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Vehicle Scheduling Problem in Terminals: A Review

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Verification and Evaluation of Computer and Communication Systems (VECoS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12519))

Abstract

This paper presents a review on vehicle scheduling problem in terminal. First of all, the current status of vehicle scheduling in terminal is introduced. The introduction includes the main types of current terminals and the main operating machinery in those terminals. Then, three main issues in vehicle scheduling problems are discussed and clarified in this paper, they are fleet sizing problem, vehicle dispatching problem and path planning problem. Research on these issues is divided into multiple types, and the development of these studies is introduced in the article. At last, this article explores the advantages and disadvantages of these studies. By comparing various types of research, this article presents the future research directions of these issues.

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References

  1. Salzborn, F.J.M, Buckley, D J.: Minimum fleet size models for transportation systems. In: Buckley, D.J., (Ed.), Proceedings of the 6th International Symposium on Transportation & Traffic Theory (ISTTT6), pp. 607–624. Elsevier, Sydney (1974)

    Google Scholar 

  2. Proll, L.G.: Letter to the editor—a note on the minimum fleetsize for a suburban railway system. Transp. Sci. 6(2), 204–207 (1972)

    Article  Google Scholar 

  3. Koo, P.H., Jang, D.W., Lee, W.S.: Fleet sizing and vehicle routing for static freight container transportation. IE Interfaces 16(2), 174–184 (2003)

    Google Scholar 

  4. Koo, P.H., Lee, W.S., Jang, D.W.: Fleet sizing and vehicle routing for container transportation in a static environment. OR Spectr. 26(2), 193–209 (2004)

    Google Scholar 

  5. Vis, I.F.A., René (M.)B.M., de Koster, M.W.P.: Savelsbergh minimum vehicle fleet size under time-window constraints at a container terminal. Transp. Sci. 39(2), 249–260 (2005)

    Google Scholar 

  6. Vis, I.F.A., De Koster, R., Roodbergen, K.J., Peeters, L.W.: Determination of the number of automated guided vehicles required at a semi-automated container termina. J. Oper. Res. Soc. 52(4), 409–417 (2001)

    Article  Google Scholar 

  7. Gobal, S.L., Kasilingam, R.G.: A simulation model for estimating vehicle requirements in automated guided vehicle systems. Comput. Ind. Eng. 21(1–4), 623–627 (1991)

    Article  Google Scholar 

  8. Wibisono, R., Ai, T.J., Yuniartha, D.R.: Fleet sizing of automated material handling using simulation approach. Mater. Sci. Eng. Conf. Ser. 319, 012030 (2018)

    Article  Google Scholar 

  9. Chang, K.H., Huang, Y.H., Yang, S.P.: Vehicle fleet sizing for automated material handling systems to minimize cost subject to time constraints. Lie Trans. 46(3), 301–312 (2014)

    Google Scholar 

  10. Pjevcevic, D., Nikolic, M., Vidic, N., Vukadinovic, K.: Data envelopment analysis of AGV fleet sizing at a port container terminal. Int. J. Prod. Res. 55(14), 4021–4034 (2016)

    Article  Google Scholar 

  11. bin Md Fauadi, M.H.F., Li, W.-L., Murata, T., Prabuwono, A.S.: Vehicle requirement analysis of an AGV system using discrete-event simulation and data envelopment analysis. In: 2012 8th International Conference on Computing Technology and Information Management (NCM and ICNIT), pp. 819–823. IEEE, Seoul (2012)

    Google Scholar 

  12. Chang, K.H., Chang, A.L., Kuo, C.Y.: A simulation-based framework for multi-objective vehicle fleet sizing of automated material handling systems: an empirical study. J. Simul. 8(4), 271–280 (2014)

    Article  Google Scholar 

  13. Samuel, R.: Routing and scheduling of vehicles and crews: the state of the art. Comput. Oper. Res. 10(2), 63–67 (1983)

    Article  MathSciNet  Google Scholar 

  14. Liu, C.I., Ioannou, P.A.: A comparison of different AGV dispatching rules in an automated container terminal. In: IEEE International Conference on Intelligent Transportation Systems, pp. 880–885. IEEE, Singapore (2002)

    Google Scholar 

  15. Mahadevan, B., Narendran, T.T.: Design of an automated guided vehicle-based material handling system for a flexible manufacturing system. Int. J. Prod. Res. 28(9), 1611–1622 (1990)

