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    A literature review of drone-truck routing problems: challenges and future research directions
    (Edp Sciences S A, 2025) Cengiz, Enes; Yilmaz, Cemal; Kahraman, Hamdi Tolga
    In recent years, new routing problems with high complexity have emerged in parallel with the diversification of collaborative operational activities carried out by drones with ground vehicles. There are two main factors that affect the complexity level of these new generation routing problems. The first factor is the synchronization of vehicles with very different characteristics to optimally perform operational tasks. The second factor is that unlike traditional routing problems such as transportation and logistics, healthcare, military and emergency operations, new generation problems have more complex objective and constraint spaces. In traditional routing problems, customers are represented by fixed points in a two-dimensional search space. In contrast, in agricultural spraying, which is a new generation routing problem, the areas to be sprayed are represented by irregular areas. The objective and constraint functions in next generation routing problems are specified as area-based rather than point-based because of this difference. This increases the geometric complexity of the objective and constraint spaces in next generation routing problems. This paper analyzes 108 publications on traditional and next generation routing problems published between 2015 and 2024. A comprehensive review of routing problems based on drone-truck cooperation is provided according to their application areas, mathematical models, and solution methods. Unlike the review studies in the literature, new routing problems in the field of agriculture are also included in this research and the mathematical complexity of these problems is presented for the first time. Current trends for future research on drone-truck based problems are discussed.
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    Öğe
    A new evolutionary optimization algorithm with hybrid guidance mechanism for truck-multi drone delivery system
    (Pergamon-Elsevier Science Ltd, 2024) Yilmaz, Cemal; Cengiz, Enes; Kahraman, Hamdi Tolga
    Synchronization of the Traveling Salesman Problem with Drone (TSP-D) is one of the most complex NP-hard combinatorial routing problems in the literature. The speeds, capacities and optimization constraints of the truck-drone pair are different from each other. These differences lead to the search space of TSP-D having a high geometric complexity and a large number of local solution traps. Being able to avoid local solution traps in the search space of TSP-D and accurately converge to the global optimal solution is the main challenge for evolutionary search algorithms. The way to overcome this challenge is to dynamically adapt exploitation and exploration behaviors during the search process and maintain these two in a balanced manner depending on the geometric structure of TSP-D's search space. To overcome this challenge, research consisting of three steps was conducted in this article: (i) three different guide selection methods, namely greedy, random and FDB-score based, were used to provide exploitation, exploration and balanced search capabilities, (ii) by hybridizing these three methods at different rates, guide selection strategies with different search capabilities were developed, (iii) by associating these hybrid guide selection strategies with different stages of the search process, the guidance mechanism was given a dynamic behavioral ability. Thus, the Fitness-Distance Balance-based evolutionary search algorithm (FDB-EA) was designed to achieve a sustainable exploitation-exploration balance in the search space of TSP-D and stably avoid local solution traps. To test the performance of the FDB-EA, the number of delivery points was set to 30, 50, 60, 80, and 100 and compared with twenty-seven powerful and current competing algorithms. According to the non-parametric Wilcoxon pairwise comparison results, FDB-EA outperformed all competing algorithms in all five different TSP-D problems. According to the results obtained from the stability analysis, the success rates and calculation times of FDB-EA, EA and AGDE algorithms were 88.00% (6308.79 sec), 58.40% (7377.43 sec) and 13.460% (34664.19 sec) respectively.