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Decision making strategies for warehouse operations

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents issues related to design and organization of warehouse processes. Draws attention to problems of modelling logistics facilities and processes occurring in them. Paper also points out the essence of correct operation of warehouses and associated with this risks for supply chain. Warehouse processes have been properly defined and divided into smaller components, like: phases of process, activities, sets of procedures, procedures or working movements. Paper presents various possible configurations of warehouse processes, which were named as warehouse strategies. Warehouse process strategy is a part of warehousing. It determines the sequence of warehouse operations. These operations are related to internal transport, cargo form transformation and cargo buffering. In paper it is noted that the warehouse strategy depends on the several technical and organizational factors and is associated with a certain probability. This probability can be estimated based on data analysis concerning logistics processes in the specified warehouse facility. As shown in case study, appropriate selection of warehouse strategies determines possibility of fulfilling customer order in time and costs that are acceptable for him.
Rocznik
Strony
43--53
Opis fizyczny
Bibliogr. 28 poz., rys., tab.
Twórcy
  • Warsaw University of Technology, Faculty of Transport, Warsaw, Poland
autor
  • Warsaw University of Technology, Faculty of Transport, Warsaw, Poland
  • Warsaw University of Technology, Faculty of Production Engineering, Warsaw, Poland
autor
  • Warsaw University of Technology, Faculty of Transport, Warsaw, Poland
Bibliografia
  • [1] BAKER, P. & CANESSA, M., 2009. Warehouse design: A structured approach. European Journal of Operational Research, 193, 425–436.
  • [2] BARTHOLDI III, J.J., EISENSTEIN, D.D. & FOLEY, R.D., 2001. Performance of bucket brigades when work is stochastic. Operations Research, 49(5), 1-22.
  • [3] BASSAN, Y., ROLL, Y. & ROSENBLATT, M. J., 1980. Internal layout design of a warehouse. AIIE Transactions, 12(4), 317-322.
  • [4] BERRY, J.R., 1968. Elements of warehouse layout, International Journal of Production Research, 7(2), 105-121.
  • [5] BOZER, Y.A. & CHO, M., 2005. Throughput performance of automated storage/retrieval systems under stochastic demand. IIE Transactions, 37.
  • [6] BOZER, Y.A. & PARK, J.H., 1999. Expected move request waiting times in single-device, polling-based material handling systems. IIE Transactions, 33.
  • [7] BOZER, Y.A. & WHITE, J.A., 1990. Design and performance models for end-of-aisle order picking systems. Management Science, 36.
  • [8] CHEW, E.P. & TANG, L.C., 1999. Travel time analysis for general item location assignment in a rectangular warehouse. European Journal of Operational Research, 112.
  • [9] DE KOSTER, M.B.M., 1994. Performance approximation of pick-to-belt orderpicking systems. European Journal of Operational Research, 72, 582–597.
  • [10] FRAZELLE, E., 2002. World-Class Warehousing and Material Handling. McGraw-Hill.
  • [11] GONG, Y. & DE KOSTER, M.B.M., 2008. A polling-based dynamic order picking system for online retailers. IIE Transactions, 40.
  • [12] GU, J., GOETSCHALCKX, M. & MCGINNIS, L. F., 2010. Research on warehouse design and performance evaluation: A comprehensive review. European Journal on Operational Research. 203, 539-549.
  • [13] GUE, K.R., MELLER, R.D. & SKUFCA, J.D., 2006. The effects of pick density on order picking areas with narrow aisles. IIE Transactions 38.
  • [14] HSIEH, S., HWANG, J.S. & CHOU, H.C., 1998. A Petri-net-based structure for AS/RS operation modelling. International Journal of Production Research, 36.
  • [15] JACYNA, M., KŁODAWSKI, M. & SZCZEPAŃSKI, E., 2016. Designing and management of storage facilities: The SIMMAG3D as an advanced decision support system. In R. KENSYS (Eds.), 20th International Conference Transport Means 2016. Proceedings (pp. 846-851), Publishing House "Technologija”.
  • [16] JACYNA, M., 2014. Kształtowanie systemów w wybranych obszarach transportu i logistyki. Oficyna Wydawnicza Politechniki Warszawskiej.
  • [17] JACYNA-GOŁDA, I., 2014. Some aspects of risk assessment in the logistics chain. Journal of KONES. Institute of Aviation, 21(4), 193-202.
  • [18] JACYNA-GOŁDA, I., 2015. Decision-making model for supporting supply chain efficiency evaluation. Archive of Transport, 33(1).
  • [19] KARKULA, M., 2014. Selected aspects of simulation modelling of internal transport processes performed at logistics facilities. Archive of Transport, 30(2).
  • [20] KŁODAWSKI, M. & JACYNA, M., 2012. Selected aspects of research on order picking productivity in aspect of congestion problems, Proceedings of International Conference on Industrial Logistics ICIL 2012.
  • [21] KŁODAWSKI, M. & ŻAK, J., 2013. Order picking area layout and its impact on efficiency of order picking process. Journal of Traffic and Logistics Engineering, 1(1), 41-46.
  • [22] LE-DUC, T. & DE KOSTER, M.B.M., 2005. Travel distance estimation and storage zone optimization in a 2-block class-based storage strategy warehouse. International Journal of Production Research 43.
  • [23] LEE, F.H., 1997. Performance analysis for automated storage and retrieval systems. IIE Transactions 29.
  • [24] LEWCZUK, K. & ŻAK, J., 2013. Supportive role of dynamic order picking area in distribution warehouse, Journal of Traffic and Logistics Engineering, 1(1).
  • [25] LIN, S.C. & WANG, H.P., 1995. Modelling an automated storage and retrieval system using Petri nets. International Journal of Production Research 33.
  • [26] MACRO, J.G. & SALMI, R.E., 2002. A simulation tool to determine warehouse efficiencies and storage allocations. In. YUCESAN, E., CHEN, C.H., SNOWDON, J.L. & CHARNES, J.M. (Eds.), Proceedings of the 2002 Winter Simulation Conference, 1274–1281.
  • [27] ROBERTS, S.D. & REED, R., 1972. Optimal warehouse bay configurations. AIIE Transactions, 4(3), 178-185.
  • [28] ROSENBLATT, M.J. & ROLL, Y., 1988. Warehouse capacity in a stochastic environment. International Journal of Production Research 26.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ce430709-3a69-4110-9608-5fb088173815
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