This paper proposes two aggregation algorithms for aggregating individuals’ linear ordinal rankings (LORs) in group decision-making environment, which improves the computability of LORs. At first, in order to represent the position information of alternatives, we depict LORs by extended preference map, and then analyze the basic statistical quantitative characteristics of the LORs. Subsequently, combining with the concept of granular computing, we derive the arithmetic expressions of interval utility values for alternatives, in which the information granularity indices are introduced to determine the interval lengths. Later on, two programming​ models are established with the aim of minimizing the differences between individual opinions and the corresponding collective one. Besides, in the two models, we propose the methods to determine experts’ weights according to the differences of experts’ knowledge backgrounds. Both information granularity index and the aggregated interval utility value can be calculated by solving the models, based on which the final aggregated ranking can be determined. Furthermore, a numerical case concerning the electric vehicle charging station site selection problem is presented to illustrate the usage of the proposed algorithms, and finally, the efficiency and features of the two algorithms are exhibited through comparative analyses and simulation experiment.

提出了两种在群体决策环境中聚合个人线性有序等级（LOR）的聚合算法，提高了LOR的可计算性。首先，为了表示替代方案的位置信息，我们通过扩展偏好图来描述LOR，然后分析LOR的基本统计定量特征。随后，结合粒度计算的概念，我们得出了替代方案的区间效用值的算术表达式，其中引入了信息粒度指数来确定区间长度。之后，建立了两种编程模型，旨在最大程度地减少个人意见与相应的集体意见之间的差异。此外，在两个模型中 根据专家知识背景的差异，提出了确定专家权重的方法。信息粒度指数和合计区间效用值都可以通过求解模型来计算，基于该模型可以确定最终的合计排名。此外，以一个关于电动汽车充电站选址问题的数值案例为例，说明了该算法的用法，最后，通过比较分析和仿真实验，展示了两种算法的效率和特点。