Consensus modeling with probability and cost constraints under uncertainty opinions

Date

2017-09-01

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

Elsevier

Type

Article

Peer reviewed

Yes

Abstract

Goal programming is often applied into uncertain group decision making to achieve the optimal solution. Exiting models focus on either the minimum cost (guaranteeing negotiation budget) or the maximum utility (improving satisfaction level). This paper constructs a stochastic optimization cost consensus group decision making model adopting the minimum budget and the maximum utility as objective function simultaneously to study the negotiation consensus with decision makers' opinions expressed in the forms of multiple uncertain preferences such as utility function and normal distribution. Thus, the proposed model is a generalization of the existing cost consensus model and utility consensus model, respectively. Furthermore in this model, utility priority coefficients cause acceptable budget range and chance constraint shows the probability of reaching consensus. Differing from previous optimization models, the proposed model designs a Monte Carlo simulation combined with Genetic Algorithm to reach an optimal solution, which makes it more applicable to real-world decision making.

Description

Keywords

Group decision making, Cost consensus, Uncertain chance constraint;, Normal distribution, Utility function, Goal programming priority

Citation

Tan, X., Gong, Z., Chiclana, F. and Zhang, N. (2017) Consensus modeling with probability and cost constraints under uncertainty opinions. Applied Soft Computing, in press

Rights

Research Institute

Institute of Artificial Intelligence (IAI)