MAGDM linear-programming models with distinct uncertain preference structures.
Group decision making with preference information on alternatives is an interesting and important research topic which has been receiving more and more attention in recent years. The purpose of this paper is to investigate multiple-attribute group decision-making (MAGDM) problems with distinct uncertain preference structures. We develop some linear-programming models for dealing with the MAGDM problems, where the information about attribute weights is incomplete, and the decision makers have their preferences on alternatives. The provided preference information can be represented in the following three distinct uncertain preference structures: 1) interval utility values; 2) interval fuzzy preference relations; and 3) interval multiplicative preference relations. We first establish some linear-programming models based on decision matrix and each of the distinct uncertain preference structures and, then, develop some linear-programming models to integrate all three structures of subjective uncertain preference information provided by the decision makers and the objective information depicted in the decision matrix. Furthermore, we propose a simple and straightforward approach in ranking and selecting the given alternatives. It is worth pointing out that the developed models can also be used to deal with the situations where the three distinct uncertain preference structures are reduced to the traditional ones, i.e., utility values, fuzzy preference relations, and multiplicative preference relations. Finally, we use a practical example to illustrate in detail the calculation process of the developed approach.
Xu
Zeshui S
ZS
Antai School of Economic and Management, Shanghai Jiaotong University, Shanghai 200052, China. xu_zeshui@263.net
Chen
Jian
J
D000465:Algorithms; D003198:Computer Simulation; D003661:Decision Support Techniques; D017143:Fuzzy Logic; D016014:Linear Models; D015233:Models, Statistical; D008962:Models, Theoretical; D011382:Programming, Linear