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

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/256998
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