Maurizio Bruglieri

TL;DR: A Mixed Integer Linear Programming formulation of the problem is proposed assuming that the battery recharging level reached at each station is a decision variable in order to guarantee more flexible routes and a Variable Neighborhood Search Branching is designed for solving the problem at hand in reasonable computational times.

TL;DR: This paper formally defines the problem, mentions some examples and summarizes general results, and provides an annotated bibliography of combinatorial optimization problems of which versions with cardinality constraint have been considered in the literature.

TL;DR: A mixed integer linear programming (MILP) formulation of the EVRP and some techniques to speedup its solution through a state‐of‐the‐art solver (CPLEX), and a simple but effective heuristic based on such a formulation and four upper bound generation methods.

TL;DR: In this article, the authors propose mathematical programming models for solving problems arising from planning and running an energy production process based on burning biomasses, and show that a spatial branch-and-bound type algorithm applied to them is guaranteed to converge to an exact optimum in a finite number of steps.

TL;DR: A two-phase solution approach in which a route is seen as the composition of paths, each one handling a subset of customers without intermediate stops at AFSs, which outperforms the existing exact methods and can also address larger- sized instances outperforming, in terms of solution quality, the best heuristic approach in the literature.