Truck–drone combination involves launch/retrieval of rotary-wing drones on trucks, which can address the issues of limited endurance and capacity of rotary-wing drones in delivery systems. Truck–drone combination technologies provide a compelling alternative to traditional emergency logistics systems that rely on on-ground transportation networks. Thus far, little research has been conducted on the truck–drone routing variant with stochastic demand, which is closely related to emergency logistics systems. Herein, we formally define the truck–drone routing problem with stochastic demand (TDRP-SD), which involves drones responding quickly to stochastic demands and restocking the supply. In particular, a new restocking policy, termed the truck–drone synchronized (TDS) restocking policy, is introduced to complement the traditional restocking operations that rely on ground vehicles. We analyze the characteristics of the introduced restocking policy and develop several propositions to address the computational burden caused by the dynamic programming computation of the expected cost. We propose a hybrid heuristic that combines the state-of-the-art Slack Induction by String Removals (SISRs) and greedy insertion utilizing blink rules. Several mechanisms, such as short-route deep search, lower-bound and upper-bound guiding, and simulated annealing, are adopted to ensure the algorithm performance. In computational experiments, the hybrid heuristic solves two types of benchmark instances and achieves new solutions. In addition, a collection of converted instances with up to 302 customers is effectively solved. The sensitivity analysis demonstrates the performance of the TDS restocking policy.

中文翻译：

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具有随机需求的卡车-无人机路线问题


卡车-无人机组合涉及在卡车上发射/回收旋翼无人机，这可以解决交付系统中旋翼无人机的续航能力和容量有限的问题。卡车-无人机组合技术为依赖地面运输网络的传统应急物流系统提供了一种引人注目的替代方案。到目前为止，对具有随机需求的卡车-无人机路线变体的研究很少，这与应急物流系统密切相关。在本文中，我们正式定义了随机需求的卡车-无人机路线问题 （TDRP-SD），它涉及无人机快速响应随机需求并补充供应。特别是，引入了一项新的补货政策，称为卡车-无人机同步 （TDS） 补货政策，以补充依赖地面车辆的传统补货操作。我们分析了引入的补货政策的特点，并提出了几个主张来解决预期成本的动态规划计算造成的计算负担。我们提出了一种混合启发式方法，它结合了最先进的字符串删除松弛归纳 （SISR） 和利用闪烁规则的贪婪插入。采用短路由深度搜索、下界和上界引导、模拟退火等多种机制来保证算法性能。在计算实验中，混合启发式求解两种类型的基准测试实例并获得新的解决方案。此外，还有效地解决了具有多达 302 个客户的已转换实例的集合。敏感性分析表明了 TDS 补货政策的绩效。