Network Optimization Of Household Appliances Based On Discrete Simulation

With the expansion of the scale of production, the amount of the discarded electric and electronic appliances have grown faster and faster, which results in aggravated resource waste and environmental pollution because of the some ingredients such as heavy metal and composite materials in electronic components and poisonous material in used batteries. The reverse logistics, which is one of effective returned logistics control modes, can help solve the problem of waste resources recycling. Furthermore, building a reasonable recycling network guarantees economic efficiency and environmental protection benefits of recycling process, which has became a hot issue in the research of scholars both domestically and internationally in recent years. The recycling work of waste household appliances in China, however, is still in its infancy. The problems of the unsound recycle system, the irregular methods of waste disposal, especially the recycling network construction’s lack of scientific planning, have led to difficulties such as low recovery efficiency and high construction cost of building recycling network, as a result, the energy waste and environmental pollution have become more serious than in the past. Therefore, how to construct a reasonable recycling network and multi-objective evaluation system is the key to promote the healthy development of the recycling industry.In order to gain accurate planning data of regional recycling network, the influence factors of predicting the ownership and scrap quantity of electrical appliances have been analyzed firstly, and the factors of which the correlation between all factors and the two variables mentioned before are more remarkable than others have been treated as the entry point of prediction. What is more, Gompertz model and ownership coefficient method have been used to establish the prediction models. The predicted results have been compared with relevant statistics and other research achievements to ensure the results accuracy, and the predicted results have provided basic data for subsequent recycling network optimization.In addition, the recycling network hierarchy has been determined according to the logical structure of recycling process. In the premise of full consideration of the randomness of recycling quantity and transportation as well as the fluctuation influence of different recovery seasons, the discrete simulation software called Arena has been used to build the simulated model for multi-products in multi-cycles. Additionally, evaluating indexes such as dismantling & recycling coverage, inventory, cost, energy consumption and greenhouse gas emissions have been put forward in order to optimizing the system performance in different aspects, including economy, energy and environment.Finally, taking Anhui Province as an example, different scenarios (including network layout schemes, dismantling plans, and transport plans) have been designed according to the predicted results in the third chapter and the different situations of economy and geography and traffic in Anhui Province (16 cities, five areas and 78 districts in total). The simulating outputs under different scenarios have been analyzed to gain the optimal plan which has a better performance on system and economy-energy-environment indicators.