基于供求融合的计量物资智能调度匹配路径优化研究

Optimization of path matching for intelligent scheduling of quantitative materials chain based on supply and demand fusion

为解决电力计量物资供应链配送供应环节中,车货匹配与路径规划不科学不合理等,需进一步优化调度路径、降低物流运输成本、提高运作效率。基于供应与需求融合的角度,提出了电力计量物资供应链智能调度算法。主要对路径优化方面使用遗传算法对混合粒子群算法进行优化,结合交叉运行和变异运算,对最佳适应度粒子求解,提高运算效率,降低局部最优解几率,获得最佳配送路径。应用启发式正交二叉树搜索算法用于计量物资车辆的合理配备,最终从最优配送调度路径和最优装车方案相结合,形成基于实际调度物资需求的电网供应物资组合智能调度算法。通过与经典的调度算法比对实验证明,提出的算法在行车路径、派车数量以及装载效率3方面均有大幅提升,具有一定的研究与推广应用价值。

In order to solve the problems of cyclic vehicle allocation,unreasonable vehicle cargo matching,and unreasonable path planning in the distribution and supply process of power metering materials supply chain,it is necessary to further optimize the scheduling path,reduce logistics transportation costs,and improve operational efficiency.An intelligent scheduling algorithm for the supply chain of electricity metering materials is proposed based on the integration of supply and demand.In terms of path optimization,genetic algorithm is mainly used to optimize the hybrid particle swarm optimization algorithm.The algorithm combines cross operation and mutation operation to solve the particle with the best fitness,improves the operation efficiency,reduces the probability of local optimal solution,and makes the best distribution path.Heuristic orthogonal binary tree search algorithm is applied to the reasonable distribution of electric power metering material vehicles.Finally,by combination of the optimal distribution scheduling path and the optimal loading scheme,an intelligent scheduling algorithm for the combination of power grid supply materials based on the actual demand for dispatching electric power metering materials is formed.Comparative experiments with classical scheduling algorithms demonstrate that the proposed algorithm exhibits improvements in travel paths,dispatching quantities,and loading efficiency,which has certain research and promotion application value.