This paper addresses the traveling salesman problem with drone (TSP-D), in which a truck and drone are used to deliver parcels to customers. The objective of this problem is to either minimize the total operational cost (min-cost TSP-D) or minimize the completion time for the truck and drone (min-time TSP-D). This problem has gained a lot of attention in the last few years reflecting the recent trends in a new delivery method among logistics companies. To solve the TSP-D, we propose a hybrid genetic search with dynamic population management and adaptive diversity control based on a split algorithm, problem-tailored crossover and local search operators, a new restore method to advance the convergence and an adaptive penalization mechanism to dynamically balance the search between feasible/infeasible solutions. The computational results show that the proposed algorithm outperforms two existing methods in terms of solution quality and improves many best known solutions found in the literature. Moreover, various analyses on the impacts of crossover choice and heuristic components have been conducted to investigate their sensitivity to the performance of our method.

中文翻译：

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无人机旅行商问题的混合遗传算法

本文解决了无人驾驶飞机（TSP-D）的旅行业务员问题，在该问题中，使用卡车和无人驾驶飞机向客户交付包裹。该问题的目的是使总运营成本（最小成本TSP-D）最小化或使卡车和无人机的完成时间最小化（最小时间TSP-D）。在过去的几年中，这个问题已经引起了很多关注，这反映了物流公司中一种新的交付方式的最新趋势。为了解决TSP-D问题，我们提出了一种基于分裂算法，针对问题的分频器和局部搜索算子，具有动态种群管理和自适应分集控制的混合遗传搜索，一种促进收敛的新恢复方法以及一种自适应惩罚机制。动态平衡可行/不可行解决方案之间的搜索。计算结果表明，所提出的算法在解决方案质量方面优于两种现有方法，并改进了文献中发现的许多最著名的解决方案。此外，对交叉选择和启发式组件的影响进行了各种分析，以研究它们对我们方法性能的敏感性。