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  2. 10 AI Tools That Can Plan Your Next Road Trip

    www.aol.com/10-ai-tools-plan-next-145700892.html

    2. Optimize your route. Optimizing your travel routes can help you save time, money, and effort. Apart from arriving at your destination faster, you can save on fuel, accommodations, and other ...

  3. Journey planner - Wikipedia

    en.wikipedia.org/wiki/Journey_Planner

    A journey planner, trip planner, or route planner is a specialized search engine used to find an optimal means of travelling between two or more given locations, sometimes using more than one transport mode. [1][2] Searches may be optimized on different criteria, for example fastest, shortest, fewest changes, cheapest. [3]

  4. Vehicle routing problem - Wikipedia

    en.wikipedia.org/wiki/Vehicle_routing_problem

    The vehicle routing problem (VRP) is a combinatorial optimization and integer programming problem which asks "What is the optimal set of routes for a fleet of vehicles to traverse in order to deliver to a given set of customers?" It generalises the travelling salesman problem (TSP). It first appeared in a paper by George Dantzig and John Ramser ...

  5. Travelling salesman problem - Wikipedia

    en.wikipedia.org/wiki/Travelling_salesman_problem

    Travelling Salesman, by director Timothy Lanzone, is the story of four mathematicians hired by the U.S. government to solve the most elusive problem in computer-science history: P vs. NP. [77] Solutions to the problem are used by mathematician Robert A. Bosch in a subgenre called TSP art.

  6. Heuristic routing - Wikipedia

    en.wikipedia.org/wiki/Heuristic_routing

    Heuristic routing is a system used to describe how deliveries are made when problems in a network topology arise. Heuristic is an adjective used in relation to methods of learning, discovery, or problem solving. Routing is the process of selecting paths to specific destinations. Heuristic routing is used for traffic in the telecommunications ...

  7. Simulated annealing - Wikipedia

    en.wikipedia.org/wiki/Simulated_annealing

    Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem. For large numbers of local optima, SA can find the global optimum. [1]

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