On stockpile planning using a multi-objective genetic algorithm

R. Pall, E. Cheung
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引用次数: 0

Abstract

The North Atlantic Treaty Organization (NATO) Stockpile Planning Committee (SPC) periodically determines if NATO member nations have the necessary munitions for a full range of mission types, accomplished through the use of a model that minimizes the cost of the required stockpile. We were tasked to examine how the methodology of this model could be modified to allow individual nations to better determine their requirements for Precision-Guided Munitions (PGMs). The approach we undertook involves augmenting the methodology of the model with a multi-objective optimization approach using a genetic algorithm, in which the solution is optimized along two competing objectives: total cost (which is minimized), and the usage of PGMs (which is maximized). We recommended that the SPC consider including this change in all future versions of ACROSS.
基于多目标遗传算法的库存规划
北大西洋公约组织(NATO)库存计划委员会(SPC)定期确定北约成员国是否拥有各种任务类型所需的弹药,通过使用最小化所需库存成本的模型来完成。我们的任务是研究如何修改该模型的方法,以使各个国家能够更好地确定其对精确制导弹药(pgm)的需求。我们采用的方法包括使用遗传算法的多目标优化方法来扩展模型的方法,其中解决方案沿着两个相互竞争的目标进行优化:总成本(最小化)和pgm的使用(最大化)。我们建议SPC考虑在ACROSS的所有未来版本中包含此更改。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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