A novel particle swarm optimization for multiple campaigns assignment problem

This paper presents a novel swarm intelligence approach to optimize simultaneously multiple campaigns assignment problem, which is a kind of searching problem aiming to find out a customer-campaign matrix to maximize the outcome of multiple campaigns under certain restrictions. It is treated as a very challenging problem in marketing. In personalized marketing it is very important to optimize the customer satisfaction and targeting efficiency. Particle swarm optimization (PSO) method can be chosen as a suitable tool to overcome the multiple recommendation problems that occur when several personalized campaigns conducting simultaneously. Compared with original PSO we have modified the particle representation and velocity by a multi-dimensional matrix, which represents the customer-campaign assignment. A new operator known as REPAIRED is introduced to restrict the particle within the domain of solution space. The proposed operator helps the particle to fly into the better solution areas more quickly and discover the near optimal solution. We measure the effectiveness of the propose method with two other methods know as Random and Independent using randomly created customer-campaign preference matrix. Further a generalized Gaussian response suppression function is introduced and it differs among customer classes. An extensive simulation studies are carried out varying on the small to large scale of the customer-campaign assignment matrix and the percentage of recommendations. Simulation result shows a clear edge between PSO and other two methods.