Time-series Based Multi-criteria LSGDM With Intuitionistic Fuzzy Information And Its Application In Supplier Selection

Battery is the core component of new energy vehicles,battery provides the core driving force for new energy vehicles,the efficiency of battery is related to the driving range and total power of new energy vehicles.The choice of proper battery supplier is crucial for new energy vehicle companies since it directly affects the market share and core competitiveness.Since the efficiency of battery changes over time,some criteria of battery suppliers cannot be evaluated in the short period and need to be continuously evaluated at different periods(or time).Furthermore,Decision makers(DMs)may present some hesitancy when giving their evaluations.Thus,this thesis formulates the battery supplier selection as a type of time-series based multi-criteria large-scale group decision making(LSGDM)with intuitionistic fuzzy information.Many scholars have made many outstanding achievements in the research of LSGDM.However,there are still some defects that need to be further consideration.Firstly,in the existing research on LSGDM,DMs are usually required to provide evaluation information only in a single period.However,the evaluation information at different periods has different effects on the final evaluation results.To enhance the understanding of the alternatives,it is necessary to introduce a time-series perspective to the practical LSGDM.Secondly,the determination of DMs’ weights is a critical part of LSGDM.However,most existing studies do not distinguish the weights of DMs with respect to different criteria and only use objective or subjective models alone.These models might lead to two problems: one is that giving equal weight to DMs with respect to different criteria is unreasonable since DMs have different understandings for different criteria.The other is that the DMs’ weights obtained from only considering a single aspect are not accurate enough.Thirdly,the criteria weights also play an important role for LSGDM.However,some studies usually utilize a single approach to determining the criteria weights.The single approach may only take into account a one-side situation,which is very difficult to derive the criteria weights reasonably and comprehensively.Fourthly,most existing methods for consensus reaching process(CRP)are used to determine whether DMs reach a consensus based on the evaluation information given by DMs in a single period.However,the existing methods for CRPs are not suitable for the LSGDM with multiple periods.It is very urgent to develop a new CRP for LSGDM with multiple periods.To remedy the above deficiencies,this thesis proposed a time-series based multi-criteria LSGDM with intuitionistic fuzzy information,the main works include the following aspects.(1)Determine the time weight of each period.Considering the importance of different periods,the comprehensive time weights are determined by combining the exponential decay model and the maximal entropy ordered weighted averaging(OWA)operator weights determining model.(2)The determination of the DMs’ weights with respect to each criterion.DMs are divided into different clusters by using the proposed intuitionistic fuzzy Hamming distance-based fuzzy c-means(FCM)algorithm on each criterion.Combining the confidence levels of DMs and the corresponding clustering results,the DMs’ weights with respect to each criterion are determined.(3)The determination of the comprehensive criteria weights.The comprehensive criteria weights are obtained by integrating the intuitionistic fuzzy entropy and the constructed multi-objective programming model.(4)The develop of a novel CRP for LSGDM with multiple periods.The individual consensus level and group consensus degree at each period are defined,respectively.Then,a new identification approach is proposed to identify DMs to be adjusted.Subsequently,a total adjustment minimization model is built to adjust these DMs’ evaluation information.The optimal alternative is generated according to the collective global overall evaluations.