Particle swarm optimization method applied in total life cycle materials allocation of electricity engineering projects in the green and low-carbon supply chain

Power grid enterprises face the problem of unbalanced supply and demand of power materials and unequal distribution; intelligent allocation of power materials can optimize the distribution and use of power resources through advanced technology and algorithms to ensure the efficient use of energy and stable supply. In this paper, the whole life cycle cost model of power supply chain is constructed and solved by improved particle swarm optimization (PSO). This algorithm combines the advantages of standard PSO and genetic algorithm (GA), avoids their disadvantages effectively, and realizes the effect of complementary advantages. This paper proposes using the sum of the costs associated with warehousing, transportation, and service penalties in the supply chain of electrical power projects as the objective function, thereby achieving the goal of optimal supply chain costs. In this study, the improved PSO algorithm is used to optimize the objective function, and the optimization results are compared with those of standard PSO algorithm and GA, revealing that its convergence is superior to the other two algorithms. Considering the influence of different factors on the cost, the superiority of the improved PSO algorithm is further verified.