000079083 001__ 79083
000079083 005__ 20220208112845.0
000079083 0247_ $$2doi$$a10.1016/j.ejor.2018.02.041
000079083 0248_ $$2sideral$$a106378
000079083 037__ $$aART-2018-106378
000079083 041__ $$aeng
000079083 100__ $$0(orcid)0000-0001-7603-9380$$aCalvete, H.I.$$uUniversidad de Zaragoza
000079083 245__ $$aDealing with residual energy when transmitting data in energy-constrained capacitated networks
000079083 260__ $$c2018
000079083 5060_ $$aAccess copy available to the general public$$fUnrestricted
000079083 5203_ $$aThis paper addresses several problems relating to the energy available after the transmission of a given amount of data in a capacitated network. The arcs have an associated parameter representing the energy consumed during the transmission along the arc and the nodes have limited power to transmit data. In the first part of the paper, we consider the problem of designing a path which maximizes the minimum of the residual energy remaining at the nodes. After formulating the problem and proving the main theoretical results, a polynomial time algorithm is proposed based on computing maxmin paths in a sequence of non-capacitated networks. In the second part of the paper, the problem of obtaining a quickest path in this context is analyzed. First, the bi-objective variant of this problem is considered in which we aim to minimize the transmission time and to maximize the minimum residual energy. An exact polynomial time algorithm is proposed to find a minimal complete set of efficient solutions which amounts to solving shortest path problems. Second, the problem of computing an energy-constrained quickest path which guarantees at least a given residual energy at the nodes is reformulated as a variant of the energy-constrained quickest path problem. The algorithms are tested on a set of benchmark problems providing the optimal solution or the Pareto front within reasonable computing times.
000079083 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/E58
000079083 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000079083 590__ $$a3.806$$b2018
000079083 591__ $$aOPERATIONS RESEARCH & MANAGEMENT SCIENCE$$b13 / 84 = 0.155$$c2018$$dQ1$$eT1
000079083 592__ $$a2.205$$b2018
000079083 593__ $$aComputer Science (miscellaneous)$$c2018$$dQ1
000079083 593__ $$aModeling and Simulation$$c2018$$dQ1
000079083 593__ $$aManagement Science and Operations Research$$c2018$$dQ1
000079083 593__ $$aInformation Systems and Management$$c2018$$dQ1
000079083 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000079083 700__ $$0(orcid)0000-0001-8686-3963$$adel-Pozo, L.$$uUniversidad de Zaragoza
000079083 700__ $$0(orcid)0000-0001-9993-9816$$aIranzo, J.A.
000079083 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000079083 773__ $$g269, 2 (2018), 602-620$$pEur. J. oper. res.$$tEuropean Journal of Operational Research$$x0377-2217
000079083 8564_ $$s968912$$uhttps://zaguan.unizar.es/record/79083/files/texto_completo.pdf$$yPostprint
000079083 8564_ $$s46775$$uhttps://zaguan.unizar.es/record/79083/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000079083 909CO $$ooai:zaguan.unizar.es:79083$$particulos$$pdriver
000079083 951__ $$a2022-02-08-11:23:45
000079083 980__ $$aARTICLE