Close loop supply chain network problem with uncertainty in demand and returned products: Genetic artificial bee colony algorithm approach

Abstract In recent years, due to environmental concerns, remanufacturing of products is practiced in different companies and closed loop supply chain network in these companies is significant to optimize. Therefore, current study is aimed to determine an optimal closed loop supply chain network, which is composed of multiple producers, remanufacturers, intermediate centers and customer centers. Furthermore, uncertainty in the demand and uncertainty in the quantity of returned products is considered simultaneously in the network to make it significantly useful in the uncertain environment. A novel genetic artificial bee colony (GABC) algorithm is introduced with a new food source representation for the current problem. The proposed GABC algorithm considered neighbor food sources for local search and used crossover and mutation operations of genetic algorithm to enhance the exploration ability of the proposed algorithm. Taguchi method is employed to compute the optimal parameters of GABC for two different size test problems taken from literature and a Case problem which are modified according to the current research problem. The performance of presented GABC algorithm is tested by comparing the results of considered test problems with the results obtained from the original artificial bee colony (ABC) algorithm and genetic algorithm (GA). Moreover, to test the robustness of the proposed GABC algorithm, different scenarios of small and large size problems based on the quantity of demand and variations in demand are made to perform the experiments. Results indicate that proposed GABC outperforms standard ABC and GA in different scenarios to give smaller value of the total cost of network and gives more robust results to give smaller variations in the total cost of network due to uncertain variations in the demand, as compared to original ABC and GA.d