We propose an Adaptive Large Neighborhood Search metaheuristic to solve a vehicle relocation problem arising in the management of electric carsharing systems. The solution approach, aimed to optimize the total prot, is tested on three real-like benchmark sets of instances. It is compared with a Tabu Search, ad hoc designed for this work, with a previous Ruin and Recreate metaheuristic and with the optimal results obtained via Mixed Integer Linear Programming. We also develop a bounding procedure to evaluate the solution quality when the optimal solution is not available.
An Adaptive Large Neighborhood Search for Relocating Vehicles in Electric Carsharing Services / Bruglieri, M.; Pezzella, F.; Pisacane, O.. - In: DISCRETE APPLIED MATHEMATICS. - ISSN 0166-218X. - 253:(2018), pp. 185-200. [10.1016/j.dam.2018.03.067]
An Adaptive Large Neighborhood Search for Relocating Vehicles in Electric Carsharing Services
F. Pezzella;O. Pisacane
2018-01-01
Abstract
We propose an Adaptive Large Neighborhood Search metaheuristic to solve a vehicle relocation problem arising in the management of electric carsharing systems. The solution approach, aimed to optimize the total prot, is tested on three real-like benchmark sets of instances. It is compared with a Tabu Search, ad hoc designed for this work, with a previous Ruin and Recreate metaheuristic and with the optimal results obtained via Mixed Integer Linear Programming. We also develop a bounding procedure to evaluate the solution quality when the optimal solution is not available.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
