Abstract
Automated storage and retrieval system (AS/RS) is being widely used in the logistics industry. The order picking problem is researched to improve the overall efficiency of the system. A mathematical model is constructed to obtain the minimal travel time during the retrieval and storage operation under the operating condition that each machine serves several aisles of the system. The aisles are assumed to exist in the same region of the AS/RS and thus form a valid order S/R zone. An improved ant colony algorithm with awaiting node set, dynamic change on algorithm parameters and selection operator is developed for searching the optimal solution. Simulation results demonstrate the approach has better search ability and quickly astringency, satisfying the demands of medium or large scale work in AS/RA. The approach is an effective solution to order picking problem in automated warehouse.
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References
Van Den Berg, J.P.: Analytic expressions for the optimal dwell point in an automated storage/retrieval system. Int. Production Economics 76, 13–25 (2002)
Hu, Y.H., Huang, S.Y., Chen, C.Y., et al.: Travel time analysis of a new automated atorage and retrieval system. Computers Operations Research 32, 1514–1544 (2005)
Zaki, S., Can, S.G., Noureddline, G.: Travel-time models for flow-rack automated storage and retrieval system. Advanced Manufacturing Technology (2004)
Wen, U.P., Chang, D.T., Chen, S.P.: The impact of acceleration/deceleration on travel-time models in class-based automated S/R system. IEEE Trans. 33, 599–608 (2001)
Chang, S.-H., Egbelu, P.J.: Relative pre-positioning of storage/ re-trieval machines in automated storage/retrieval system to minimize expected system. IIE Transactions 29, 313–322 (1997)
Dorigo, M., Vitorio, M., Alberto, C.: The ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics-Part B 26, 1–13 (1996)
Thomas, S., Marco, D.A.: A short convergence for a class of ant colony optimization algorithm. IEEE Trans. on Evolutionary Computation 6, 358–365 (2002)
Tsai, C.F., Tsai, C.W.: A new approach for solving large traveling salesmam problem using evolution ant rules. In: Proceedings of the 2002 Int’1 Joint Conference, Honolulu, pp. 1540–1545. IEEE Press, Los Alamitos (2002)
Dorigo, M., Birattari, M., Stiitzle, T.: Ant colony optimization: artificial ants as a computational intelligence technique. IEEE Computational Intelligence Magazine 11, 28–39 (2006)
Bin, W., Zhong-zhi, S.: An ant colony algorithm based partition algorithm for TSP. Chinese J. Computer 24, 1328–1333 (2001)
Kon, M.M., Gos Wami, D., Jyoti, A.: Routing in telecommunication network with controlled ant population. In: Proceedings of The First IEEE Consumer Communications and Networking Conference, pp. 665–666. Institute of Electrical and Electronics Engineers Inc., New York (2004)
Yuan-jing, F., Zu-ren, F., Qin-ke, P.: Adaptive ant colony optimization algorithm and its convergence. Control Theory Applications 22, 713–717 (2005)
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Tang, Hy., Li, Mj. (2009). An Improved Ant Colony Algorithm for Order Picking Optimization Problem in Automated Warehouse. In: Cao, B., Li, TF., Zhang, CY. (eds) Fuzzy Information and Engineering Volume 2. Advances in Intelligent and Soft Computing, vol 62. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03664-4_163
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DOI: https://doi.org/10.1007/978-3-642-03664-4_163
Publisher Name: Springer, Berlin, Heidelberg
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