000151069 001__ 151069
000151069 005__ 20250221105703.0
000151069 0247_ $$2doi$$a10.1016/j.ejor.2022.03.018
000151069 0248_ $$2sideral$$a129219
000151069 037__ $$aART-2022-129219
000151069 041__ $$aeng
000151069 100__ $$aGorria, C.
000151069 245__ $$aPerformance measures of nonstationary inventory models for perishable products under the EWA policy
000151069 260__ $$c2022
000151069 5060_ $$aAccess copy available to the general public$$fUnrestricted
000151069 5203_ $$aAccurately estimating key performance indicators in inventory models for perishable items is essential in order to assess and improve the management strategy of these systems. We analyse the production of platelet concentrates at blood banks under the EWA replenishment policy. We give analytical approximations of the most important performance measures, such as the size of orders, the size of stocks, the percentage of outdating, the age distribution of stocks and the freshness of units issued, among others. The production of platelet concentrates is a prototypical example of inventory models for short life items with random demand and a weekly pattern, where a high service level is required. The methodology and the approximations presented here can be easily adapted to other inventory systems with similar characteristics. Most of the formulae in this article are new for nonstationary models under the EWA policy; indeed, formulae for the age distribution of units in stock and of units issued have not appeared in the literature even for the simpler base-stock replenishment policy. We apply our results to a real blood bank and find very close agreement between the formulae and the results of Monte Carlo simulations. The accuracy of our approximations is also tested in several scenarios, depending on the lifetime of units, safety stock levels and the probabilistic distribution of demand. © 2022 The Author(s)
000151069 536__ $$9info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2020-116873GB-I00
000151069 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000151069 590__ $$a6.4$$b2022
000151069 591__ $$aOPERATIONS RESEARCH & MANAGEMENT SCIENCE$$b13 / 86 = 0.151$$c2022$$dQ1$$eT1
000151069 592__ $$a2.371$$b2022
000151069 593__ $$aComputer Science (miscellaneous)$$c2022$$dQ1
000151069 593__ $$aIndustrial and Manufacturing Engineering$$c2022$$dQ1
000151069 593__ $$aModeling and Simulation$$c2022$$dQ1
000151069 593__ $$aManagement Science and Operations Research$$c2022$$dQ1
000151069 593__ $$aInformation Systems and Management$$c2022$$dQ1
000151069 594__ $$a11.2$$b2022
000151069 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000151069 700__ $$aLezaun, M.
000151069 700__ $$0(orcid)0000-0002-7615-2559$$aLópez, F. J.$$uUniversidad de Zaragoza
000151069 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000151069 773__ $$g303, 3 (2022), 1137-1150$$pEur. J. oper. res.$$tEuropean Journal of Operational Research$$x0377-2217
000151069 8564_ $$s781792$$uhttps://zaguan.unizar.es/record/151069/files/texto_completo.pdf$$yVersión publicada
000151069 8564_ $$s2801320$$uhttps://zaguan.unizar.es/record/151069/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000151069 909CO $$ooai:zaguan.unizar.es:151069$$particulos$$pdriver
000151069 951__ $$a2025-02-21-09:53:45
000151069 980__ $$aARTICLE