Multi-objective AGV scheduling in an automatic sorting system of an unmanned (intelligent) warehouse by using two adaptive genetic algorithms and a multi-adaptive genetic algorithm
Autoři:
Yubang Liu aff001; Shouwen Ji aff001; Zengrong Su aff002; Dong Guo aff003
Působiště autorů:
School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China
aff001; Aviation Business Department, Beijing capital international airport Company Limited, Beijing, China
aff002; School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
aff003
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0226161
Souhrn
Automated guided vehicle (AGV) is a logistics transport vehicle with high safety performance and excellent availability, which can genuinely achieve unmanned operation. The use of AGV in intelligent warehouses or unmanned warehouses for sorting can improve the efficiency of warehouses and enhance the competitiveness of enterprises. In this paper, a multi-objective mathematical model was developed and integrated with two adaptive genetic algorithms (AGA) and a multi-adaptive genetic algorithm (MAGA) to optimize the task scheduling of AGVs by taking the charging task and the changeable speed of the AGV into consideration to minimize makespan, the number of AGVs used, and the amount of electricity consumption. The numerical experiments showed that MAGA is the best of the three algorithms. The value of objectives before and after optimization changed by about 30%, which proved the rationality and validity of the model and MAGA.
Klíčová slova:
Algorithms – Electricity – Entropy – Genetic algorithms – Mathematical models – Optimization – Population genetics – Species diversity
Zdroje
1. Rundong Y, Dunnett S J, Jackson L M. Novel methodology for optimising the design, operation and maintenance of a multi-AGV system. Reliability Engineering & System Safety. 2018; 178:130–139.
2. Blazewicz J, Eiselt HA, Finke G, Laporte G, Weglarz J. Scheduling tasks and vehicles in a flexible manufacturing system. International Journal of Flexible Manufacturing Systems. 1991; 4(1):5–16.
3. Sabuncuoglu I, Hommertzheim DL. Dynamic dispatching algorithm for scheduling machines and automated guided vehicles in a flexible manufacturing system. The International Journal Of Production Research. 1992; 30(5):1059–79.
4. Reddy B, Rao C. Flexible manufacturing systems modelling and performance evaluation using AutoMod. International Journal of Simulation Modelling. 2011; 10(2):78–90.
5. Kumar MS, Janardhana R, Rao C. Simultaneous scheduling of machines and vehicles in an FMS environment with alternative routing. The International Journal of Advanced Manufacturing Technology. 2011; 53(1–4):339–51.
6. Udhayakumar P, Kumanan S. Task scheduling of AGV in FMS using non-traditional optimization techniques. International Journal of Simulation Modelling. 2010; 9(1):28–39.
7. Liang Y, Lin L, Gen M, Chien CF, editors. A hybrid evolutionary algorithm for FMS optimization with AGV dispatching. Computers & Industrial Engineering. 2012.
8. Pan XY, Wu J, Zhang QW, Lai D, Xie HL, Zhang C. A Case Study of AGV Scheduling for Production Material Handling. Applied Mechanics and Materials. 2013; 411:2351–4.
9. Fazlollahtabar H., Saidi-Mehrabad M. Optimising a multi-objective reliability assessment in multiple AGV manufacturing system. International Journal of Services and Operations Management. 2013;16:352–372.
10. Zheng K, Tang D, Gu W, Dai M. Distributed control of multi-AGV system based on regional control model. Production Engineering. 2013; 7(4):433–41.
11. Zheng Yan, Xiao Yujie & Seo Yoonho. A tabu search algorithm for simultaneous machine/AGV scheduling problem, International Journal of Production Research. 2014; 52:19, 5748–5763.
12. Vasava AS. Scheduling of automated guided vehicle in different flexible manufacturing system environment. International Journal of Innovative Research in Advanced Engineering (IJIRAE). 2014; 1(8):262–7.
13. Fazlollahtabar H., & Hassanli S. Hybrid cost and time path planning for multiple autonomous guided vehicles. Applied Intelligence. 2017; 48(2):482–498.
14. Mousavi M, Yap HJ, Musa SN, Tahriri F, Md Dawal SZ. Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithmand particle swarm optimization. PLoS ONE. 2017; 12(3):e0169817. doi: 10.1371/journal.pone.0169817 28263994
15. Karimi B, Niaki S T A, Haleh H, et al. Bi-Objective optimization of a job shop with two types of failures for the operating machines that use automated guided vehicles. Reliability Engineering & System Safety. 2018; S0951832017308335.
16. Huang D, Zhang G, editors. Scheduling control of AGV system based on game theory. 6th International Conference on Advanced Infocomm Technology (ICAIT); 2013; Hsinchu, Taiwan: IEEE.
