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Multi-period age-discriminated perishable inventory

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, an extremely short shelf-life inventory of age-discriminated stochastic demand is considered. Age discriminated demand can be found in products of high circulation and short shelf-lives such as dairy products, packaged food, pharmaceutical products and medical products of short shelf lives. Simulation based optimization is considered to find the optimal order quantity. The model employs Discrete Event Simulation along with a modified simulated annealing algorithm. To validate the model and the optimization algorithm, the classical newsvendor problem is tested first, later, different experiments are carried out for different product lifetimes. In contrast to the classical newsvendor, this problem tackles a multi-period inventory of different ages and different demand distributions. The objective is to determine the optimal order quantity to satisfy the stochastic demand of all ages such that shortages and expirations are minimized. The results showed remarkable performance and outstanding minimum levels of shortage and expiration.
Wydawca
Rocznik
Tom
Strony
97--105
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
  • The Hashemite University Department of Management Faculty of Economics and Administrative Sciences P.O. Box 150459, Zarqa 13115, Jordan
Bibliografia
  • [1] L. McNeill. (2011). “The retail market: Challenges & opportunities” IBM, Hampshire, UK. Available: http://www03.ibm.com/systems/data/flash/retail/resources/Inventory _Risk_Paper.pdf.v.
  • [2] S. Nahmias. “Perishable inventory theory: a review” Operations Research, vol. 30, 1982, pp. 680-708.
  • [3] I.Z. Karaesmen, A. Scheller-Wolf, B. Deniz. “Planning Production and Inventories in the Extended Enterprise”. International Series in Operations Research & Management Science, vol. 151, 2011, pp. 393-436.
  • [4] X. Chao, X. Gong, C. Shi, H. Zhang. “Approximation algorithms for perishable inventory systems”. Operations Research, vol. 63, no 3, 2015, pp. 585-601.
  • [5] S.D. Pinson, W.P. Pierskalla, B. Schaefer “A computer simulation analysis of blood bank inventory policies”. Technical Report, Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois, 1972.
  • [6] J.B, Jennings, “Blood bank inventory control”. Management Science, vol 19, 1973, pp. 637-645.
  • [7] M.A. Cohen, W.P. Pierskalla. “Management policies for a regional blood bank”. Transfusion, vol 15, 1975, pp. 58-69.
  • [8] C.P. Schmidt, S. Nahmias. “(S-1, S) policies for perishable inventory”. Management Science, vol 31, 1985, pp. 719- 728.
  • [9] D. Perry, M.J.M Posne. “An (S-1, S) inventory system with fixed shelf life and constant lead-times”. Operations Research, vol 46, 1998, pp. 565-571.
  • [10] A. Johnston. “Trends in retail inventory performance: 1982-2012”. Operations Management Research, Volume 7, Issue 3-4, 2014, pp. 86-98.
  • [11] S. Nahmias. “Perishable Inventory Systems” Springer International Series in Operations Research & Management Science, vol. 160, 2011.
  • [12] R. Levi, C. Shi. “Approximation algorithms for the stochastic lot-sizing problem with order lead times”. Operations Research, vol. 61, no 3, 2013, pp. 593-602.
  • [13] D. Chazan, S. Gal. “A Markovian model for a perishable product inventory”. Management Science, vol 23, 1977, pp. 512-521.
  • [14] M.A. Cohen. “Analysis of single critical number ordering policies for perishable inventories”. Operations Research, vol 24, 1976, pp. 726-741.
  • [15] Z. Lian, L. Liu, M.F. Neuts. “A discrete-time model for common lifetime inventory systems”. Mathematics of Operations Research, vol 30, no 3, 2005, pp. 718-732.
  • [16] R. Haijema, J. van der Wal, & N. van Dijk. “Blood platelet production: Optimization by dynamic programming and simulation”. Computers and Operations Research, vol 34, no 3, 2007, pp. 760-779.
  • [17] H. Zhang, C. Shi, X. Chao. “Technical Note – Approximation Algorithms for Perishable Inventory Systems with Setup Costs”. Informs operations research, 2016, pp. 432-440.
  • [18] Q. Duan, T.W. Liao. “A new age-based replenishment policy for supply chain inventory optimization of highly perishable products”. International Journal of Production Economics, vol 145, 2014, pp. 658-671.
  • [19] S.A. Glynn. “The red blood cell storage lesion: A method to the madness”. Transfusion, vol 50, no 6, 2010, pp. 1164- 1169.
  • [20] D. Zhou, Leung, L.C., W.P. Pierskalla. “Inventory management of platelets in hospitals: Optimal inventory policy for perishable products with regular and optional expedited replenishments”. Manufacturing and Service Operations Management, vol 13, no 4, 2011, pp. 420-438.
  • [21] A. Gutierrez-Alcoba, G. Ortega, M.T. Eligius, I. García. “Accelerating an algorithm for perishable inventory control on heterogeneous platforms Original Research Article”. Journal of Parallel and Distributed Computing, vol 104, 2017, pp. 12-18.
  • [22] D. Dalalah, O. Bataineh, K. Alkhaledi. “Platelets inventory management: A rolling horizon Sim-Opt approach for an age-differentiated demand”. Journal of Simulation, vol 13, Issue 3, 2019, pp. 209-225.
  • [23] Z. Lian, L. Liu. “A discrete-time model for perishable inventory systems”. Annual of Operations Research, vol 87, 1999, pp.103-116.
  • [24] T.M. Choi. “Handbook of Newsvendor Problems: Models, Extensions and Applications”. Springer Science & Business Media, 2012.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-959e4277-b9bb-4759-805a-2842d23dae62
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