Chaotic genetic algorithm for performance optimization of green agricultural products supply chain network

The green agricultural products supply chain network (GAP-SCN) design is to provide an optimal platform for efficient and effective supply chain management. This paper proposes a new solution based on chaotic genetic algorithm (CGA) to find optimal solution for the GAP-SCN problem. Different from other methods in the literature, CGA adopts transforming operator to modify chromosomes in the population, uses the blending operator of roulette wheel selection and elitist reserve strategy and uses the chaotic operator to enhance diversity of chromosomes in order to avoid populations trapping in local optima. The novelty of the transforming operator is that it can avoid applying the penalty function so that the diversity of populations is decreased. To show the efficacy of the algorithm, CGA is also tested on three cases. Results show that the proposed algorithm is promising and outperforms the classic GA by both optimization speed and solution quality.