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A multi-objective approach for dynamic missile allocation using artificial neural networks for time sensitive decisions

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dc.contributor.author Karasakal, Orhan
dc.contributor.author Karasakal, Esra
dc.contributor.author Silav, Ahmet
dc.date.accessioned 2022-02-23T08:06:38Z
dc.date.available 2022-02-23T08:06:38Z
dc.date.issued 2021-08
dc.identifier.citation Karasakal, Orhan; Karasakal, Esra; Silav, Ahmet (2021). "A multi-objective approach for dynamic missile allocation using artificial neural networks for time sensitive decisions", Soft Computing, Vol. 25, No. 15, pp. 10153-10166. tr_TR
dc.identifier.uri http://hdl.handle.net/20.500.12416/5040
dc.description.abstract In this study, we develop a new solution approach for the dynamic missile allocation problem of a naval task group (TG). The approach considers the rescheduling of the surface-to-air missiles (SAMs), where a set of them have already been scheduled to a set of attacking anti-ship missiles (ASMs). The initial schedule is mostly inexecutable due to disruptions such as neutralization of a target ASM, detecting a new ASM, and breakdown of a SAM system. To handle the dynamic disruptions while keeping efficiency high, we use a bi-objective model that considers the efficiency of SAM systems and the stability of the schedule simultaneously. The rescheduling decision is time-sensitive, and the amount of information to be processed is enormous. Thus, we propose a novel approach that supplements the decision-maker (DM) in choosing a Pareto optimal solution considering two conflicting objectives. The proposed approach uses an artificial neural network (ANN) that includes an adaptive learning algorithm to structure the DM's prior articulated preferences. ANN acts like a DM during the engagement process and chooses one of the non-dominated solutions in each rescheduling time point. We assume that the DM's utility function is consistent with a non-decreasing quasi-concave function, and the cone domination principle is incorporated into the solution procedure. An extensive computational study is provided to present the effectiveness of the proposed approach. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1007/s00500-021-05923-x tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Dynamic Weapon Target Allocation Problem tr_TR
dc.subject Air Defense tr_TR
dc.subject Rescheduling tr_TR
dc.subject Artificial Neural Network tr_TR
dc.title A multi-objective approach for dynamic missile allocation using artificial neural networks for time sensitive decisions tr_TR
dc.type article tr_TR
dc.relation.journal Soft Computing tr_TR
dc.contributor.authorID 216553 tr_TR
dc.identifier.volume 25 tr_TR
dc.identifier.issue 15 tr_TR
dc.identifier.startpage 10153 tr_TR
dc.identifier.endpage 10166 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü tr_TR


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