Modelling and Analysis of an electric vehicle routing problem

The Vehicle Routing Problem (VRP) has been studied for many decades . The majority of these works focus on the static and deterministic cases of vehicle routing in which all information is known at the time of the planning of the routes. A Green Vehicle Routing Problem (G-VRP) is formulated and solution for these techniques are developed to aid organizations with alternative fuel-powered vehicle fleets in overcoming difficulties that exist as a result of limited number of vehicle driving range in conjunction with limited refueling infrastructure. The G-VRP is formulated as a mixed integer linear program.During the past decade the number of published papers dealing with dynamic transportation models has been growing. The dynamic vehicle routing problem is only a subset of these models. In most real-life applications though, stochastic and/or dynamic information occurs parallel to the routes being carried out. In Real-life examples of stochastic and/or dynamic routing problems they commonly include the distribution of oil to private households, the pick-up of courier mail packages and the dispatching of busses for the transportation of elderly and handicapped people. In these examples the customer profiles (i.e. the time to begin service, the geographic location, the actual demand etc.) may not be known at the time of the planning or even when service has begun for the advance request customers. Two distinct features make the planning of high quality routes in this environment much more difficult than in its deterministic counterpart; firstly, the constant change, secondly, the time horizon. Here in our thesis we have implemented the part of model which is a Ph.D. thesis of Dossier de candidature pour le prix de lameilleure these en transport et logistique by Yun He and analyse the Electric Vehicle Routing Problem considering the Energy consumption which is solved by using Python and the Mathematical tool Gurobi to solve the integer programming model.