考慮多階段決策信息集結(jié)的新算法及其應(yīng)用

摘要： 針對(duì)多階段區(qū)間信息集結(jié)與決策問(wèn)題,提出一種考慮最小化集結(jié)矩陣與階段區(qū)間矩陣之間距離的新方法,尋求更趨近帕累托最優(yōu)的集結(jié)結(jié)果,使最終評(píng)價(jià)值更符合多階段評(píng)價(jià)的目標(biāo)。首先,根據(jù)多階段專家評(píng)價(jià)值將區(qū)間信息轉(zhuǎn)化為二維坐標(biāo)點(diǎn),并將其映射到二維坐標(biāo)系中。然后,構(gòu)建區(qū)間信息離差最小化集結(jié)模型,并基于植物模擬生長(zhǎng)算法(PGSA)進(jìn)行群體判斷信息的集結(jié),再通過(guò)合成各方案的屬性評(píng)價(jià)值,給出各決策方案的綜合評(píng)價(jià)值并進(jìn)行排序,進(jìn)而給出最優(yōu)決策方案。最后,以物流服務(wù)商的多階段績(jī)效評(píng)價(jià)為例,驗(yàn)證了該方法的合理性和有效性。
A new method is proposed to multistage interval information aggregation and decision making. This method considers minimizing the distance between the aggregation matrix and multistage interval matrices. The method aims to seek the aggregation result which is closer to Pareto optimum so as to make the final evaluation value more in line with the goal of multistage evaluation. Firstly, the interval information is converted into two dimensional coordinate points according to the multistage expert evaluation values, and then these points are mapped to planar reference frame. Next, the dispersion minimization aggregation model is constructed and solved by plant simulation growth algorithm. Later, the ranking of each decision plan is given by synthesizing the attribute evaluation values of each plan and then the optimal decision plan is given. Finally, the comprehensive evaluation values and the rationality and effectiveness of the method are verified by the example of the multistage performance evaluation of the logistics service providers.