Optimum Analysis On The Supply Chain Carbon Footprint Of Multi-granularity Complex Product Based On Uncertainties

With the rapid development of the global economy, excessive carbon emissions have a tremendous negative effect on the natural system and socio-economics. So the international community is constantly trying to strengthen environmental regulation and other ways to reduce carbon emissions. Theunprecedented pressure on energy conservationobjective requires the enterprises must pay great attention to optimize the supply chain carbon footprint of product.Under the background of research, this thesis collected the domestic and foreign research papers about the carbon footprint, and changed the co-word cluster analyzer and the clustering algorithm to mine the preprocessing data and refine the researchtopics. Based on the review of the literatures according to research topics, the research ideas and content were established.On the basis of the meso and micro perspective, this thesis constructed the analysis model of the supply chain carbon footprint of multi-granularity complex product based on uncertainties, which included three dimensions that were the main responsibility for the supply chain, life cycle and multi-granularity level. Then the fishbone diagram method and structure equation method were used to describe the carbon footprint of life cycle. And this thesis drew multi-target disassembly hybrid graphs of carbon footprint structure element, and designed a driven recursive clustering search algorithm and a progressive modularity to divide the multi-granularity level of complex product. To dynamicanalyzethe uncertainties of carbon footprint optimization process, this thesis used the fuzzy method to delete the uncertainties of life cycle, and created the HQMC algorithm and improved the Spearman coefficient method to assess and control of data quality.According to the analysis model of the supply chain carbon footprint of multi-granularity complex product based on uncertainties, the thesis took the liquid crystal display as example and expanded the sample set to analyze the supply chain carbon footprint. After drawing the influence factors heat diagram of the supply chain carbon footprint of complex product set, the key influence parts were found.Then the thesis began to research the low carbon design of the supply chain carbon footprint of multi-granularity complex product based on uncertainties with example. Firstly, it created the QFDC method, and used the triangular fuzzy number method and improved distance closeness method to transform the low carbon needs of the main responsibility for the supply chain. And second, it calculated the carbon footprint design performance to determine the multi-granularity level and life cycle of design goals. At last, it used the TRIZ method to describe the mapping relationship of low-carbon design measures, eliminate the technical conflict and put forward suggestions of low-carbon design.Supplier evaluation and selection have great influence on the optimization of the supply chain carbon footprint of complex product. Based on reviewing the related research papers, the thesis mined the documents with the grounded theory method which improved by the rough set and principal components methods. After discriminating the initial and core categories by factor analysis, it selected the evaluation and selection indexes, and combined of the three methods, which were hybrid multidimensional hierarchical analysis method based on fuzzy theory,entropy evaluation method and data envelopment analysis method,to determine the Indexes weights. And it was hoped to achieve favorable expansibility and objectivity in supplier evaluation and selectionunder low carbon supply chain environment.According to the distribution decision method and stochastic chance-constrained programming theory, the analysis model of the supply chain carbon footprint of multi-granularity complex product based on uncertainties was expanded to multi-parameter, multi-product and multi-cycle. Based on the characteristics of the model, such as multi-constrained, nonlinear and mixed programming, the thesis designedan improved simulated annealing genetic hybrid algorithm and selected benchmark functions to compare algorithm performance. After designinga model example, the algorithm was used to achieve the overall optimization of the supply chain carbon footprint of complex product.At last, based on summarizing the work, the limitations and the future directions of the research were analyzed. This thesis expects to establish a new perspective and analysis framework of theoretically carbon footprint study, provide a scientific basis to the optimum analysis on the supply chain carbon footprint of enterprise productsfrom the applicationpoint, and help to reduce carbon emissions of the society.