Abstract
The simultaneous scheduling of cranes and automated guided vehicles (AGVs) has been a critical problem, especially in traditional heavy-duty manufacturing enterprises. Previous studies mainly focus on the coscheduling problem for the container terminals rather than workshop. Hence, this study evaluated the coscheduling problem of automated single-beam cranes and AGVs incurred by an actual manufacturing environment. A joint scheduling model was established by considering the constraints of conflict-free, noncrossing, and safety distance as well as the feature of the material. This model considers the process and minimum makespan as the scheduling unit and optimization objective, respectively, including handling time and process time. Subsequently, a hybrid genetic algorithm and particle swarm optimization (HGA-PSO) was developed, and a check-repair procedure was designed to guarantee the feasibility of optimal solution. Finally, numerical illustrative experiments and nonparametric tests were conducted to verify the effectiveness of the proposed model and algorithm, and the results show that the proposed algorithm has better performance than PSO and GA in solving the proposed model.
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Kulak, O.: A decision support system for fuzzy multi-attribute selection of material handling equipments. Expert Syst. Appl. 29(2), 310–319 (2005). https://doi.org/10.1016/j.eswa.2005.04.004
Yan, R.; Dunnett, S.J.; Jackson, L.M.: Novel methodology for optimising the design, operation and maintenance of a multi-AGV system. Reliab. Eng. Syst. Saf. 178, 130–139 (2018). https://doi.org/10.1016/j.ress.2018.06.003
Heshmati, S.; Toffolo, T.A.M.; Vancroonenburg, W.; Vanden Berghe, G.: Crane-operated warehouses: integrating location assignment and crane scheduling. Comput. Ind. Eng. 129, 274–295 (2019). https://doi.org/10.1016/j.cie.2019.01.039
Heger, J.; Voss, T.: Optimal Scheduling of AGVs in a Reentrant Blocking Job-shop. Procedia CIRP 67, 41–45 (2018). https://doi.org/10.1016/j.procir.2017.12.173
Heger, J.; Voss, T.: Reducing mean tardiness in a flexible job shop containing AGVs with optimized combinations of sequencing and routing rules. Procedia CIRP 81, 1136–1141 (2019). https://doi.org/10.1016/j.procir.2019.03.281
Nageswararao, M.; Narayanarao, K.; Ranagajanardhana, G.: Simultaneous Scheduling of Machines and AGVs in Flexible Manufacturing System with Minimization of Tardiness Criterion. Procedia Materials Science 5, 1492–1501 (2014). https://doi.org/10.1016/j.mspro.2014.07.336
Miyamoto, T.; Inoue, K.: Local and random searches for dispatch and conflict-free routing problem of capacitated AGV systems. Comput. Ind. Eng. 91, 1–9 (2016). https://doi.org/10.1016/j.cie.2015.10.017
Saidi-Mehrabad, M.; Dehnavi-Arani, S.; Evazabadian, F.; Mahmoodian, V.: An Ant Colony Algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs. Comput. Ind. Eng. 86, 2–13 (2015). https://doi.org/10.1016/j.cie.2015.01.003
Fazlollahtabar, H.; Saidi-Mehrabad, M.; Balakrishnan, J.: Mathematical optimization for earliness/tardiness minimization in a multiple automated guided vehicle manufacturing system via integrated heuristic algorithms. Robot. Auton. Syst. 72, 131–138 (2015). https://doi.org/10.1016/j.robot.2015.05.002
Murakami, K.: Time-space network model and MILP formulation of the conflict-free routing problem of a capacitated AGV system. Comput. Ind. Eng. 141 (2020). https://doi.org/10.1016/j.cie.2020.106270
Bilge, Ü.; Tanchoco, J.M.A.: AGV systems with multi-load carriers: basic issues and potential benefits. J. Manu. Syst. 16(3), 159–174 (1997). https://doi.org/10.1016/S0278-6125(97)88885-1
Grunow, M.; Gunther, H.O.; Lehmann, M.: Dispatching multi-load AGVs in highly automated seaport container terminals. OR Spectrum 26(2), 211–235 (2004). https://doi.org/10.1007/s00291-003-0147-1
Chawla, V.K., Chanda, A.K., Angra, S., Rani, S.: Simultaneous Dispatching and Scheduling of Multi-Load AGVs in FMS-A Simulation Study. Materials Today: Proceedings 5(11, Part 3), 25358-25367 (2018). doi:https://doi.org/10.1016/j.matpr.2018.10.339
Zhang, L.; Hu, Y.; Guan, Y.: Research on hybrid-load AGV dispatching problem for mixed-model automobile assembly line. Procedia CIRP 81, 1059–1064 (2019). https://doi.org/10.1016/j.procir.2019.03.251
Gharehgozli, A.H.; Yu, Y.; de Koster, R.; Udding, J.T.: An exact method for scheduling a yard crane. Eur. J. Oper. Res. 235(2), 431–447 (2014). https://doi.org/10.1016/j.ejor.2013.09.038
