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With improved quality of life, types of waste have become increasingly complex, and waste classification has become a global issue. As a result, the world is facing a waste-classified collection problem. However, the existing research on waste collection has paid little attention to waste classification. In this paper, we consider the pretreatment and classification of waste transfer stations. In recent years, global warming caused by carbon emissions has become a serious problem. Therefore, this work proposes the first dual-objective multi-depot two-echelon green vehicle routing system with various pickups to optimize waste-classified collection based on a mixed-integer programming model. To ensure the efficiency of our developed model, we designed a multiobjective brainstorming optimization algorithm with a novel clustering strategy based on the rank-clustering method and differential mutation. Compared with two classical multiobjective optimization algorithms in various generated test instances and a real-world case, the experimental results showed that the proposed model can help sanitation departments improve the economic and environmental benefits of waste-classified collection, and the proposed algorithm is an excellent optimizer for solving associated problems. [ABSTRACT FROM AUTHOR]

Big data have been widely studied by numerous scholars and enterprises due to its great power in making highly reliable decisions for various complex systems. Remanufacturing systems have recently received much attention, because they play significant roles in end-of-life product recovery, environment protection and resource conservation. Disassembly is treated as a critical step in remanufacturing systems. In practice, it is difficult to know the accurate data of end-of-life products such as disassembly time because of their various usage processes, leading to the great difficulty of making effective and reliable decisions. Thus, it is necessary to model the disassembly process with stochastic programming method where the past collected data are fitted into stochastic distributions of parameters by applying big data technology. Additionally, designing and applying highly efficient intelligent optimization algorithms to handle a variety of complex problems in the disassembly process are urgently needed. To achieve the global optimization of disassembling multiple products simultaneously, this work studies a stochastic multi-product disassembly line balancing problem with maximal disassembly profit while meeting disassembly time requirements. Moreover, a chance-constrained programming model is correspondingly formulated, and then, an enhanced group teaching optimization algorithm incorporating a stochastic simulation method is developed by considering this model's features. Via performing simulation experiments on real-life cases and comparing it with five popularly known approaches, we verify the excellent performance of the designed method in solving the studied problem. [ABSTRACT FROM AUTHOR]