No abstract

With complexity and uncertainty having an increasing impact on the decision-making environment, much attention is being paid to the development and application of multiple criteria group decisionmaking (MCGDM) models owing to the potential for fully exploiting the diverse strengths and expertise of various members. In general, inevitable interactions among decision makers (DMs), when a number of DMs share similar knowledge and experiences, can have a significant impact on the management of decision information directly or indirectly related to DMs, and can easily lead to distorted and unconvincing decision outcomes. In order to model the MCGDM problem in which DMs share a similar background, a consolidated MCGDM model in the context of intuitionistic fuzzy sets (IFSs) is developed. First, we refine the constructive principles for intuitionistic fuzzy entropy (IFE) and use them as a basis to produce a novel IFE measure simultaneously factoring in the intuitionism and fuzziness of IFSs. With the aim of dealing with the impact on the specifications of the weights of DMs and criteria, an integrated method is then proposed based on the novel IFE measure, 2-additive fuzzy measure, and Choquet integral. Due to their capability of modeling effectively the interrelationships among arguments, the weighted intuitionistic fuzzy Bonferroni mean (WIFBM) and the weighted intuitionistic fuzzy geometric Bonferroni mean (WIFGBM) are introduced to fuse the individual evaluation values of alternatives on criteria. In addition, simple additive weighting based on the WIFBM or WIFGBM is applied to rank alternatives and select the best one. Finally, the feasibility and effectiveness of the proposed model are explored with a case study of an emergency plan decision-making problem accompanied with sensitivity and comparison analysis.INDEX TERMS Intuitionistic fuzzy sets, Bonferroni mean, intuitionistic fuzzy entropy, 2-additive fuzzy measure, choquet integral, multiple criteria group decision making.

No abstract

No abstract

Rapid growth and development of civil engineering in recent years inspire building enterprises to concentrate on construction contractor selection for achieving more construction quality and lower construction cost. The existing studies generally regard the process of selecting the best contractor as a multi-criteria group decision making problem. Few research studies addressed the contractor selection problem in the context of large-scale group decision making, which is common in practical scenarios in terms of major construction projects as a number of experts with diverse backgrounds are usually involved. On this basis, we establish a contractor selection framework under large-scale group decision making environment, which covers expert classification, consensus reaching process, collective decision matrix generation, and the ranking-oriented decision making method. We cluster expert group with K-means clustering method based on expertise levels, which are depicted by six features generated with an expertise identification approach. The consensus model manages consensus reaching process from both intra-and interlayers and takes into account the interactions between them. After reaching agreements among experts, this paper utilizes the concept of proportional hesitant fuzzy linguistic term set to assemble intra-subgroup assessments for the reduction of information loss or distortion. Then, an aggregation process carries on as to gather subgroup assessments in which the subgroup weights are derived from their cluster centers and sizes in the use of the TOPSIS method. Finally, the well-established decision making tool integrating qualitative and quantitative criteria, ELECTRE III, is adapted to elicit the ranking of bidders. An illustrative study and a comparative analysis are performed to demonstrate the feasibility and effectiveness of the established multi-criteria group decision making approach.