煤炭是我国重要的战略能源，煤炭消费量占一次能源消费总量的56.8%（2020年）。煤炭供应链网络包括从坑口至最终用户的一系列作业，例如：开采、洗配、运输、销售等。煤炭供应链的网络优化可以实现煤炭产销优化配置，解决煤炭生产销售不匹配问题，使煤炭消费客户与煤炭企业实现互利共赢。传统煤炭供应链网络优化通常以经济效益为主，但在全球变暖以及环境问题日益显著的背景下，煤炭供应链网络优化亟需兼顾经济效益和环境效益，对考虑碳排放的煤炭供应链网络优化进行深入研究尤为重要。基于此，本文的主要研究工作如下：
首先，对煤炭供应链网络、考虑碳排放的供应链网络以及求解该问题的智能优化算法——量子进化算法（QEA）的研究现状进行综述；分析煤炭供应链网络、碳排放核算理论、多目标优化理论及元启发式算法的相关概念及理论。其次，建立了基于成本最小化和碳排放量最小化的多目标混合整数规划模型，该模型对节点数量、煤炭流量平衡、产能以及供求关系、转运情况等进行约束限制，主要通过铁路、公路、水路三种运煤方式使用不同燃料所产生的直接碳排放量，来核算煤炭供应链网络的碳排放情况。最后，提出了改进量子进化算法（IQEA）求解此类问题，根据问题特性，创新设计了基于量子比特与序列相结合的改进编解码机制，引入三种旋转角度更新策略来改进量子旋转门更新机制，并设计了强化学习机制来实现量子旋转门更新步骤的自学习；使用IQEA算法和Pareto最优思想对本文所选案例的煤炭供应链网络布局方案进行优化，通过10组不同规模的算例仿真实验，对传统QEA和IQEA算法的求解质量进行对比分析，实验表明所提IQEA算法在求解此类问题时具备高效性，并且在探索和应用两个方面达到了有效平衡，算法的收敛效果和全局搜索能力均得到提升。
本文所建立的考虑碳排放的混合整数规划模型，为研究煤炭供应链网络优化问题提供了一个新的视角，所设计的IQEA算法为求解煤炭供应链网络优化等组合优化问题提供了一种新型智能优化求解方法。本研究在煤炭供应链网络布局优化、运输路线优化、运输方式优化及降低供应链碳排放等方面具备重要价值。

Coal is an important strategic energy in China, and its consumption accounts for 56.8% of the total primary energy consumption (in 2020). Coal supply chain network includes a series of operations from pithead to end user, such as mining, washing, transportation, sales, etc.The network optimization of coal supply chain can realize the optimal allocation of coal production and sales, solve the mismatch between coal production and sales, and make customers and coal enterprises achieve mutual benefit and win-win.Traditional coal supply chain network optimization usually focuses on economic benefits. However, under the background of global warming and increasingly prominent environmental problems, coal supply chain network optimization needs to give consideration to both economic benefits and environmental benefits. It is particularly important to conduct in-depth research on coal supply chain network optimization considering carbon emissions.Based on the above analysis, the main research work of this paper is as follows：
Firstly, the research status of coal supply chain network, supply chain network considering carbon emissions and intelligent optimization algorithm -- Quantum evolution algorithm (QEA) for solving this problem are summarized.And this study analyzes the related concepts and theories of coal supply chain network, carbon emission measurement model, multi-objective optimization theory and algorithm.
Secondly, a multi-objective mixed integer programming model based on cost and carbon emission minimization is established, which imposes constraints on node number, coal flow balance, capacity, supply and demand relationship, and transshipment.The carbon emissions of coal supply chain network are calculated mainly through the carbon emission measurement of transportation link, and the direct carbon emissions generated by the fuel consumption of different fuels used in railway, highway and waterway transportation mode in transportation link are calculated.
Finally, an improved quantum evolutionary algorithm (IQEA) is proposed to solve this kind of problems. According to the characteristics of the problem, an improved coding and decoding mechanism based on the combination of quantum bit and sequence decoding is innovatively designed. Three rotation angle updating strategies are introduced to improve the updating mechanism, and a reinforcement learning mechanism is designed to realize the self-learning of selecting one suitable rotation angle updating strategy during each generation.In this study, IQEA algorithm and Pareto optimal concept are used to optimize the coal supply chain network. Through 10 groups of simulation experiments with different scales, the traditional QEA and IQEA are compared and analyzed, and it is verified that the proposed IQEA algorithm is efficient in solving such problems, and it achieves an effective balance between exploration and application. The convergence effect and global search ability of the algorithm are improved.
The mixed integer programming model considering carbon emissions established in this paper provides a new perspective for studying the optimization of coal supply chain network. The improved quantum evolutionary algorithm designed in this paper provides a new intelligent optimization method for solving combinatorial optimization problems such as coal supply chain network optimization. This method has important value for optimizing the network layout, transportation route and transportation mode of coal supply chain, and then reducing the carbon emission of supply chain.

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