000101200 001__ 101200
000101200 005__ 20230519145447.0
000101200 0247_ $$2doi$$a10.1017/S0269964819000329
000101200 0248_ $$2sideral$$a123056
000101200 037__ $$aART-2021-123056
000101200 041__ $$aeng
000101200 100__ $$0(orcid)0000-0002-6651-3306$$aBadía, F.G.$$uUniversidad de Zaragoza
000101200 245__ $$aLog-concavity of compound distributions with applications in operational and actuarial models
000101200 260__ $$c2021
000101200 5060_ $$aAccess copy available to the general public$$fUnrestricted
000101200 5203_ $$aWe establish that a random sum of independent and identically distributed (i.i.d.) random quantities has a log-concave cumulative distribution function (cdf) if (i) the random number of terms in the sum has a log-concave probability mass function (pmf) and (ii) the distribution of the i.i.d. terms has a non-increasing density function (when continuous) or a non-increasing pmf (when discrete). We illustrate the usefulness of this result using a standard actuarial risk model and a replacement model.We apply this fundamental result to establish that a compound renewal process observed during a random time interval has a log-concave cdf if the observation time interval and the inter-renewal time distribution have log-concave densities, while the compounding distribution has a decreasing density or pmf. We use this second result to establish the optimality of a so-called (s, S) policy for various inventory models with a stock-out cost coefficient of dimension [$/unit], significantly generalizing the conditions for the demand and leadtime processes, in conjunction with the cost structure in these models. We also identify the implications of our results for various algorithmic approaches to compute optimal policy parameters. Copyright
000101200 536__ $$9info:eu-repo/grantAgreement/ES/DGA/S11$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/MTM2015-63978-P
000101200 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000101200 590__ $$a1.561$$b2021
000101200 592__ $$a0.357$$b2021
000101200 594__ $$a1.5$$b2021
000101200 591__ $$aSTATISTICS & PROBABILITY$$b63 / 125 = 0.504$$c2021$$dQ3$$eT2
000101200 593__ $$aIndustrial and Manufacturing Engineering$$c2021$$dQ2
000101200 591__ $$aENGINEERING, INDUSTRIAL$$b43 / 50 = 0.86$$c2021$$dQ4$$eT3
000101200 593__ $$aStatistics, Probability and Uncertainty$$c2021$$dQ2
000101200 591__ $$aOPERATIONS RESEARCH & MANAGEMENT SCIENCE$$b69 / 87 = 0.793$$c2021$$dQ4$$eT3
000101200 593__ $$aManagement Science and Operations Research$$c2021$$dQ2
000101200 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000101200 700__ $$0(orcid)0000-0002-7099-7665$$aSangüesa, C.$$uUniversidad de Zaragoza
000101200 700__ $$aFedergruen, A.
000101200 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000101200 773__ $$g35, 4 (2021), 201-235$$pProbab. eng. inf. sci.$$tPROBABILITY IN THE ENGINEERING AND INFORMATIONAL SCIENCES$$x0269-9648
000101200 8564_ $$s163331$$uhttps://zaguan.unizar.es/record/101200/files/texto_completo.pdf$$yVersión publicada
000101200 8564_ $$s1330032$$uhttps://zaguan.unizar.es/record/101200/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000101200 909CO $$ooai:zaguan.unizar.es:101200$$particulos$$pdriver
000101200 951__ $$a2023-05-18-14:39:04
000101200 980__ $$aARTICLE