VNU-UET Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T08:05:35ZEPrintshttp://eprints.uet.vnu.edu.vn/images/sitelogo.pnghttps://eprints.uet.vnu.edu.vn/eprints/2019-11-29T03:37:53Z2019-11-29T03:37:53Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3671This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/36712019-11-29T03:37:53ZA hybrid genetic algorithm for the traveling salesman problem with droneThis 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.Quang Minh HaYves DevilleQuang Dung PhamMinh Hoang Haminhhoang.ha@vnu.edu.vn2017-12-11T09:38:43Z2019-02-18T04:14:15Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2748This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/27482017-12-11T09:38:43ZOn the min-cost traveling salesman problem with droneOver the past few years, unmanned aerial vehicles (UAV), also known as drones, have been adopted as part of a new logistic method in the commercial sector called "last-mile delivery". In this novel approach, they are deployed
alongside trucks to deliver goods to customers to improve the quality of service and reduce the transportation cost. This approach gives rise to a new variant of the traveling salesman problem (TSP), called TSP with drone (TSP-D). A variant of this problem that aims to minimize the time at which truck and drone finish the service (or, in other words, to maximize the quality of service) was studied in the work of Murray and Chu (2015). In contrast, this paper considers a new variant of TSP-D in which the objective is to minimize operational costs including total transportation cost and one created by
waste time a vehicle has to wait for the other. The problem is first formulated mathematically. Then, two algorithms are proposed for the solution. The first
algorithm (TSP-LS) was adapted from the approach proposed by Murray and Chu (2015), in which an optimal TSP solution is converted to a feasible TSP-D solution by local searches. The second algorithm, a Greedy Randomized Adaptive Search Procedure (GRASP), is based on a new split procedure that optimally splits any TSP tour into a TSP-D solution. After a TSP-D solution has been generated, it is then improved through local search operators. Numerical results obtained on various instances of both objective functions with different sizes and characteristics are presented. The results show that GRASP outperforms TSP-LS in terms of solution quality under an acceptable running time.Quang Minh HaYves DevilleQuang Dung PhamMinh Hoang Haminhhoang.ha@vnu.edu.vn