以空车总走行里程最小为目标,以空车供需平衡和车流量不超过线路通过能力为约束条件,建立空车调配数学模型,并设计分步优化迭代算法进行求解。该算法的基本思路是:先放弃模型中能力约束条件,将问题转化为标准运输问题求解;再检验解是否满足能力约束条件,若满足,则得到最优解;否则,记忆有效解,调整OD供需量、路段通过容量和路网路段,形成新的能力约束条件下的空车调配子模型,再求解。如此反复迭代,直到全部空车车流配置殆尽为止;累计各步迭代的结果,得到空车调配方案。在应用实例中,分别采用直接求解算法和分步优化迭代算法求解,分步优化迭代算法得到的空车调配方案比直接求解法可减少空车走行里程6 000 km,且路网配流相对均衡。结果验证了空车调配数学模型及其分步优化迭代算法的正确性及可行性。 The objective of this paper is to minimize the total mileage of empty trucks. Based on the balance between supply and demand of empty trucks and the capacity of traffic flow not exceeding the ability of passing routes, a mathematic model of empty truck deployment is established and a step-by-step optimized iterative algorithm is designed to solve the problem. The basic idea of ​​the algorithm is: first give up the ability constraints in the model, and then convert the problem into a standard transportation problem; if the solution satisfies the ability constraint condition, then obtain the optimal solution; otherwise, remember the effective solution and adjust OD Supply and demand, road sections through the capacity and road network segments, the formation of new capacity constraints under the empty truck sub-model, and then solve. So iterative, until all empty car traffic configuration exhausted so far; cumulative results of each iteration, get the empty car deployment program. In the application example, the direct solution algorithm and the step-by-step optimization iterative algorithm are respectively used to solve the empty truck deployment plan. The direct route solution method can reduce the empty truck mileage by 6,000 km and the road network distribution relatively balanced. The results validate the correctness and feasibility of the mathematic model of empty truck deployment and its iterative optimization algorithm.