Research On Coal Supply Chain Network Optimization Considering Carbon Emissions

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,and 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.