In this paper we introduce a bi-objective location-allocation problem for Unmanned Aircraft Systems (UASs) operating in a hostile environment. The objective is to find the locations to deploy UASs and assign Unmanned Aerial Vehicles to regions for surveillance. One of the objectives is to maximize search effectiveness, while the second is the minimization of the threats posed to the UASs. These two objectives are in conflict, because they are affected differently by the proximity between the UAS locations and the target regions. First, we have formulated this problem as a mixed integer nonlinear program. Next, we have developed its linearization which can be solved by a commercial optimizer for small-scale problem instances. To solve large-scale problems, we have adopted a well-known metaheuristic for multi-objective problems, namely the elitist non-dominated sorting genetic algorithm. We have also developed a hybrid approach, which has proven to be more effective than each approach alone.

在本文中，我们介绍了在敌对环境中运行的无人机系统（UAS）的双目标位置分配问题。目的是找到部署UAS的位置，并将无人飞行器分配到监视区域。目标之一是最大程度地提高搜索效率，而第二个目标是最小化对UAS构成的威胁。这两个目标是冲突的，因为它们受到UAS位置和目标区域之间邻近程度的不同影响。首先，我们将此问题表述为混合整数非线性程序。接下来，我们开发了它的线性化，可以通过商业优化器针对小规模的问题实例进行求解。为了解决大规模问题，我们针对多目标问题采用了著名的元启发式方法，即精英非支配排序遗传算法。我们还开发了一种混合方法，已证明比单独使用每种方法更有效。