Approach for Scheduling Automatic Guided Vehicles Considering Equipment Failure and Power Management

Guoliang Fan , Zuhua Jiang

Journal of Marine Science and Application ›› 2023, Vol. 22 ›› Issue (3) : 624 -635.

PDF
Journal of Marine Science and Application ›› 2023, Vol. 22 ›› Issue (3) : 624 -635. DOI: 10.1007/s11804-023-00357-3
Research Article

Approach for Scheduling Automatic Guided Vehicles Considering Equipment Failure and Power Management

Author information +
History +
PDF

Abstract

Intermediate charging and sudden failure of automatic guided vehicles (AGVs) interrupt and severely affect the stability and efficiency of scheduling. Therefore, an AGV scheduling approach considering equipment failure and power management is proposed for outfitting warehouses. First, a power consumption model is established for AGVs performing transportation tasks. The powers for departure and task consumption are used to calculate the AGV charging and return times. Second, an optimization model for AGV scheduling is established to minimize the total transportation time. Different conditions are defined for the overhaul and minor repair of AGVs, and a scheduling strategy for responding to sudden failure is proposed. Finally, an algorithm is developed to solve the optimization model for a case study. The method can be used to plan the charging time and perform rescheduling under sudden failure to improve the robustness and dynamic response capability of AGVs.

Keywords

Automatic guided vehicle / Scheduling / Outfitting warehouse / Power consumption / Equipment failure

Cite this article

Download citation ▾
Guoliang Fan, Zuhua Jiang. Approach for Scheduling Automatic Guided Vehicles Considering Equipment Failure and Power Management. Journal of Marine Science and Application, 2023, 22(3): 624-635 DOI:10.1007/s11804-023-00357-3

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Chen Y, Jiang Z. Multi-AGVs scheduling with vehicle conflict consideration in ship outfitting Items warehouse. Journal of Shanghai Jiao Tong University (Science), 2022, 22: 1-15

[2]

Chiu Y, Shih CJ. Rescheduling strategies for integrating rush orders with preventive maintenance in a two-machine flow shop. International Journal of Production Research, 2012, 50(20): 5783-5794

[3]

Custodio L, Machado R. Flexible automated warehouse: a literature review and an innovative framework. International Journal of Advanced Manufacturing Technology, 2020, 106: 533-558

[4]

Dang QV, Singh N, Adan I, Martagan T, Sande D. Scheduling heterogeneous multi-load AGVs with battery constraints. Computers & Operations Research, 2021, 136: 105517

[5]

Deng Y, Chen Y, Zhang Y, Mahadevan S. Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment. Applied Soft Computing, 2012, 12(3): 1231-1237

[6]

Fazlollahtabar H, Saidi-Mehrabad M, Balakrishnan J. Integrated Markov-neural reliability computation method: A case for multiple automated guided vehicle system. Reliability Engineering & System Safety, 2015, 135: 34-44

[7]

Fu JL, Zhang HZ, Zhang J, Jiang LK. Review on AGV scheduling optimization. Journal of System Simulation, 2020, 32(9): 1664-1675

[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. A hybrid approach for forecasting ship motion using CNN-GRU-AM and GCWOA. Applied Soft Computing, 2022, 114: 108084

[10]

Li MW, Xu DY, Geng J, Hong WC. A ship motion forecasting approach based on empirical mode decomposition method hybrid deep learning network and quantum butterfly optimization algorithm. Nonlinear Dynamics, 2022, 107(3): 2447-2467

[11]

Li R, Liu YJ, Hamada K. Research on the ITOC based scheduling system for ship piping production. Journal of Marine Science and Application, 2010, 9(4): 355-362

[12]

Majdzik P, Witczak M, Lipiec B, Banaszak Z. Integrated fault-tolerant control of assembly and automated guided vehicle-based transportation layers. International Journal of Computer Integrated Manufacturing, 2022, 35(4–5): 409-426

[13]

Mousavi M, Yap HJ, Musa SN, Dawal SZM. A fuzzy hybrid GA-PSO algorithm for multi-objective AGV scheduling in FMS. International Journal of Simulation Modelling, 2017, 16(1): 58-71

[14]

Umar UA, Ariffin MK, Ismail N, Tang SH. 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, 2015, 81(9–12): 2123-2141

[15]

Vivaldini K, Rocha LF, Martarelli NJ, Becker M, Moreira AP. Integrated tasks assignment and routing for the estimation of the optimal number of AGVS. International Journal of Advanced Manufacturing Technology, 2016, 82: 719-736

[16]

Witczak M, Majdzik P, Stetter R, Lipiec B. A fault-tolerant control strategy for multiple automated guided vehicles. Journal of Manufacturing Systems, 2020, 55: 56-68

[17]

Wang J, Pan J, Huo J, Wang R, Li L, Nian T. Research on optimization of multi-AGV path based on genetic algorithm considering charge utilization. Journal of Physics: Conference Series, 2021, 1769(1): 012052

[18]

Wu B, Chi X, Zhao C, Zhang W, Lu Y, Jiang D. Dynamic path planning for forklift AGV based on smoothing A* and improved DWA hybrid algorithm. Sensors, 2022, 22(18): 7079

[19]

Xiao Y, Zhao Q, Kaku I, Xu Y. Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Computers & Operations Research, 2012, 39(7): 1419-1431

[20]

Yan R, Dunnett SJ, Jackson LM. Model-based research for aiding decision-making during the design and operation of multi-load automated guided vehicle systems. Reliability Engineering & System Safety, 2022, 219: 108264

[21]

Yan R, Jackson LM, Dunnett SJ. Automated guided vehicle mission reliability modelling using a combined fault tree and Petri net approach. The International Journal of Advanced Manufacturing Technology, 2017, 92: 1825-1837

[22]

Zacharia PT, Xidias EK. AGV routing and motion planning in a flexible manufacturing system using a fuzzy-based genetic algorithm. The International Journal of Advanced Manufacturing Technology, 2020, 109: 1801-1813

[23]

Zhang MJ, Zheng JX, Zhang J. Selection method of multi-objective problems using genetic algorithm in motion plan of AUV. Journal of Marine Science and Application, 2002, 1(1): 81-86

[24]

Zhen L, Wu YW, Zhang S, Sun QJ, Yue Q. A decision framework for automatic guided vehicle routing problem with traffic congestions. Journal of the Operations Research Society of China, 2020, 8(3): 357-373

[25]

Zou WQ, Pan QK, Wang L, Miao ZH, Peng C. Efficient multiobjective optimization for an AGV energy-efficient scheduling problem with release time. Knowledge-Based Systems, 2022, 242: 108334

AI Summary AI Mindmap
PDF

143

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/