343-4
[16] Witczak M, Majdzik P, Stetter R, Lipiec B (2020) A fault-tolerant control strategy for multiple automated guided vehicles. Journal of Manufacturing Systems 55: 56–68. https://doi.org/10.1016/j.jmsy.2020.02.009
[17] Wang J, Pan J, Huo J, Wang R, Li L, Nian T (2021) Research on optimization of multi-AGV path based on genetic algorithm considering charge utilization. Journal of Physics: Conference Series 1769(1): 012052. https://doi.org/10.1088/1742-6596/1769/1/012052
[18] Wu B, Chi X, Zhao C, Zhang W, Lu Y, Jiang D (2022) Dynamic path planning for forklift AGV based on smoothing A* and improved DWA hybrid algorithm. Sensors 22(18): 7079. https://doi.org/10.3390/s22187079
[19] Xiao Y, Zhao Q, Kaku I, Xu Y (2012) Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Computers & Operations Research 39(7): 1419–1431. https://doi.org/10.1016/j.cor.2011.08.013
[20] Yan R, Dunnett SJ, Jackson LM (2022) Model-based research for aiding decision-making during the design and operation of multi-load automated guided vehicle systems. Reliability Engineering & System Safety 219: 108264. https://doi.org/10.1016/j.ress.2021.108264
[21] Yan R, Jackson LM, Dunnett SJ (2017) Automated guided vehicle mission reliability modelling using a combined fault tree and Petri net approach. The International Journal of Advanced Manufacturing Technology 92: 1825–1837. https://doi.org/10.1007/s00170-017-0175-7
[22] Zacharia PT, Xidias EK (2020) AGV routing and motion planning in a flexible manufacturing system using a fuzzy-based genetic algorithm. The International Journal of Advanced Manufacturing Technology 109: 1801–1813. https://doi.org/10.1007/s00170-020-05755-3
[23] Zhang MJ, Zheng JX, Zhang J (2002) Selection method of multi-objective problems using genetic algorithm in motion plan of AUV. Journal of Marine Science and Application 1(1): 81–86. https://doi.org/10.1007/BF02921423
[24] Zhen L, Wu YW, Zhang S, Sun QJ, Yue Q (2020) A decision framework for automatic guided vehicle routing problem with traffic congestions. Journal of the Operations Research Society of China 8(3): 357–373. https://doi.org/10.1007/s40305-018-0216-4
[25] Zou WQ, Pan QK, Wang L, Miao ZH, Peng C (2022) Efficient multiobjective optimization for an AGV energy-efficient scheduling problem with release time. Knowledge-Based Systems 242: 108334. https://doi.org/10.1016/j.knosys.2022.108334