## 1 Introduction
### Related Work
Computational experiments demonstrate that the hybrid metaheuristic algorithm can generate optimal routes for various TSP-D scenarios, outperforming rival heuristics in terms of solution quality. Lately, the focus has shifted towards the multimodal last-mile e-mobility concept, which aims to make urban deliveries cleaner, greener, and more accessible. This approach is suitable for various logistics operations and medical applications as part of sustainable transportation systems. A study [41] investigated an application of multimodal e-mobility by introducing and modeling the traveling salesman problem with drone and bicycle (TSP-D-B). A new mixed-integer linear programming model is developed to minimize total travel time. Within the context of last-mile delivery, the model employs a fleet comprising a truck, a drone, and a bicycle to efficiently serve customers in a single visit. The truck serves as the primary vehicle, with the drone and bicycle available as alternatives in case of emergencies like traffic congestion or route issues. To evaluate the model's complexity, validity, and practicality, a dataset with 64 different benchmarks is created. The results show that the model can effectively solve the benchmarks for up to 50 customers within a maximum of 685 s. A comparison is conducted between TSP-D-B, the traditional TSP, and TSP-D. The results demonstrate that TSP-D-B offers significant time savings across all benchmarks. Additionally, a comparative analysis utilizing instances from the existing literature confirms that TSP-D-B achieves notable time savings in the majority of scenarios. Developing path planning strategies for the set of multi unmanned vehicles is a significant and growing research topic. Recent research [42] focuses on the min-max multiple traveling salesman problem with single depot (min-max mTSP) and multiple depots (min-max multi-depot mTSP), which aim to minimize the longest path among a set of paths. These two problems have various practical applications and are usually analyzed separately in the existing literature. They introduce a unified memetic approach to address both the min-max mTSP and min-max multi-depot mTSP cases. The proposed algorithm includes a generalized edge assembly crossover for generating offspring solutions, an efficient variable neighborhood descent for local optimization, and an aggressive post-optimization step for further enhancing solutions. Through extensive experiments on 77 min-max mTSP benchmark instances and 43 min-max multi-depot mTSP instances commonly found in the literature, they have demonstrated the superior performance of the proposed algorithm compared to other state-of-the-art methods. Furthermore, additional experimental investigations have been carried out to provide insights into the effectiveness of the key algorithmic components. The Multi-Depot Vehicle Routing Problem (MDVRP) seeks to minimize the total distance traveled by unmanned vehicles originating from multiple depots to visit specified locations exactly once. This problem is classified as a Nondeterministic Polynomial Hard (NP-hard) problem and is frequently used as a benchmark for the development of optimization schemes. A recent paper [43] examines a variation of the MDVRP known as the min-max MDVRP, which aims to reduce the path length of the unmanned vehicle that covers the longest distance. Unlike the traditional MDVRP, the min-max MDVRP is particularly relevant for time-sensitive applications, such as emergency response, where reducing customer response time is crucial. The paper introduces an extension of an existing ant colony optimization algorithm (ACO) for solving the Single Depot Vehicle Routing Problem (SDVRP) to address both the multiple depots and min-max variants of the problem. Initially, the algorithm for solving the min-max version of SDVRP is presented, followed by an extension of the algorithm for min-max MDVRP using an equitable region partitioning strategy to assign customer locations to depots, thereby reducing MDVRP to multiple SDVRPs.