Using a hybrid genetic- simulated annealing algorithm for designing a recyclable waste collection system

The conflict between the environmental and economic factors in waste recycling management has attracted researchers’ attention. Inadequate management of recycling process leads to severe disorders of environmental and financial resources. The aim of this paper is to address the importance of effective planning of recyclable waste collection system in terms of determining the cost-optimal routes of a heterogeneous fleet of vehicles. This paper accounts for different types of the recyclable waste collection system and the necessity of processing each kind of waste in its compatible recycling plant. Additionally, a new mathematical model is presented for collecting recyclable wastes and a two-stage solution approach is applied to solve the problem. Three metaheuristic algorithms, including genetic algorithm, simulated annealing, as well as a hybrid of these algorithms are utilized in the first stage to collect all types of waste and separate recyclable ones in the most efficient routes with minimum traveling cost. The results of experiments show that the quality of the solution obtained by SA is superior to others by increasing the problem dimension. However, the hybrid method is better in terms of required CPU time. In the second stage, an exact method is used to solve the transportation problem of waste residues to disposal facilities. Moreover, the numerical experiments demonstrate that the use of the nearest method of solution representation in all three metaheuristic algorithms can reduce the cost function from 4 to 7%.