目的 为解决无人机生鲜配送中因飞行速度引发的保鲜与节能之间的矛盾,通过量化“鲜度-能耗”之间的效益背反关系,构建一套协同优化配送路径与飞行速度的决策方法,以提升系统综合效益与运营弹性。方法 以最小化鲜度损失成本与无人机能耗成本为目标,将各弧段飞行速度作为连续决策变量引入双目标优化模型,提出两阶段求解方法：第1阶段采用非支配排序遗传算法Ⅱ（NSGA-II）优化配送路径;第2阶段针对每条路径设计基于距离感知的启发式速度优化算法,并在MATLAB环境中基于小规模（5客户）和中等规模（10客户）算例开展仿真。结果 研究算法在5客户和10客户算例中,分别获得了40个和73个非支配解的Pareto前沿,发现鲜度与能耗之间存在显著效益背反关系。通过第2阶段速度优化,在5客户和10客户算例中,使总成本较固定速度策略分别降低了19.1%和9.6%,且优化后速度呈现分段差异化特征。结论 研究模型有效刻画了无人机生鲜配送中“鲜度-能耗”的效益背反关系。研究表明,两阶段优化可显著降低总成本,NSGA-II算法能够生成分布良好的Pareto最优解集,从而为决策者提供多样化的运营策略。
Abstract
The work aims to quantify the conflicting "freshness-energy consumption" relationship and establish a collaborative optimization method for distribution routes and flight speeds to enhance the comprehensive benefits and operational flexibility of the system, so as to address the trade-off between freshness preservation and energy conservation induced by flight speed in UAV fresh food distribution. With the dual objectives of minimizing freshness loss costs and drone energy consumption, the flight speed was taken as a continuous decision variable in a dual-objective optimization model. A two-stage solution approach was developed. In Phase I, the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) was employed to optimize delivery route. In Phase Ⅱ, a distance-aware heuristic speed optimization algorithm was employed for each route. Simulations were conducted in MATLAB using small-scale (5 customers) and medium-scale (10 customers) cases. The Pareto frontier analysis revealed 40 and 73 non-dominated solutions in the 5- and 10-customer cases respectively, demonstrating a significant trade-off between freshness preservation and energy efficiency. Through Phase Ⅱ speed optimization, total costs were reduced by 19.1% and 9.6% compared with fixed-speed strategies, with optimized speeds exhibiting segmentally differentiated characteristics. This study validates the effectiveness of the proposed model in capturing the conflicting relationship. The two-stage optimization significantly lowers total cost, and the NSGA-Ⅱ algorithm can generate well-distributed Pareto optimal solutions, providing diversified operational strategies for decision-makers.
关键词
无人机物流 /
生鲜配送 /
多目标优化 /
路径-速度协同优化 /
非支配排序遗传算法Ⅱ（NSGA-Ⅱ）
Key words
UAV logistics /
fresh food delivery /
multi-objective optimization /
route-speed joint optimization /
Non-dominated Sorting Genetic Algorithm II (NSGA-Ⅱ)
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基金
陕西省软科学研究项目（2025KG-YBXM-108）
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