Strategic Decision Making
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
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This paper presents a methodological framework developed to address a tactical vehicle fleet mix optimization problem in a military context. Tactical vehicles are used to fulfill a variety of operational roles that require specific performance capabilities. The problem complexity is attributed to the fact that the vehicle role requirements are not well defined in practice and are dependent on various qualitative operational aspects. To address the problem, a decision analysis framework is developed to define the role requirements and to assess the vehicle performance. An optimization model is then formulated to determine the optimal vehicle mix under different operational constraints. The model is considered in a multi-objective format and the Pareto optimal front is determined through an exhaustive search. Single and multi-objective solution trade-offs are compared and discussed. An illustrative example is presented to demonstrate the methodology.