In this paper, a multi-coalition fixed cost allocation (MCFCA) is considered. The problem is solved using epsilon equilibrium (also called ε-Nash equilibrium) concept and data envelopment analysis framework. The MCFCA epsilon equilibrium involves tradeoffs between satisfying objectives of cooperative coalitions and conflicts arising from inter-coalitions objectives. First, it is shown that a simple single objective fixed cost allocation problem is an easy problem to solve and multiple solutions satisfying traditional Nash equilibrium can be trivially obtained. Second, the MCFCA problem is proposed by using different coalitions’ objective criteria highlighted in previous studies. Third, the MCFCA problem is solved using a genetic algorithm (GA) procedure. Several real-world datasets from different domains are used to test the procedure and two different types of GA fitness functions – raw and scaled – are used. The results indicate that different fitness functions perturb solutions around the epsilon equilibrium so that multiple statistically similar solutions can be obtained to aid subjective managerial solution selection among multiple similar solutions.

在本文中，考虑了多联盟固定成本分配（MCFCA）。该问题使用 epsilon 平衡（也称为ε-纳什均衡）概念和数据包络分析框架。MCFCA epsilon 均衡涉及在满足合作联盟的目标和由联盟间目标引起的冲突之间的权衡。首先，它表明一个简单的单一目标固定成本分配问题是一个容易解决的问题，并且可以很容易地获得满足传统纳什均衡的多个解。其次，MCFCA 问题是通过使用先前研究中强调的不同联盟的客观标准提出的。第三，使用遗传算法 (GA) 程序解决 MCFCA 问题。来自不同领域的几个真实世界数据集用于测试该过程，并使用两种不同类型的 GA 适应度函数 - 原始和缩放 - 。