    Article  Google Scholar 

  16. Wanan, C.C., Bin, W.: Vehicle dispatching under the shortest path and port centralization. In: Fourth International Symposium on Knowledge Acquisition & Modeling, pp. 139–142. IEEE, Sanya (2012)

    Google Scholar 

  17. Cheng, Y.L., Sen, H.C., Natarajan, K., Teo, C.P., Tan, K.C.: Dispatching automated guided vehicles in a container terminal. Supply Chain Optimization, 355–389 (2006)

    Google Scholar 

  18. Günther, H.-O., Kim, K.H.: Dispatching multi-load AGVs in highly automated seaport container terminals. Container Terminals and Automated Transport Systems. 231–255(2005)

    Google Scholar 

  19. Kagaya, S., Kikuchi, S., Donnelly, R.A.: Use of a fuzzy theory technique for grouping of trips in the vehicle routing and scheduling problem. Eur. J. Oper. Res. 76(1), 143–154 (1994)

    Article  Google Scholar 

  20. Umashankar, N., Karthik, V.N.: Multi-criteria intelligent dispatching control of automated guided vehicles in FMS. In: 2006 IEEE Conference on Cybernetics and Intelligent Systems, pp. 1–6. IEEE, Bangkok (2006)

    Google Scholar 

  21. Kozan, E., Preston, P.: Genetic algorithms to schedule container transfers at multimodal terminals. Int. Trans. Oper. Res. 6(3), 311–329 (2010)

    Article  Google Scholar 

  22. Kim, K.H., Bae, J.W.: A look-ahead dispatching method for automated guided vehicles in automated port container terminals. Transp. Sci. 38(2), 224–234 (2004)

    Article  Google Scholar 

  23. Kim, J., Choe, R., Kwang, R.R.: Multi-objective optimization of dispatching strategies for situation-adaptive AGV operation in an automated container terminal. In: Proceedings of the 2013 Research in Adaptive and Convergent Systems (RACS 2013), pp. 1–6. Association for Computing Machinery, New York (2013)

    Google Scholar 

  24. Lee, N.M.Y., Lau, H.Y.K., Ko, A.W.Y.: An immune inspired algorithm for solving dynamic vehicle dispatching problem in a port container terminal. In: Andrews, P.S., Timmis, J., Owens, N.D.L., Aickelin, U., Hart, E., Hone, A., Tyrrell, A.M. (eds.) ICARIS 2009. LNCS, vol. 5666, pp. 329–342. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03246-2_30

    Chapter  Google Scholar 

  25. Koo, P.H.: Dispatching transport vehicles in maritime container terminals. Int. J. Bus. Tour. Appl. Sci. 1, 90–97 (2013)

    Google Scholar 

  26. Zulvia, F.E., Kuo, R.J., Hu, T.L.: Solving CVRP with time window, fuzzy travel time and demand via a hybrid ant colony optimization and genetic algorithm. In: 2012 IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE, Brisbane (2012)

    Google Scholar 

  27. Xing, Y., Yin, K., Quadrifoglio, L., Wang, B.X.: Dispatch problem of automated guided vehicles for serving tandem lift quay crane. Transp. Res. Rec. 2273(1), 79–86 (2012)

    Article  Google Scholar 

  28. Homayouni, S.M., Tang, S.H., Ismail, N., Ariffin, M.K.A.M., Samin, R.: A hybrid genetic-heuristic algorithm for scheduling of automated guided vehicles and quay cranes in automated container terminals. In: 2009 International Conference on Computers & Industrial Engineering, pp. 96–101. IEEE, Troyes (2009)

    Google Scholar 

  29. Bose, J., Reiners, T., Steenken, D., Voß, S.: Vehicle dispatching at seaport container terminals using evolutionary algorithms. In: Proceedings of the 33rd annual Hawaii international conference on system sciences, pp. 10-pp. IEEE, Maui (2000)

    Google Scholar 

  30. Bae, J.W., Kim, K.H.: A pooled dispatching strategy for automated guided vehicles in port container terminals. Int. J. Manage. Sci. 6(2), 47–60 (2000)

    Google Scholar 

  31. Yue, L., Fan, H., Zhai, C.: Joint configuration and scheduling optimization of a dual-trolley quay crane and automatic guided vehicles with consideration of vessel stability. Sustainability 12(1), 1–16 (2019)

    Article  Google Scholar 

  32. Zhicheng, B., Yaozhou, Z., Xuemin, Z., Yansong, X., Jiaqi, C., Weijian, M.: Simulation-based AGV dispatching in automated container terminal. In: 2019 International Conference on Advances in Construction Machinery and Vehicle Engineering (ICACMVE), pp. 414–42. IEEE, Changsha (2019)

