Evolutionary optimization of service times in interactive voice response systems

A call center is a system used by companies to provide a number of services to customers, which may vary from providing simple information to gathering and dealing with complaints or more complex transactions. The design of this kind of system is an important task, since the trend is that companies and institutions choose call centers as the primary option for customer relationship management. This paper presents an evolutionary algorithm based on Dandelion encoding to obtain near-optimal service trees which represent the structure of the desired call center. We introduce several modifications to the original Dandelion encoding in order to adapt it to the specific problem of service tree design. Two search space size reduction procedures improve the performance of the algorithm. Systematic experiments have been tackled in order to show the performance of our approach: first, we tackle different synthetic instances, where we discuss and analyze several aspects of the proposed evolutionary algorithm, and second, we tackle a real application, the design of the call center of an Italian telecommunications company. In all the experiments carried out we compare our approach with a lower bound for the problem based on information theory, and also with the results of a Huffman algorithm we have used for reference

Optimal switch location in mobile communication networks using hybrid genetic algorithms

The optimal positioning of switches in a mobile communication network is an important task, which can save costs and improve the performance of the network. In this paper we propose a model for establishing which are the best nodes of the network for allocating the available switches, and several hybrid genetic algorithms to solve the problem. The proposed model is based on the so called capacitated pmedian problem, which have been previously tackled in the literature. This problem can be split in two subproblems: the selection of the best set of switches, and a terminal assignment problem to evaluate each selection of switches. The hybrid genetic algorithms for solving the problem are formed by a conventional genetic algorithm, with a restricted search, and several local search heuristics. In this work we also develop novel heuristics for solving the terminal assignment problem in a fast and accurate way. Finally, we show that our novel approaches, hybridized with the genetic algorithm, outperform existing algorithms in the literature for the p-median problem.This work has been partially supported by a Universidad de Alcal´a project number UAH-PI2005/019. X. Yao’s work has been partially supported by a National Natural Science Foundation of China grant, number 60428202