A particle swarm optimization based algorithm to the Internet subscription problem

It is a business practice that Internet service providers (ISPs) employ the differential service concept to satisfy various requirements of different Internet customers. Under this concept, the bandwidth resources are usually divided into several Internet commodity classes for sales. For each commodity class, there is a different sales price and a unique bandwidth restriction. Theoretically, subscribers can enjoy their services up to the bandwidth limits. However, in reality, many subscribers may encounter a situation in which their on-line requests are denied or their connection speeds are far below their contracted speed limits. One of the reasons is that ISPs do not set sales limits for their Internet commodities and accept too many subscribers as their Internet subscribers. To overcome this problem, this paper deals with an ISP’s sales limit problem over a finite time planning period. The purpose of this paper is to maximize the total expected revenues by determining the sales limits for each subscription type and bandwidth allocation for each commodity class over any subscription interval under service level guarantee. In this paper, a constrained nonlinear integer model is developed and an algorithm based on a particle swarm optimization (PSO) is proposed to acquire admissible solutions. Several examples are provided for showing the efficiency of the proposed method.