17. 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. Computers & Industrial Engineering. 2015; 86:2–13.
18. Aized T. Modelling and performance maximization of an integrated automated guided vehicle system using coloured Petri net and response surface methods. Computers & Industrial Engineering. 2009; 57 (3):822–31.
19. Kato F, Shin S, editors. Multistep optimal scheduling of Automated Guided Vehicles in a semiconductor fabrication. Proceedings of SICE Annual Conference 2010; 2010: IEEE.
20. Ji M, Xia J. Analysis of vehicle requirements in a general automated guided vehicle system based transportation system. Computers & Industrial Engineering. 2010; 59(4): 544–551.
21. Wang HF, Chan CH. Multi-objective optimisation of automated guided dispatching and vehicle routing system. International Journal of Modelling in Operations Management. 2014; 4(1):35–52.
22. Vivaldini K, Rocha LF, Martarelli NJ, Becker M, Moreira AP. Integrated tasks assignment and routing for the estimation of the optimal number of AGVS. The International Journal of Advanced Manufacturing Technology. 2016; 82(1): 719–736.
23. Wang JB, Hou LY, Li W, Zheng XJ. Simulating an AGV Scheduling in Job Workshop for Optimal Configuration. In: Peilong Xu HS, Wang Yiqian and Wang Pin, editor. Advanced Materials Research. 2014; 926–930:1562–1565.
24. Yaqi ZHANG, Bin YANG, Zhihua HU, et al. Research of AGV charging and job integrated scheduling at automated container terminal. Computer Engineering and Applications. 2017; 53(18):257–262.
25. Ulusoy G., Sivrikaya-Şerifoǧlu F., & Bilge Ü. A genetic algorithm approach to the simultaneous scheduling of machines and automated guided vehicles. Computers & Operations Research. 1997; 24(4): 335–351.
26. Abdelmaguid T. F., Nassef A. O., Kamal B. A., & Hassan M. F. A hybrid ga/heuristic approach to the simultaneous scheduling of machines and automated guided vehicles. International Journal of Production Research. 2004;42(2): 15.
27. Reddy B. S. P., & Rao C. S. P. A hybrid multi-objective ga for simultaneous scheduling of machines and agvs in fms. International Journal of Advanced Manufacturing Technology. 2006; 31(5–6): 602–613.
28. Ren NF, Liu D, Zhao Y, Ge XB. AGV Scheduling Optimizing Research of Collaborative Manufacturing System Based on Improved Genetic Algorithm. Applied Mechanics and Materials. 2013; 300:55–61.
29. Wang Y, Ma X, Xu M, et al. Vehicle routing problem based on a fuzzy customer clustering approach for logistics network optimization. Journal of Intelligent & Fuzzy Systems, 2015, 29(4):1427–1442.
30. Dezhi Z, Fangzi Z, Shuangyan L, et al. Green Supply Chain Network Design with Economies of Scale and Environmental Concerns[J]. Journal of Advanced Transportation, 2017, 2017:1–14.
31. Chen C., Tiong L. K., & Chen I.-M. Using a genetic algorithm to schedule the space-constrained AGV-based prefabricated bathroom units manufacturing system. International Journal of Production Research. 2018; 12:1–17. doi: 10.1080/00207543.2018.1521532
32. Han Z, Wang D, Liu F, Zhao Z. Multi-AGV path planning with double-path constraints by using an improved genetic algorithm. PLoS ONE. 2017;12(7):e0181747. doi: 10.1371/journal.pone.0181747 28746355
33. Dezhi Z, Xin W, Shuangyan L, et al. Joint optimization of green vehicle scheduling and routing problem with time-varying speeds. PLOS ONE, 2018, 13(2):e0192000–. doi: 10.1371/journal.pone.0192000 29466370
34. Srinvivas M. and Patnaik L.M. Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms. IEEE Transaction on Systems, Man, and Cybernetics. 1994; 24: 656–657
35. Sun N., & Lu Y. A self-adaptive genetic algorithm with improved mutation mode based on measurement of population diversity. Neural Computing and Applications. 2018.
36. Wang et al. Profit distribution in collaborative multiple centers vehicle routing problem. Journal of Cleaner Production. 2017; 144: 203–219.
37. Pareto V. Corso di economia politica: P. Boringhieri; 1961.
38. Xia GM, Zeng JC. A stochastic particle swarm optimization algorithm based on the genetic algorithm of roulette wheel selection, Computer Engineering and Science. 2007; 29(6): 6–11.
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2019 Číslo 12
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