Nowadays, issues regarding to e-commerce unpredictability become a problem in warehouse operations. This unpredictability is make difficult by fulfillment challenges. Designing a goods-to-man picking system and dispatching order strategy based on service in random order (SIRO) can be one of promising alternative to reduce AGV empty travel distance. The focus is on the warehouse operations, start from item classification on dynamic slots location, multi-attribute AGV dispatching rules and AGV battery management. The system aims to minimize total cost of AGV by assign the multi-attribute dispatching rules and bidding process to get on time delivery as many orders that can be completed, dealing with minimum battery-charging effects on the system operation. The planning system considers dynamic nature of customer order demand, and the simulation based development is used to model real time dynamic slots storage location and AGVs availability. The computational experiments showed this methodology most likely could reduce total cost by perform more than one AGV in operating systems

Nowadays, issues regarding to e-commerce unpredictability become a problem in warehouse operations. This unpredictability is make difficult by fulfillment challenges. Designing a goods-to-man picking system and dispatching order strategy based on service in random order (SIRO) can be one of promising alternative to reduce AGV empty travel distance. The focus is on the warehouse operations, start from item classification on dynamic slots location, multi-attribute AGV dispatching rules and AGV battery management. The system aims to minimize total cost of AGV by assign the multi-attribute dispatching rules and bidding process to get on time delivery as many orders that can be completed, dealing with minimum battery-charging effects on the system operation. The planning system considers dynamic nature of customer order demand, and the simulation based development is used to model real time dynamic slots storage location and AGVs availability. The computational experiments showed this methodology most likely could reduce total cost by perform more than one AGV in operating systems

Accenture. (2015). Industrial Internet of Things: Unleashing the Potential of Connected Products and Services. Retrieved from http://www3.weforum.org/docs/WEFUSA_IndustrialInternet_Report2015.pdf
Bilge, Ü., Esenduran, G., Varol, N., Öztürk, Z., Aydın, B., & Alp, A. (2006). Multi-attribute responsive dispatching strategies for automated guided vehicles. International Journal of Production Economics, 100(1), 65-75. doi:http://dx.doi.org/10.1016/j.ijpe.2004.10.004
Chuang, Y.-F., Lee, H.-T., & Lai, Y.-C. (2012). Item-associated cluster assignment model on storage allocation problems. Computers & Industrial Engineering, 63(4), 1171-1177. doi:http://dx.doi.org/10.1016/j.cie.2012.06.021
de Koster, R., Le-Duc, T., & Roodbergen, K. J. (2007). Design and control of warehouse order picking: A literature review. European Journal of Operational Research, 182(2), 481-501. doi:http://dx.doi.org/10.1016/j.ejor.2006.07.009
Ebben, M. (2001). Logistic Control In Automated Transportation Networks. University of Twente.
Enright, J. J., & Wurman, P. R. (2011). Optimization and Coordinated Autonomy in Mobile Fulfillment System. Automated Action Planning Mobile Robots AAAI Workshop.
Grunow, M., Gunther, H.-O., & Lehmann, M. (2006). Starategies for dispatching AGVs at automated seaport container terminals. doi:10.1007/s00291-006-0054-3
Hsieh, L.-f., & Tsai, L. (2006). The optimum design of a warehouse system on order picking efficiency. The International journal of advanced manufacturing technology, 28(5-6), 626-637.
Hwi Kim, S., & Hwang, H. (1999). An adaptive dispatching algorithm for automated guided vehicles based on an evolutionary process. International Journal of Production Economics, 60–61, 465-472. doi:http://dx.doi.org/10.1016/S0925-5273(98)00132-7
Kim, B. S., & Smith, J. S. (2012). Slotting methodology using correlated improvement for a zone-based carton picking distribution system. Computers & Industrial Engineering, 62(1), 286-295. doi:http://dx.doi.org/10.1016/j.cie.2011.09.016
Le-Anh, & Koster, R. M. B. M. d. (2004). MULTI-ATTRIBUTE DISPATCHING RULES FOR AGV SYSTEMS WITH MANY VEHICLES. ERS-2004-077-LIS
Le-Anh, T., & Koster, M. B. M. D. (2004). A Review Of Design And Control Of Automated Guided Vehicle Systems (ERS-2004-030-LIS). Retrieved from
Lin, C.-H., & Lu, I.-Y. (1999). The procedure of determining the order picking strategies in distribution center. International Journal of Production Economics, 60–61, 301-307. doi:http://dx.doi.org/10.1016/S0925-5273(98)00188-1
Liu, C.-M. (2004). Optimal storage layout and order picking for warehousing. International Journal of Operations Research, 1(1), 37-46.
McHaney, R. (1995). Modelling battery constraints in discrete event automated guided vehicle simulations. International Journal of Production Research. doi:10.1080/00207549508904859
Rogiest, W., Laevens, K., Walraevens, J., & Bruneel, H. (2015). Random order of service for heterogeneous customer : waiting time analysis. doi: 10.1007/s10479-014-1721-4
Thomas, L. M., & Meller, R. D. (2015). Developing design guidelines for a case-picking warehouse. International Journal of Production Economics, 741 - 762.
Vidović, M., & Ratković, B. (2015). MODELING APPROACH TO SIMULTANEOUS SCHEDULING BATTERIES AND VEHICLES IN MATERIALS HANDLING SYSTEMS. International Journal for Traffic and Transport Engineering, 5(1).
Wu, L. H., Mok, P. Y., & Zhang, J. (2011). An adaptive multi-parameter based dispatching strategy for single-loop interbay material handling systems. Computers in Industry, 62(2), 175-186. doi:http://dx.doi.org/10.1016/j.compind.2010.10.010