Department of Logistics

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Browsing Department of Logistics by Subject "Air defenses"

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    Modelling weapon assignment as a multiobjective decision problem
    (Stellenbosch : Stellenbosch University, 2012-03) Lotter, Daniel Petrus; Nieuwoudt, I.; Van Vuuren, J. H.; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Logistics.
    ENGLISH ABSTRACT: In a ground-based air defense (GBAD) military environment, defended assets on the ground require protection from enemy aircraft entering the defended airspace. These aircraft are detected by means of a network of sensors and protection is afforded by means of a pre-deployment of various ground-based weapon systems. A fire control officer is responsible for deciding upon an assignment of weapon systems to those aircraft classified as threats. The problem is therefore to find the best set of weapon systems to assign to the threats, based on some pre-specified criterion or set of criteria. This problem is known as the weapon assignment problem. The conditions under which the fire control officer has to operate are typically extremely stressful. A lack of time is a severely constraining factor, and the fire control officer has to propose an assignment of weapon systems to threats based on his limited knowledge and intuition, with little time for analysis and no room for error. To aid the fire control officer in this difficult decision, a computerised threat evaluation and weapon assignment (TEWA) decision support system is typically employed. In such a decision support system a threat evaluation subsystem is responsible for classifying aircraft in the defended airspace as threats and prioritising them with respect to elimination, whereas a weapon assignment subsystem is responsible for proposing weapon assignments to engage these threats. The aim in this thesis is to model the weapon assignment problem as a multiobjective decision problem. A list of relevant objectives is extracted by means of feedback received from a weapon assignment questionnaire which was completed by a number of military experts. By using two of these objectives, namely the cost of assigning weapon systems and the accumulated single shot hit probability, for illustrative purposes, a bi-objective weapon assignment model is derived and solved by means of three multiobjective optimisation methodologies from the literature in the context of a simulated, but realistic, GBAD scenario. The analytic hierarchy process (AHP) is implemented by means of assessments carried out in conjunction with a military expert. The assignment of weapon systems to threats is achieved by means of a greedy assignment heuristic and an AHP assignment model. Both these methods provide plausible results in the form of high quality assignments achieving an acceptable tradeoff between the two decision objectives. However, a disadvantage of the AHP approach is that it is inflexible in the sense that a large portion of its pre-assessments have to be reiterated if the set of weapon systems and/or threats is adapted or updated. A bi-objective additive utility function solution approach to the weapon assignment problem is also developed as a result of various assessments having been carried out in conjunction with a military expert. The assignment of weapon systems to threats is again achieved by means of a greedy assignment heuristic and a utility assignment model. Both these methods again provide high quality assignments of weapon systems to threats, achieving an acceptable trade-off between the two decision objectives. However, a disadvantage of the utility function approach is that if additional weapon systems are added to the current set of weapon systems, which achieve objective function values outside the current ranges of the values employed, new utility functions have to be determined for the relevant objective function. Moreover, both the AHP and utility function approaches are also constrained by generating only one solution at a time. A final solution approach considered is the implementation of a multiobjective evolutionary metaheuristic, known as the Nondominated Sorting Genetic Algorithm II (NSGA II). This approach provides very promising results with respect to high quality assignments of weapon systems to threats. It is also flexible in the sense that additional weapon systems and threats may be added to the current sets without the need of considerable additional computations or significant model changes. A further advantage of this approach is that it is able to provide an entire front of approximately pareto optimal solutions to the fire control officer.