![]() However, the routing energy efficiency aspect of these systems remains unexplored. These vehicles are using battery as energy source. Recently, a new generation of Automated Guided Vehicles (AGVs) has been developed to assist human order pickers in order to minimize their travel time. Order picker routing refers to the process of collecting a set of products with the minimum travel time. This proposed study, which is powered by different algorithms with visual artifacts, might be accepted as a unique blueprint in its field. A user-friendly and dynamic interface, displaying visually the shortest route in distance or duration on Google Maps, has been developed by adding different features such as travelling mode options, remaining route distance and time. In this case, the proposed system updates its current route for the rest of the nodes by using the enhanced system to keep the total travel-cost minimum. Sometimes the TSP route list changes according to some sudden reasons or when the traffic intensity changes while travelling the nodes. #GOOGLE MAPS KILOMETRE HESAPLAMA UPDATE#Additionally, a dynamic route update mechanism with Hamiltonian Circuit function is adopted to enhance the conventional TSP system. All these methods, sometimes even Greedy Search, have given the same TSP route for any of test cases. In developing the GUI application, different integer programming methods such as Exhaustive Search, Heuristic A-Star Search, BitMask Dynamic Programming, Branch-and-Bound Algorithm, and Greedy Search have been implemented with the help of Google APIs. In this study, a real-world application that draws the real time route of the TSP using the current traffic intensity information taken from Google Maps is proposed. Conclusively, nearest insertion which utilises lesser execution time and shorter distance covered is found to be more efficient and better than nearest neighbour approach.The Travelling Salesman Problem (TSP), defined as returning to the starting point after visiting all the points with the least cost, is the modeling framework for many engineering problems. The total distance covered by nearest neighbour are : 7584, 8946, 8835, 8196, 7946, 7156, (in km) respectively and the distance covered by nearest insertion are : 8246, 8509, 8574, 7227, 7946, 7156, (in km) respectively. A sample of six cities were taken and nearest neighbour’s execution time for the six cities are: 59, 60, 63, 97, 58, 77, (in ms) respectively and for nearest insertion are: 137, 68, 48, 55, 45, 57, (in ms) respectively. The algorithms were implemented using c# programming language of the dot Net framework. This work adopts the nearest neighbour and nearest insertion algorithm to solve the well- known travelling salesman problem. In other words, the problem deals with finding a route covering all cities so that total distance and execution time is minimized. The travelling salesman problem (TSP) is a combinatorial optimization problem in which the goal is to find the shortest path between different cities that the salesman takes. We also present a thorough analysis of the implemented algorithm for several cases using different parameter values. The parallel genetic algorithm devised for GPUs has been compared to the alternative algorithms and found to be promising in terms of speed-up. The proposed approach is based on converting the problem into Traveling Salesman Problem which is solved using Genetic Algorithms on Graphical Processing Unit (GPU). This study presents an approach to find a near-optimal solution to the flight route planning problem. Due to the combinatorial nature of the problem it is impractical to devise a solution using brute force approaches. The optimal flight route planning manages to find a tour passing through all of the waypoints by covering the minimum possible distance. A flight route may consist of hundreds of predefined geographical positions called waypoints. Flight route planning operation has to be done before the actual mission is executed. The route that the aerial reconnaissance vehicle will follow is known as the flight route. Aerial surveillance missions require a geographical region known as the area of interest to be inspected. ![]()
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