Dynamic grey target decision making method with three-parameter grey numbers

Abstract

Purpose – The purpose of this paper is to present a new method about dynamic decision problems with three-parameter grey numbers from other angle of view which not only aggregates the attribute values of alternatives of all the periods, but also excavates changes of attribute values about alternatives between the adjacent periods. Design/methodology/approach – The authors adopt grey target method to calculate the distance between every alternative and the best, worst bull’s eye, the distance between change series and the best, worst change bull’s eye, then both distances can be aggregated to reflect information about two aspects. Findings – This dynamic decision-making method not only aggregates the existing state of alternatives all of the stages, but also excavates the change information from vertical and horizontal direction, the decision result conforming to decision maker’s psychological behavior is obtained though adjusting the priority parameter. Originality/value – The paper considers on change of alternative’s attribute values from one period to the next period, and the dynamic characteristic has been reflected adequately. The grey target decision-making method reflects the distance between alternative and bull’s eye, the comprehensive target distance between alternative and positive, worst bull’s eye about change series are separately provided. And the final target distance reflecting both existing state and change trend is constructed.

Energy Security Assessment Based on a New Dynamic Multi-Criteria Decision-Making Framework

“…Polomčić et al [31] considered the problem of groundwater management scenarios evaluation. Yan et al [32], trying to solve the problem of vendor selection, proposed to study the trend of changes in alternatives over time and take this trend into account when aggregating subsequent periods of time. Keshavarz-Ghorabaee et al [33], considering the subcontractor evaluation problem in a construction project, proposed extending the MCDM paradigm to capture changes in collections of alternatives and decision makers, and to aggregate ratings from different time periods.…”

“…Table 1 presents an overview of the literature related to the various areas of DMCDM application along with the dynamic extensions of the classic MCDM paradigm proposed by the researchers. Project risk management MAUT Calculation of dynamic risk exposure and dynamic discriminative index for different periods [25] Air traffic SAW Changeability of the set of alternatives over time, taking into account historical and present data [24] Automotive manufacturing SAW Changeability of the set of alternatives and the set of criteria over time, taking into account historical, present and projected future data [37] Construction industry Fuzzy EDAS Changeability of the set of alternatives and the set of decision makers over time, aggregation of different periods of time [33] Marketing management PROMETHEE GDSS Aggregation of different periods of time, different aggregation strategies [27] Enterprise Resources Planning system implementation GRA/Fuzzy GRA Changeability of criteria weights over time, aggregation of different periods of time, the use of real numbers for the oldest periods, interval numbers for intermediate periods and triangular fuzzy numbers for the most recent periods [38] Investment management TIFN-WAA Aggregation of different periods of time, different aggregation strategies [28] Investment management Fuzzy TOPSIS Variability of criterion weights over time, aggregation of different periods of time [29] Vendor selection TPIGN Study of the trend of changes in alternatives on the criteria in subsequent periods, aggregation of different periods of time, taking into account the trend of changes [32] Reverse logistics management DIF-MAGDM Variability of criteria weights over time, aggregation of different time periods, aggregation of assessments of many experts [34] Electric energy metering device selection DINFWAA/DINFWGA Aggregation of different periods of time, different aggregation strategies [30] Supplier selection BLTS DMCDM Variability of the set of alternatives and the set of criteria over time, taking into account historical and present data [35] Groundwater management Fuzzy TOPSIS Aggregation of different periods of time [31] Supplier selection IFS DGMCDM Variability of the set of alternatives and the set of criteria over time, taking into account historical and present data [36] DEA-Data Envelopment Analysis, MAUT-Multi-Attribute Utility Theory, SAW-…”

“…The last assumption is related to the suggestion in the work of Yan et al [32] to study the trend of changes in alternatives over time in DMCDM problems and to take this trend into account when making assessments. When it comes to assessing the energy security of countries, the analysis of the trend is also important because the energy policy of countries is usually stable and does not undergo major changes over at least a few years.…”

A Dynamic Fuzzy Approach Based on the EDAS Method for Multi-Criteria Subcontractor Evaluation

“…In Equation (29), the value of ρ t is equal to 1 for the first period (t = 1), and it is always greater than 0.5.…”

“…It should be noted that we use Equation (29) to set the weights for aggregating the dynamic scores. However, this function can be replaced with any custom function which can consider the importance of newer decision information.…”