Anti‐ship missile defense for a naval task group

Karasakal, Orhan; Özdemirel, Nur Evin; Kandiller, Levent

doi:10.1002/nav.20457

Cited by 24 publications

(20 citation statements)

References 27 publications

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“…MAP is the allocation of defensive systems to attacking missiles under the present or assumed formation, so as to maximize the probability of no leaker for the TG. Interested readers are referred to Karasakal et al [7] for detailed description and a MAP solution approach. A scenario typically involves the number and type of ASMs, attack directions, detection distances of ASMs, defending SAM systems, the number of available missiles in the magazines, and the TG formation.…”

Section: Derivation Of the Coverage Parametermentioning

confidence: 99%

A branch and bound algorithm for sector allocation of a naval task group

Karasakal

Kandiller

Özdemirel

2011

Naval Research Logistics

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Abstract:A naval task group (TG) is a collection of naval combatants and auxiliaries that are grouped together for the accomplishment of one or more missions. Ships forming a TG are located in predefined sectors. We define determination of ship sector locations to provide a robust air defense formation as the sector allocation problem (SAP). A robust formation is one that is very effective against a variety of attack scenarios but not necessarily the most effective against any scenario. We propose a 0-1 integer linear programming formulation for SAP. The model takes the size and the direction of threat into account as well as the defensive weapons of the naval TG. We develop tight lower and upper bounds by incorporating some valid inequalities and use a branch and bound algorithm to exactly solve SAP. We report computational results that demonstrate the effectiveness of the proposed solution approach.

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Section: Derivation Of the Coverage Parametermentioning

confidence: 99%

A branch and bound algorithm for sector allocation of a naval task group

Karasakal

Kandiller

Özdemirel

2011

Naval Research Logistics

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“…Interceptors priorities calculation and firing decision-making. For THD interceptors from 1 to 32 (TLD interceptors from 1 to 8), calculate their priorities value according to (7) and (8), and then compare the priorities value of available interceptors, select the maximum one to shot. Note that if several interceptors share a same priority value, select the smaller (number) one to shot; if an interceptor already assigned or launched, then its priority value is zero.…”

Section: Experimental Examplesmentioning

confidence: 99%

“…Bracken and McCormick presented a nonlinear approximation to a weapon allocation problem where the goal is to maximize expected inflicted damage [3]. Orhan Karasakal presented the issue of allocating air defense missiles to incoming air targets in order to maximize the air defense effectiveness of a naval task group [8]. Rest of the literature proposed different aspects within the WTA problem, interested readers can referred to [9][10][11][12] for a recent survey on WTA problem.…”

Section: Introductionmentioning

confidence: 99%

Modified particle swarm optimization for BMDS interceptor resource planning

Fu-xian

Long

et al. 2015

Appl Intell

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One important mission of the strategic defense is to develop an integrated, layered Ballistic Missile Defense System (BMDS). We consider the problem of assigning interceptors to multiple waves of incoming ballistic missiles. This work addresses the issue of shot time constraints and directly allocates multiple targets to interceptors in the single engagement problem. A mathematical model and method for BMDS Interceptor resource planning (IRP) is presented. In addition to mathematical model, a modified particle swarm optimization RPPSO algorithm has been developed for solving complex IRP problem. This algorithm adapted a basic PSO algorithm to make it compatible with reasonable engineering problems. RPPSO was developed by embedding a reverse predictor within the basic algorithm to avoid premature convergence without significantly reducing convergence speed, and adding a repulsive force to keep the diversity of both local and global optima.

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“…Karasakal () models the MAP to maximize the effectiveness of a TG. Karasakal, Özdemirel, and Kandiller () develop a discrete model to generate the optimal engagement schedule for a TG. They address the launching of SAM rounds according to the shoot‐look‐shoot (SLS) engagement policy or its variations, considering multiple SAM systems and ASM types.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Thus, SAM systems do not shoot against each ASM until the previous shot has completed. Refer to Glazebrook and Washburn () for a comprehensive review and extension on SLS policy and Karasakal, Özdemirel, et al () Section 5.3 for adaptation of SLS policy to variants such as shoot‐shoot‐look‐shoot‐shoot policy. Figure shows a schematic of the time slots, time horizon, and the starting time of the slots.…”

Section: Description Of the Problem And The Modelmentioning

confidence: 99%

Bi‐objective missile rescheduling for a naval task group with dynamic disruptions

Silav

Karasakal

2019

Naval Research Logistics

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This paper considers the rescheduling of surface‐to‐air missiles (SAMs) for a naval task group (TG), where a set of SAMs have already been scheduled to intercept a set of anti‐ship missiles (ASMs). In missile defense, the initial engagement schedule is developed according to the initial state of the defensive and attacking units. However, unforeseen events may arise during the engagement, creating a dynamic environment to be handled, and making the initial schedule infeasible or inefficient. In this study, the initial engagement schedule of a TG is assumed to be disrupted by the occurrence of a destroyed ASM, the breakdown of a SAM system, or an incoming new target ASM. To produce an updated schedule, a new biobjective mathematical model is formulated that maximizes the no‐leaker probability value for the TG and minimizes the total deviation from the initial schedule. With the problem shown to be NP‐hard, some special cases are presented that can be solved in polynomial time. We solve small size problems by the augmented ϵ‐constraint method and propose heuristic procedures to generate a set of nondominated solutions for larger problems. The results are presented for different size problems and the total effectiveness of the model is evaluated.

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Anti‐ship missile defense for a naval task group

Cited by 24 publications

References 27 publications

A branch and bound algorithm for sector allocation of a naval task group

A branch and bound algorithm for sector allocation of a naval task group

Modified particle swarm optimization for BMDS interceptor resource planning

Bi‐objective missile rescheduling for a naval task group with dynamic disruptions

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