Unsustainable production and consumption are driving a significant increase in global electronic waste, posing substantial environmental and human health risks. Even in more developed nations, there is the challenge o...
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Unsustainable production and consumption are driving a significant increase in global electronic waste, posing substantial environmental and human health risks. Even in more developed nations, there is the challenge of low collection rates. In response, we integrate offline and online trading systems and design a material efficiency strategy for used cell phones. We propose a new multi -objective optimization framework to maximize profit, carbon emissions reduction, and circularity in the process of recycling and treatment. Considering multiperiod, multi -product, multi -echelon features, as well as price sensitive demand, incentives, and qualities, we established a new multi -objectivemixed -integernonlinearprogramming optimization model. An enhanced, Fast, Non -Dominated Solution Sorting Genetic Algorithm (ASDNSGA-II) is developed for the solution. We used operational data from a leading Chinese Internet platform to validate the proposed optimization framework. The results demonstrate that the reverse logistics network designed achieves a win-win situation regarding profit and carbon emission reduction. This significantly boosts confidence and motivation for engaging in recycling efforts. Online recycling shows robust profitability and carbon reduction capabilities. An effective coordination mechanism for pricing in both online and offline channels should be established, retaining offline methods while gradually transitioning towards online methods. To increase the collection rate, it is essential to jointly implement a transitional strategy, including recycling incentives and subsidy policies. Additionally, elevating customer environmental awareness should be viewed as a long-term strategy, mitigating the cost of increasing collection rates during the market maturity stage (high collection rates).
This paper presents a prediction-based optimization strategy (POS) for the Energy Management System to balance the use of diesel generator (DG) and emergency battery (EB) in the microgrid. The POS is developed by comb...
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This paper presents a prediction-based optimization strategy (POS) for the Energy Management System to balance the use of diesel generator (DG) and emergency battery (EB) in the microgrid. The POS is developed by combing two operating strategies, the "predictive analysis" and "optimal operation" in each scheduling period for the microgrid. Based on the predicted microgrid state and energy demand, a multi-objective mixed-integer nonlinear programming model (MOMINP) is constructed to minimize the fuel consumption and the regularization of battery charge/discharge subject to the practical constraints in the microgrid. This paper proposes a detailed scheme to deal with the multiple objectives and nonlinear constraints in the MOMINP, then the MOMINP is successfully converted into a mixed-integer linear programming model (MILP). And an adjustment strategy is designed to obtain the nearoptimal solution of the MOMINP based on the optimal solution of the MILP solved by using the CPLEX Optimizer. Experimental results show that in a basic scheduling period, the working time of DG in the POS-softmax regression strategy is shorter than the current operation, and the fuel consumption reduction ratio is about 15.3% with the same battery SoC value at the end of the scheduling. At the same time, the fuel consumption in the POS-accurate prediction strategy can be reduced by up to 54.9% compared with the POS-softmax regression strategy and can be reduced by 61.8% compared to the current operation. Based on the comparative analysis of the actual case data of a micro-grid in 6 months, it can be seen that on average the POS works better than the current operation, with an approximately 23.6% decrease in the objective function and an additional 16.2% decrease with an accurate prediction.
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