A Mathematical Programming Model for the Green Mixed Fleet Vehicle Routing Problem with Realistic Energy Consumption and Partial Recharges A green mixed fleet vehicle routing with realistic energy consumption and partial recharges problem (GMFVRPRECPR) is addressed in this paper. This problem involves a fixed number of electric vehicles and internal combustion vehicles to serve a set of customers. The realistic energy consumption which depends on several variables is utilized to calculate the electricity consumption of an electric vehicle and fuel consumption of an internal combustion vehicle. Partial recharging policy is included into the problem to represent the real life scenario. The objective of this problem is to minimize the total travelled distance and the total emission produced by internal combustion vehicles. This is a new variant of problem which is developed from a mixed fleet of electric and internal combustion vehicles, full recharging policy, and operational cost minimization. A mixed integer programming model is then developed to address this problem and commercial software is utilized to solve the model.

Routing a Fleet of Automated Vehicles in a Capacitated Transportation Network Routing of a fleet of automated unitoccupancy vehicles in a capacitated transportation network is an emerging problem that needs to be addressed to realize largescale automated transportation systems. We adopt an existing networkflowbased model for the problem and present a new reformulation based on DantzigWolfe decomposition. This reformulation allows us to apply the column generation solution technique which, in turn, enables us to solve largescale problem instances with tens of thousands of requests on networks with thousands of links. We empirically compare our method to the stateoftheart approach on several standard benchmark instances and find that the computational time of our solution approach scales qualitatively better in all tested problem instance parameters: namely, in the size of the transportation network, in the magnitude of demand intensity, and in the number of demand flows.