Liang, C.-J.; Chen, M.; Gen, M.; Jo, J.: A multi-objective genetic algorithm for yard crane scheduling problem with multiple work lines. J. Intell. Manuf. 25(5), 1013–1024 (2013). https://doi.org/10.1007/s10845-013-0792-4
Chu, F.; He, J.; Zheng, F.; Liu, M.: Scheduling multiple yard cranes in two adjacent container blocks with position-dependent processing times. Comput. Ind. Eng. 136, 355–365 (2019). https://doi.org/10.1016/j.cie.2019.07.013
Guo, P.; Wang, L.; Xue, C.; Wang, Y.: Dispatching Rules for Scheduling Twin Automated Gantry Cranes in an Automated Railroad Container Terminal. Arab. J. Sci. Eng. 45(3), 2205–2217 (2020). https://doi.org/10.1007/s13369-019-04176-z
Li, J., Xu, A., Zang, X.: Simulation-based solution for a dynamic multi-crane-scheduling problem in a steelmaking shop. Int. J. Prod. Res., 1-15 (2019). https://doi.org/10.1080/00207543.2019.1687952
Boysen, N.; Stephan, K.: A survey on single crane scheduling in automated storage/retrieval systems. Eur. J. Oper. Res. 254(3), 691–704 (2016). https://doi.org/10.1016/j.ejor.2016.04.008
Zhong, M., Yang, Y., Dessouky, Y., Postolache, O.: Multi-AGV scheduling for conflict-free path planning in automated container terminals. Comput. Ind. Eng. 142 (2020). https://doi.org/10.1016/j.cie.2020.106371
Yang, Y.; Zhong, M.; Dessouky, Y.; Postolache, O.: An integrated scheduling method for AGV routing in automated container terminals. Comput. Ind. Eng. 126, 482–493 (2018). https://doi.org/10.1016/j.cie.2018.10.007
Chen, X.; He, S.; Zhang, Y.; Tong, L.; Shang, P.; Zhou, X.: Yard crane and AGV scheduling in automated container terminal: a multi-robot task allocation framework. Transportation Research Part C: Emerging Technologies 114, 241–271 (2020). https://doi.org/10.1016/j.trc.2020.02.012
Kaveshgar, N.; Huynh, N.: Integrated quay crane and yard truck scheduling for unloading inbound containers. Int. J. Product. Econ. 159, 168–177 (2015). https://doi.org/10.1016/j.ijpe.2014.09.028
Zeng, C.; Tang, J.; Yan, C.: Job-shop cell-scheduling problem with inter-cell moves and automated guided vehicles. J. Intell. Manuf. 26(5), 845–859 (2014). https://doi.org/10.1007/s10845-014-0875-x
Tang, L.; Zhao, J.; Liu, J.: Modeling and solution of the joint quay crane and truck scheduling problem. Eur. J. Oper. Res. 236(3), 978–990 (2014). https://doi.org/10.1016/j.ejor.2013.08.050
Lu, H.; Wang, S.: A study on multi-ASC scheduling method of automated container terminals based on graph theory. Comput. Ind. Eng. 129, 404–416 (2019). https://doi.org/10.1016/j.cie.2019.01.050
Liang, C.J.; Li, M.M.; Lu, B.; Gu, T.Y.; Jo, J.; Ding, Y.: Dynamic configuration of QC allocating problem based on multi-objective genetic algorithm. J. Intell. Manuf. 28(3), 847–855 (2017). https://doi.org/10.1007/s10845-015-1035-7
Liu, Z.; Guo, S.; Wang, L.: Integrated green scheduling optimization of flexible job shop and crane transportation considering comprehensive energy consumption. J. Clean. Prod. 211, 765–786 (2019). https://doi.org/10.1016/j.jclepro.2018.11.231
Mokhtari, H.; Noroozi, A.: An efficient chaotic based PSO for earliness/tardiness optimization in a batch processing flow shop scheduling problem. J. Intell. Manuf. 29(5), 1063–1081 (2015). https://doi.org/10.1007/s10845-015-1158-x
Thakur, P.; Srivastava, D.C.; Gupta, P.K.: The genetic algorithm: A robust method for stress inversion. J. Struct. Geol. 94, 227–239 (2017). https://doi.org/10.1016/j.jsg.2016.11.015
Lu, P.-H.; Wu, M.-C.; Tan, H.; Peng, Y.-H.; Chen, C.-F.: A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems. J. Intell. Manuf. 29(1), 19–34 (2015). https://doi.org/10.1007/s10845-015-1083-z
García, S.; Molina, D.; Lozano, M.; Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization. J. Heuristics 15(6), 617–644 (2008). https://doi.org/10.1007/s10732-008-9080-4
Funding
This research was supported by the Natural Science Foundation of Jiangsu Province, China (No. BK20141517), the Science and Technology Support Program of Jiangsu Province (No.BY2016043-02), and the Suzhou Municipal Science and Technology Bureau (No. SS201704).
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Appendix A
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Yang, Q., Chen, Z. & Yang, H. A Coscheduling Model of Automated Single-Beam Cranes and AGVs in Assembly Workshop. Arab J Sci Eng 46, 2815–2831 (2021). https://doi.org/10.1007/s13369-020-05134-w
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DOI: https://doi.org/10.1007/s13369-020-05134-w