There are emerging transportation problems known as the Traveling Salesman Problem with Drone (TSPD) and the Flying Sidekick Traveling Salesman Problem (FSTSP) that involve using a drone in conjunction with a truck for package delivery. This study presents a hybrid genetic algorithm for solving TSPD and FSTSP by incorporating local search and dynamic programming. Similar algorithms exist in the literature. Our algorithm, however, considers more sophisticated chromosomes and less computationally complex dynamic programming to enable broader exploration by the genetic algorithm and efficient exploitation through dynamic programming and local search. The key contribution of this paper is the discovery of how decision-making processes for solving TSPD and FSTSP should be divided among the layers of genetic algorithm, dynamic programming, and local search. In particular, our genetic algorithm generates the truck and the drone sequences separately and encodes them in a type-aware chromosome, wherein each customer is assigned to either the truck or the drone. We apply local search to each chromosome, which is decoded by dynamic programming for fitness evaluation. Our new algorithm is shown to outperform existing algorithms on most benchmark instances in both quality and time. Our algorithms found the new best solutions for 538 TSPD instances out of 920 and 74 FSTSP instances out of 132.

中文翻译：

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具有类型感知染色体的混合遗传算法解决无人机旅行商问题


新兴的运输问题被称为无人机旅行推销员问题 (TSPD) 和飞行伙伴旅行推销员问题 (FSTSP)，涉及使用无人机与卡车结合进行包裹递送。本研究提出了一种混合遗传算法，通过结合局部搜索和动态规划来求解 TSPD 和 FSTSP。文献中存在类似的算法。然而，我们的算法考虑了更复杂的染色体和计算复杂度较低的动态编程，以便通过遗传算法进行更广泛的探索，并通过动态编程和局部搜索进行有效的利用。本文的主要贡献是发现了如何在遗传算法、动态规划和局部搜索层之间划分求解 TSPD 和 FSTSP 的决策过程。特别是，我们的遗传算法分别生成卡车和无人机序列，并将它们编码在类型感知染色体中，其中每个客户都被分配给卡车或无人机。我们对每条染色体应用局部搜索，通过动态规划对其进行解码以进行适应度评估。我们的新算法在大多数基准实例上在质量和时间上都优于现有算法。我们的算法为 920 个实例中的 538 个 TSPD 实例找到了新的最佳解决方案，并为 132 个实例中的 74 个 FSTSP 实例找到了新的最佳解决方案。