Multi-echelon reverse supply chain network design using new ant colony optimisation algorithms

Reverse logistics (RL) is becoming more important in the general area of the industry due to environmental and business factors. Planning and implementing a suitable RL network can lead to more benefits, customer satisfaction, and a nice social image for businesses. Since such network design challenges belong to the NP-hard problem class, three proposed ant colony algorithms that differ in the heuristic information, and artificial pheromone trail calculation rules were developed to solve a designed distribution-allocation problem in multi-stage RL network with a fixed transportation cost in distribution network as well as variable cost of the route. Five network characteristics with different sizes are designed, and thirty instances are randomly generated for each network characteristic to evaluate the performance of the three developed ant colony optimisation (ACO) algorithms. Computational analysis of the results reveals the high quality and validity of the developed ACO algorithms when compared with the exact results.