WeRoute: Route Optimization Web-Based System and Driver Mobile Application

This paper intends to conceptualise an optimisation solution for vehicle routing that can get the best routing result and release the most optimal route to the driver, namely WeRoute. The objectives of the paper are to manage the data efficiently, save time, reduce cost, enhance customer satisfaction, and decrease the emission of carbon. Moreover, this is also known as the vehicle routing problem, which deals with a range of variables, including drivers, stops, roads, and customers. The method, Genetic algorithm, was developed to improve the efficiency of generating feasible routes for a project. A team of drivers and several stops are needed to generate the solution of optimising the vehicle routing. It can be said that the more drivers or stops, the more complicated the problem becomes, such as cost controls and vehicle limitations. Thus, a route optimisation tool slowly becomes the key to ensuring the delivery business as efficiently as possible.

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Genetic Algorithm Optimization in Vehicle Routing Problem

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.361-363.2249 ◽

2013 ◽

Vol 361-363 ◽

pp. 2249-2254

Author(s):

Lang Zhi Zhang ◽

Song Yan Chen ◽

Yong Yue Cui

Keyword(s):

Genetic Algorithm ◽

Vehicle Routing ◽

Vehicle Routing Problem ◽

Route Optimization ◽

Algorithm Optimization ◽

Genetic Algorithm Optimization ◽

Routing Problem ◽

Mutation Probability ◽

Vehicle Route ◽

Crossover Probability

Numerous strategies for optimizing vehicle route based on genetic algorithm (GA) have been put forward. However there is still much room for improvement despite the existing experiment results. In this paper, significant improvement of traditional genetic algorithm is achieved, dealing with discrete vehicle route optimization. In view of multi-client points equally distributing around logistics centre, initial group optimization being performed, crossover probability being decreased, mutation probability being improved, chromosome calculation being simplified, optimization being accelerated and genetic performance quantity is reduced. All this offers powerful support to genetic algorithm for multi client points.

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The Mathematical Model and an Genetic Algorithm for the Two-Echelon Electric Vehicle Routing Problem

Journal of Physics Conference Series ◽

10.1088/1742-6596/1813/1/012006 ◽

2021 ◽

Vol 1813 (1) ◽

pp. 012006

Author(s):

Yue Zhang ◽

Shenghan Zhou ◽

XinPeng Ji ◽

Bang Chen ◽

HouXiang Liu ◽

...

Keyword(s):

Genetic Algorithm ◽

Mathematical Model ◽

Vehicle Routing ◽

Electric Vehicle ◽

Vehicle Routing Problem ◽

Routing Problem ◽

The Mathematical Model

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A hybrid genetic algorithm for the multi-depot open vehicle routing problem

OR Spectrum ◽

10.1007/s00291-012-0289-0 ◽

2012 ◽

Vol 36 (2) ◽

pp. 401-421 ◽

Cited By ~ 29

Author(s):

Ran Liu ◽

Zhibin Jiang ◽

Na Geng

Keyword(s):

Genetic Algorithm ◽

Vehicle Routing ◽

Vehicle Routing Problem ◽

Hybrid Genetic Algorithm ◽

Routing Problem ◽

Open Vehicle Routing Problem ◽

Open Vehicle Routing

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Capacitated vehicle routing problem model for carriers

Journal of Transport and Supply Chain Management ◽

10.4102/jtscm.v12i0.345 ◽

2018 ◽

Vol 12 ◽

Cited By ~ 3

Author(s):

Irma-Delia Rojas-Cuevas ◽

Santiago-Omar Caballero-Morales ◽

Jose-Luis Martinez-Flores ◽

Jose-Rafael Mendoza-Vazquez

Keyword(s):

Vehicle Routing ◽

Vehicle Routing Problem ◽

Network Flow ◽

Route Planning ◽

Test Problems ◽

Optimal Route ◽

Routing Problem ◽

Problem Model ◽

Capacitated Vehicle Routing Problem ◽

Capacitated Vehicle

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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