Bir Deniz Görev Grubu için Satıhtan Havaya Güdümlü Mermilerin Dinamik Çizelgelemesi

Bu çalışmada, bir deniz görev grubunun hava savunma problemine yönelik, başlangıçtaki hava tehdit setine göre optimize edilen çizelgeleme planının, angajmanlar başladıktan sonra yeniden değerlendirilmesi ihtiyacı için iki amaçlı satıhtan havaya güdümlü mermi çizelgeleme modeli önerilmiştir. Modeldeki, ilk amaç hava tehditlerinin tamamını imha etme olasılığının ençoklanması, ikinci amaç ise ilk tahsis planı ile yeni tahsis planı arasındaki değişim miktarının enazlanmasıdır. Problemde, yeniden çizelgeleme ihtiyacı için üç durum dikkate alınmıştır. Bunlar; herhangi bir tehdidin imha edilmesi ve o tehdit için planlı sonraki angajmanların başka hedeflere tahsis edilebilmesi, satıhtan havaya güdümlü mermi sisteminin arızalanması ve yeni bir hedefin ortaya çıkması durumlarıdır. Her bir durum için iki amaçlı model öncelikle epsilon kısıt yöntemiyle çözülmüş ve tüm etkin çözümler üretilmiştir. Yeniden tahsis yaklaşımının amaç fonksiyonlarında hangi durumlarda ne kadarlık bir değişim sağladığı tanımlanan metriklerle ölçülmüştür. Bunun yanında, problemin NP-zor bir problem olduğu ispatlanmış ve büyük problemleri çözebilmek için iki farklı sezgisel yöntem geliştirilmiştir. Önerilen sezgisel yöntemlerin performansı incelenmiş ve her durum için iyi sonuçların elde edildiği görülmüştür

Multiobjective aerial surveillance over disjoint rectangles

In Aerial Surveillance Problem (ASP), an air platform with surveillance sensors searches a number of rectangular areas by covering the rectangles in strips and turns back to base where it starts. In this paper, we present a multiobjective extension to ASP, for which the aim is to help aerial mission planner to reach his/her most preferred solution among the set of efficient alternatives. We consider two conflicting objectives that are minimizing distance travelled and maximizing minimum probability of target detection. Each objective can be used to solve single objective ASPs. However, from mission planner's perspective, there is a need for simultaneously optimizing both objectives. To enable mission planner reaching his/her most desirable solution under conflicting objectives, we propose exact and heuristic methods for multiobjective ASP (MASP). We also develop an interactive procedure to help mission planner choose the most satisfying solution among all Pareto optimal solutions. Computational results show that the proposed methods enable mission planner to capture the tradeoffs between the conflicting objectives for large number of alternative solutions and to eliminate the undesirable solutions in small number of iterations

Multiple Criteria Target Classification Using Heterogeneous Sensor Data

Radar systems have important roles in both military and civilian applications. As the capabilities increase in terms of range, sensitivity and the number of tracks to be handled, the requirement for automatic target recognition (ATR) increase. ATR systems are used as decision support systems to classify the potential targets in military applications. These systems are composed of four phases, which are selection of sensors, preprocessing of the radar data, feature extraction and selection, and processing of features to classify the potential target. In this study, we focus on the classification phase of ATR and develop a novel multiple criteria classification method based on modified Dempster Shafer data fusion algorithm. Ensemble of classifiers are used as a classification algorithm. They are treated as the state of the art technology for classification in which each single classifier is trained separately, and then the results of them are combined through several fusion algorithms. Support vector machine and neural network are employed as probabilistic classifiers in ensemble. Each non-imaginary dataset coming from multiple heterogeneous sensors is classified by both of the classifiers in the ensemble, and the classification result that has higher accuracy ratio is chosen for each of the sensor dataset. After getting probabilistic classification of targets by different sensors, modified Dempster Shafer data fusion algorithm is used to combine the sensors’ results to reach the final classification of the targets. In this talk, a number of classification algorithms are compared with the proposed algorithm and the results will be discussed

AN APPROACH FOR EXTENDING PROMETHEE TO REFLECT CHOICE BEHAVIOUR OF THE DECISION MAKER

In this study, an approach based on PROMETHEE is developed to correctly reflect the choice behavior of the decision maker that is not explained by the utility theory. The prospect theory argues that losses have higher impact than gains. We integrate the prospect theory into PROMETHEE through defining new preference functions. The proposed approach is behaviorally realistic and tolerates some degree of intransitivities in the preferences of the decision maker. For determining the criteria weights, we utilize pairwise comparison method of Analytic Hierarchy Process. Performance of the approach is demonstrated on a university ranking problem.Bu çalışmada, karar vericinin fayda teorisi ile açıklanamayan seçim davranışını doğru bir şekilde yansıtabilmek için PROMETHEE yöntemini temel alan bir yaklaşım geliştirilmiştir. Seçim davranışı teorisi, zararların kazançlardan daha yüksek etkisinin olduğunu ileri sürmektedir. Bu teori PROMETHEE yöntemine yeni tercih fonksiyonları tanımlamak suretiyle entegre edilmiştir. Önerilen yaklaşım, davranışsal olarak gerçekçi ve karar vericinin tercihlerinde oluşabilecek geçişsiz değerlendirmelere izin veren bir yöntemdir. Kriter ağırlıklarının belirlenmesinde Analitik Hiyerarşi Süreci yaklaşımındaki ikili karşılaştırma metodu kullanılmıştır. Önerilen yaklaşımın etkinliği bir üniversite sıralama problem üzerinde gösterilmiştir

A partial coverage hierarchical location allocation model for health services

We consider a hierarchical maximal covering location problem (HMCLP) to locate health centres and hospitals so that the maximum demand is covered by two levels of services in a successively inclusive hierarchy. We extend the HMCLP by introducing the partial coverage and a new definition of the referral. The proposed model may enable an informed decision on the healthcare system when dynamic adaptation is required, such as a COVID-19 pandemic. We define the referral as coverage of health centres by hospitals. A hospital may also cover demand through referral. The proposed model is solved optimally for small problems. For large problems, we propose a customised genetic algorithm. Computational study shows that the GA performs well, and the partial coverage substantially affects the optimal solutions. [Submitted: 20 January 2021; Accepted: 15 January 2022

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

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 epsilon-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