a fuzzy bi-objective optimization model to design a reverse supply chain network: a cuckoo optimization algorithm

The design and establishment of a logistics network is a strategic decision that lasts several years to work and the parameters of customer demand and return may be changed during this time. therefore, an efficient logistics network should be designed in a way that can respond to uncertainties. the applications of such a network can be found in different industries like the battery industry. this study aims to determine the number of products sent among the centers at each time so that the total cost of reverse logistics and delay time is minimized. to address the uncertainty in the reverse logistics network (rln), a fuzzy programming method is utilized. to tackle the complexity of the problem, the cuckoo optimization algorithm (coa) and genetic algorithm (ga) were developed. to compare these two optimization algorithms and find the superiority of them, a series of problem instances were generated. the obtained results demonstrated a satisfactory efficacy for both meta-heuristic algorithms. it was also revealed that the sum of values sent to the main manufacturer is equal to the values obtained from the exact solution method.