    Google Scholar 

  33. Lim, J.K., Lim, J.M., Yoshimoto, K., Kim, K.H., Takahashi, T.: Designing guide-path networks for automated guided vehicle system by using the Q-learning technique. Comput. Ind. Eng. 44(1), 1–17 (2003)

    Article  Google Scholar 

  34. Zeng, Q., Yang, Z., Hu, X.: A method integrating simulation and reinforcement learning for operation scheduling in container terminals. Transport 26(4), 383–393 (2011)

    Article  Google Scholar 

  35. Potvin, J.Y., Shen, Y., Rousseau, J.M.: Neural networks for automated vehicle dispatching. Comput. Oper. Res. 19(3–4), 267–276 (1992)

    Article  Google Scholar 

  36. Choe, R., Kim, J., Ryu, K.R.: Online preference learning for adaptive dispatching of AGVs in an automated container terminal. Appl. Soft Comput. 38, 647–660 (2016)

    Article  Google Scholar 

  37. Wei, S., Wang, L., Wang, B.R., Ren, H.J., Yang, Y.S., Liu, X.L., Ding, Y.C.: Improvement of A-star algorithm and its application in AGV path planning. Autom. Instrument. 38, 51–54 (2017)

    Google Scholar 

  38. Yang, R., Cheng, L.: Path Planning of restaurant service robot based on a-star algorithms with updated weights. In: 2019 12th International Symposium on Computational Intelligence and Design (ISCID), pp. 292–295. IEEE, Hangzhou (2019)

    Google Scholar 

  39. Zheng, T., Xu, Y., Zheng, D.: AGV path planning based on improved A-star algorithm. In: 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp. 1534–1538. IEEE, Chongqing (2019)

    Google Scholar 

  40. Sedighi, S., Nguyen, D., Kuhnert, K.: Guided hybrid A-star path planning algorithm for valet parking applications. In: 2019 5th International Conference on Control, Automation and Robotics (ICCAR), pp. 570–575. IEEE, Beijing (2019)

    Google Scholar 

  41. Wang, C., Wang, L., Qin, J., Wu, Z., Duan, L., Li, Z.,… & Lu, Z.: Path planning of automated guided vehicles based on improved A-Star algorithm. In: 2015 IEEE International Conference on Information and Automation, pp. 2071–2076. IEEE, Lijiang (2015)

    Google Scholar 

  42. Chuang, J.H., Ahuja, N.: An analytically tractable potential field model of free space and its application in obstacle avoidance. IEEE Trans. Syst. Man, Cybern. Part B (Cybern.), 28(5), 729–736 (1998)

    Google Scholar 

  43. Manjunath, T.C., Nagaraja, B.G., Kusagur, A.: Simulation & implementation of shortest path algorithm with a mobile robot using configuration space approach. In: 2009 International Conference on Advanced Computer Control, pp. 197–201. IEEE, Singapore (2009)

    Google Scholar 

  44. Zhang, M., Shen, Y., Wang, Q., Wang, Y.: Dynamic artificial potential field based multi-robot formation control. In: 2010 IEEE Instrumentation & Measurement Technology Conference Proceedings, pp. 1530–1534. IEEE, Austin (2010)

    Google Scholar 

  45. Chen, L., Liu, C., Shi, H., Gao, B.: New robot planning algorithm based on improved artificial potential field. In: 2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control, pp. 228–232. IEEE, Shenyang (2013)

    Google Scholar 

  46. Qian, C., Qisong, Z., Li, H.: Improved artificial potential field method for dynamic target path planning in LBS. In: 2018 Chinese Control And Decision Conference (CCDC), pp. 2710–2714. IEEE, Shenyang (2018)

    Google Scholar 

  47. Jianjun, Y., Hongwei, D., Guanwei, W., Lu, Z.: Research about local path planning of moving robot based on improved artificial potential field. In: 2013 25th Chinese Control and Decision Conference (CCDC), pp. 2861–2865. IEEE, Guiyang (2013)

    Google Scholar 

  48. Zhou, L., Li, W.: Adaptive artificial potential field approach for obstacle avoidance path planning. In: 2014 Seventh International Symposium on Computational Intelligence and Design Vol. 2, pp. 429–432. IEEE, Hangzhou (2014)

    Google Scholar 

  49. Makita, Y., Hagiwara, M., Nakagawa, M.: A simple path planning system using fuzzy rules and a potential field. In: Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference, pp. 994–999. IEEE, Orlando (1994)

