Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
A multi-objective approach for dynamic missile allocation using artificial neural networks for time sensitive decisions
Date
2021-01-01
Author
Karasakal, Orhan
Karasakal, Esra
Silav, Ahmet
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
364
views
0
downloads
Cite This
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.
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85107454216&origin=inward
https://hdl.handle.net/11511/91101
Journal
Soft Computing
DOI
https://doi.org/10.1007/s00500-021-05923-x
Collections
Department of Industrial Engineering, Article
Suggestions
OpenMETU
Core
Optimal air defense strategies for naval task group
Karasakal, Orhan; Özdemirel, Nur Evin; Department of Industrial Engineering (2004)
We develop solution methods for the air defense problem of a naval task group in this dissertation. We consider two interdependent problems. The first problem is the optimal allocation of a set of defensive missile systems of a naval task group to a set of attacking air targets. We call this problem the Missile Allocation Problem (MAP). The second problem called the Sector Allocation Problem (SAP) is the determination of a robust air defense formation for a naval task group by locating ships in predefined s...
Anti-Ship Missile Defense for a Naval Task Group
Karasakal, Orhan; Özdemirel, Nur Evin; KANDİLLER, LEVENT (Wiley, 2011-04-01)
In this study, we present a new formulation for the air defense problem of warships in a naval task group and propose a solution method. We define the missile allocation problem (MAP) as the optimal allocation of a set of surface-to-air missiles (SAMs) of a naval task group to a set of attacking air targets. MAP is a new treatment of an emerging problem fostered by the rapid increase in the capabilities of anti-ship missiles (ASMs), the different levels of air defense capabilities of the warships against th...
A stochastic disassembly line balancing problem with hazardous tasks
Göksoy Kalaycılar, Eda; Batun, Sakine; Department of Industrial Engineering (2020-11)
In this thesis, we study a Stochastic Disassembly Line Balancing Problem (SDLBP) with hazardous tasks. We define success and failure events for each hazardous task and describe several scenarios over all hazardous tasks. Our aim is to maximize expected profit over all scenarios. We construct mathematical model with one, two and three hazardous tasks. Then, general formulation both in nonlinear and linear form is studied. We test the performance of the model on randomly generated data using the networks take...
Artificial intelligence based dynamic mission planning with probabilistic roadmaps and Voronoi diagrams using predictive launch acceptability region approach
Özdemir, Mustafa Raşit; Ertekin Bolelli, Şeyda; Department of Computer Engineering (2021-9-2)
In this thesis, dynamic air-to-surface mission planning strategies based on probabilistic roadmaps and Voronoi diagrams using predictive launch acceptability region approach are proposed for opportunity targets in order to strengthen decision support capabilities of aircraft. Air-to-surface missions are planned in ground support systems and loaded to aircraft before the mission begins. This means that all the waypoints which should be followed during an air-to-surface mission are planned according to va...
New method for investigation of parameters of real thermoelectric modules
AHISKA, RAŞİT; Ahiska, K. (Elsevier BV, 2010-02-01)
In the present study, a new method to investigate all output parameters of a thermoelectric module has been developed. The new method bases on the thermoemf measurement of a working module and the comparison of the results with those of classical methods. The new method is employed in investigation of the parameters of a standard thermoelectric module of Melcor with production code of CP1.0-127-05L. The theoretical results calculated by the means of new and classical methods for the parameters of an actual ...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
O. Karasakal, E. Karasakal, and A. Silav, “A multi-objective approach for dynamic missile allocation using artificial neural networks for time sensitive decisions,”
Soft Computing
, pp. 0–0, 2021, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85107454216&origin=inward.