综合成本最小的低碳车辆调度问题及算法

Low-carbon vehicle scheduling problem and algorithm with minimum-comprehensive-cost

为解决现有低碳车辆调度模型忽略企业经济效益和不能全面反映车辆调度中所有成本的问题,区别于碳排放量最少模型,在油耗成本—碳排放成本—固定使用成本模型的基础上,引入车辆折旧成本、司机工资支出成本和车辆轮胎消耗成本,建立了综合成本最小的车辆调度模型,并提出一种新的混合遗传算法用于模型求解。该算法采用Sweep算法和随机全排列算子获得初始种群,利用禁忌搜索算法设计精英保留算子,最后对传统交叉算子进行改进。通过对碳排放量最少模型、油耗成本—碳排放成本—车辆固定使用成本最小模型和综合成本最小模型进行比较验证了模型的合理性,进一步的标准算例仿真测试证明了所提算法的有效性。

To solve the problems that the economic benefits of enterprises was ignored by the present low-carbon ve- hicle scheduling model and the entire cost of vehicle scheduling could not be reflect, on the basis of fuel cost-carbon emission cost-fixed cost model, the Minimum-Comprehensive-Cost vehicle Scheduling Model （MCCVSM） distin- guished from minimum-carbon-emission model was established by introducing vehicle depreciation cost, drivers＇ wage cost and tire-consumption cost. To solve MCCVSM, a new hybrid genetic algorithm was proposed which got the initial population through Sweep algorithm and random permutation operator, and design the elitist operator with tabu search algorithm. The traditional crossover operator was improve with this algorithm. A comparing experiment among the minimum-carbon-emission model, fuel cost-carbon emission cost-vehicle depreciation cost model and mini- mum-comprehensive-cost model was made to test the rationality of the proposed model. Further, the effectiveness of the proposed hybrid genetic algorithm was proved by standard cases simulation test.