    Google Scholar 

  50. Yu, J., Sun, Y., Ruan, X., Zhang, Y.: Research on path planning for robots based on PSO optimization for fuzzy controller. In: Proceeding of the 11th World Congress on Intelligent Control and Automation, pp. 5293–5298. IEEE, Shenyang (2014)

    Google Scholar 

  51. Li, Q., Zhang, C., Han, C., Xu, Y., Yin, Y., Zhang, W.: Path planning based on fuzzy logic algorithm for mobile robots in static environment. In: 2013 25th Chinese Control and Decision Conference (CCDC), pp. 2866–2871. IEEE, Guiyang (2013)

    Google Scholar 

  52. Wang, M.: Fuzzy logic based robot path planning in unknown environment. In: 2005 International Conference on Machine Learning and Cybernetics Vol. 2, pp. 813–818. IEEE, Guangzhou (2005)

    Google Scholar 

  53. Motamedinejad, M.B., Barzamini, R., Jouzdani, J., Khosravi, A.: A new fuzzy path planning for multiple robots. In: 2006 International Conference on Information and Automation, pp. 295–300. IEEE, Shandong (2006)

    Google Scholar 

  54. Li, S., Ding, M., Cai, C., Jiang, L.: Efficient path planning method based on genetic algorithm combining path network. In: 2010 Fourth International Conference on Genetic and Evolutionary Computing, pp. 194–197. IEEE, Shenzhen (2010)

    Google Scholar 

  55. Zeqing, Y., Libing, L., Zhihong, T., Weiling, L.: Application of adaptive genetic algorithm in flexible inspection path planning. In: 2008 27th Chinese Control Conference, pp. 75–80. IEEE, Kunming (2008)

    Google Scholar 

  56. Panda, R.K., Choudhury, B.B.: An effective path planning of mobile robot using genetic algorithm. In: 2015 IEEE International Conference on Computational Intelligence & Communication Technology, pp. 287–291. IEEE, Ghaziabad (2015)

    Google Scholar 

  57. Sun, Y., Ding, M.: Quantum genetic algorithm for mobile robot path planning. In: 2010 Fourth International Conference on Genetic and Evolutionary Computing, pp. 206–209. IEEE, Shenzhen (2010)

    Google Scholar 

  58. Ali, M.M., Farooq, O., Khan, M.H., Haxha, S.: Hardware implementation of compact genetic algorithm for robot path planning in globally static environment in 8-bit microcontroller. In: 2019 5th International Conference on Information Management (ICIM), pp. 242–247. IEEE, Cambridge (2019)

    Google Scholar 

  59. Luo, M., Hou, X., Yang, J.: Multi-Robot one-target 3D path planning based on improved bio-inspired neural network. In: 2019 16th International Computer Conference on Wavelet Active Media Technology and Information Processing, pp. 410–413. IEEE, Chendu (2019)

    Google Scholar 

  60. Kassim, A. A., Kumar, B. V.: A neural network architecture for path planning. In: Proceedings 1992 of IJCNN International Joint Conference on Neural Networks Vol. 2, pp. 787–792. IEEE, Baltimore (1992)

    Google Scholar 

  61. Li, Y., Meng, M.Q.H., Li, S., Chen, W., You, Z., Guo, Q., Liang, H.: A quad tree based neural network approach to real-time path planning. In: 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1350–1354. IEEE, Sanya (2007)

    Google Scholar 

  62. Yuan, H., Zhang, G., Li, Y., Liu, K., Yu, J.: Research and implementation of intelligent vehicle path planning based on four-layer neural network. In: 2019 Chinese Automation Congress (CAC), pp. 578–582. IEEE, Hangzhou (2019)

    Google Scholar 

  63. Lv, Z., Cao, J.: Path planning methods of mobile robot based on new neural network. In: Proceedings of the 32nd Chinese Control Conference, pp. 3222–3226. IEEE, Xi’an (2013)

    Google Scholar 

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Wang, P. (2020). Vehicle Scheduling Problem in Terminals: A Review. In: Ben Hedia, B., Chen, YF., Liu, G., Yu, Z. (eds) Verification and Evaluation of Computer and Communication Systems. VECoS 2020. Lecture Notes in Computer Science(), vol 12519. Springer, Cham. https://doi.org/10.1007/978-3-030-65955-4_5

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  • DOI: https://doi.org/10.1007/978-3-030-65955-4_5

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