Improved Genetic Algorithm for Capacitated Vehicle Routing Problem

2012 ◽  
Vol 253-255 ◽  
pp. 1459-1462
Author(s):  
Chun Yu Ren

This paper studies capacitated vehicle routing problem. Since the standard genetic algorithm is short of convergent speed and partial searching ability as well as easily premature, improved genetic algorithm is then adopted as an optimized solution. Firstly, sequence of real numbers coding is used to simplify the problem; it may construct the initial solution pertinently in order to improve the feasibility. The individual amount control choice strategy can guard the diversity of group. The combined hill-climbing algorithm can strengthen the partial searching ability of chromosome. Finally, comparing to a set of standard test problems, simulation results demonstrate the effectiveness and good quality.

Author(s):  
Irma-Delia Rojas-Cuevas ◽  
Santiago-Omar Caballero-Morales ◽  
Jose-Luis Martinez-Flores ◽  
Jose-Rafael Mendoza-Vazquez

Background: The Capacitated Vehicle Routing Problem (CVRP) is one of the most important transportation problems in logistics and supply chain management. The standard CVRP considers a fleet of vehicles with homogeneous capacity that depart from a warehouse, collect products from (or deliver products to) a set of customer locations (points) and return to the same warehouse. However, the operation of carrier companies and third-party transportation providers may follow a different network flow for collection and delivery. This may lead to non-optimal route planning through the use of the standard CVRP.Objective: To propose a model for carrier companies to obtain optimal route planning.Method: A Capacitated Vehicle Routing Problem for Carriers (CVRPfC) model is used to consider the distribution scenario where a fleet of vehicles depart from a vehicle storage depot, collect products from a set of customer points and deliver them to a specific warehouse before returning to the vehicle storage depot. Validation of the model’s functionality was performed with adapted CVRP test problems from the Vehicle Routing Problem LIBrary. Following this, an assessment of the model’s economic impact was performed and validated with data from a real carrier (real instance) with the previously described distribution scenario.Results: The route planning obtained through the CVRPfC model accurately described the network flow of the real instance and significantly reduced its distribution costs.Conclusion: The CVRPfC model can thus improve the competitiveness of the carriers by providing better fares to their customers, reducing their distribution costs in the process.


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