An evolutionary approach for the optimization of the beekeeping value chain

Abstract

Evolutionary algorithms can efficiently be applied in broad practical issues by tailoring their operators to the specific combinatorial optimization problem under study. Based on Genetic Algorithms, this paper proposes a master–slave strategy enhanced with an ad hoc chromosome redefinition for the beekeeping value chain problem. Boolean and real genes are combined in a single chromosome to include a wide variety of decision-making optimization variables. Since production usually involves temporal dependency among processes, crossover and mutation operators were properly adjusted. Moreover, novel crossover and mutation operators were designed to make sure that all ensuing individuals were feasible. Since the evaluation of chromosomes may become time-consuming, parallel programming was adopted so that the Workers can simultaneously explore different instances. In particular, the model was implemented in order to optimize the beekeeping value chain aiming at the maximization of the Net Present Value. The results show that the improved algorithm is useful to make economic-financial decisions concerning the beekeeping activity in the southwest of Buenos Aires (Argentina). The proposed approach manages to boost the space search, always yielding realistic scenarios.