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By route planning one understands the problem to find as good an allocation from vehicles to orders and for each vehicle an optimal sequence as possible of the order locations which can be served. An order consists usually of bringing a certain number of units of a transmission from a start to a goal. A solution of a route planning problem has therefore usually two aspects: the Clusterung indicates, which orders are combined into a route, and the Routing defines, in which order the points are served within a route. Objective of a route planning is e.g. the minimization of the number of the assigned vehicles, the put back distance, the assignment or a more complex cost function. For the standard problem for route planning are appropriate all starting or goals in a depot and it are available there a limited or unlimited number of identical vehicles with limited capacity. Other variants regard several depots or arbitrary starting and goals (so-called Pickup and Delivery problems).
In the reality setting of tasks is extended still by many restrictions. For example one regards several depots, a heterogeneous vehicle park or priority relations between orders. Another possible auxiliary task is the view of time windows, within whose borders a vehicle must arrive at the customer. Of a dynamic route planning one speaks if the situation concerning orders changes dynamically during planning (e.g. by again which are added or cancelled orders).
Applications exist apart from the logistics range in all industries, those its customers supply (e.g. furniture industry, garbage disposal or Automatenbeschicker). Into many enterprises route planning software is used, in order to arrange the resulting routes and on the basis criteria, like e.g. the observance of allowed time or weight barriers to optimize as well as transport costs.
The Gundmodell of route planning belongs to the class of the NP weight problems. Therefore for the solution of the problem heuristics are used. Simple solution procedures are the Savings heuristic and the Sweep algorithm. Solutions with better quality are based on genetic algorithms, simulated cooling and taboo search. They use local search strategies, with which the sequence is exchanged by orders and/or allocation from orders to vehicles. Lately also ever more frequently the ant algorithm than problem solution is considered.
As Subproblem of route planning the problem of the commercial traveller results, by regarding a vehicle with unlimited capacity and letting this with minimum costs or distance drive.
Route planning software supports enterprises when planning and optimization of routes.
In addition the software needs a digital road system, a customer master file, a vehicle and a driver list as well as a current order list among other things as database.
Distances and travel time can be inferred roughly assistance by coordinates of the georeferenzierten customer's addresses estimated or from a distance work, alternatively operate algorithms for distance optimization on a digital road system.
The optimization happens, as the transportation need of a number of customers is combined in such a manner into one or more routes that temporal defaults of the customers, loads and capacities of the vehicles, tracing and work times of the drivers and maintenance cycles of the vehicles are kept, while the resulting transport costs are minimized.
These consist possibly of fixed costs consisting for drivers, planning manager and vehicle as well as the variable vehicle park costs of consumption costs, duty costs, maintenance, work time and overtime.
See also: Route planner
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R» Radio frequency identification» Reloading problem » Renetting procedure » Retoure » Rotating days » Route of transportation » Route planning |
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