Genetic-algorithms-based algorithm portfolio for inventory routing problem with stochastic demand
This paper presents an algorithm portfolio methodology based on evolutionary algorithms to solve complex dynamic optimization problems. These problems are known to have computationally complex objective functions which make their solutions to be computationally hard to find, when problem instances of large dimensions are considered. This is due to the inability of the algorithms to provide optimal or near optimal solution within allocated time interval. Therefore, this paper employs a bundle of evolutionary algorithms (EAs) tied together with several processors, known as algorithm portfolio, to solve a complex optimization problem such as inventory routing problem (IRP) with stochastic demands. EAs considered for algorithm portfolios are genetic algorithm (GA) and its four variants like memetic algorithm (MA), genetic algorithm with chromosome differentiation (GACD), age genetic algorithm (AGA), and gender specific genetic algorithm (aka SGA). In order to illustrate the applicability of the proposed methodology, generic method for algorithm portfolios design, evaluation, and analysis is discussed in detail. Experimentation has been performed on varying dimensions of IRP instances to validate different properties of algorithm portfolio. A case study was conducted to illustrate that the set of EAs allocated to certain number of processors performed better than their individual counterparts.