A fuzzy stochastic model for telecommunications bandwidth brokers under probabilistic QoS measures

In this paper, we model and solve profit maximization problem of a telecommunications Bandwidth Broker (BB) under uncertain market and network infrastructure conditions. The BB may lease network capacity from a set of Backbone Providers (BPs) or from other BBs in order to gain profit by leasing already purchased capacity to end-users. BB’s problem becomes harder to deal with when bandwidth requests of end-users, profit and cost margins are not known in advance. The novelty of the proposed work is the development of a mechanism via combining fuzzy and stochastic programming methodologies for solving complex BP selection and bandwidth demand allocation problem in communication networks, based on the fact that information needed for making these decisions is not available prior to leasing capacity. In addition, suggested model aims to maximize BB’s decision maker’s satisfaction ratio rather than just profit. As a solution strategy, the resulting fuzzy stochastic programming model is transformed into deterministic crisp equivalent form and then solved to optimality. Finally, the numerical experiments show that on the average, proposed approach provides 14.30% more profit and 69.50% more satisfaction ratio compared to deterministic approaches in which randomness and vagueness in the market and infrastructure are ignored.