scholarly journals An Adaptive Large Neighborhood Search for the green mixed fleet vehicle routing problem with realistic energy consumption and partial recharges

2021 ◽  
Vol 105 ◽  
pp. 107251
Author(s):  
Vincent F. Yu ◽  
Panca Jodiawan ◽  
Aldy Gunawan
Keyword(s):  
Energy Consumption ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Adaptive Large Neighborhood Search ◽  
Routing Problem ◽  
Large Neighborhood

scholarly journals An Adaptive Large Neighborhood Search for the Larger-Scale Instances of Green Vehicle Routing Problem with Time Windows

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Zixuan Yu ◽  
Ping Zhang ◽  
Yang Yu ◽  
Wei Sun ◽  
Min Huang
Keyword(s):  
Vehicle Routing ◽  
Carbon Emissions ◽  
Vehicle Routing Problem ◽  
Computational Time ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Adaptive Large Neighborhood Search ◽  
Routing Problem ◽  
Large Neighborhood ◽  
Insertion Method

Due to huge amount of greenhouse gases emission (such as CO2), freight has been adversely affecting the global environment in facilitating the global economy. Therefore, green vehicle routing problem (GVRP), aiming to minimize the total carbon emissions in the transportation, has become a hot issue. In this paper, an adaptive large neighborhood search (ALNS) algorithm is proposed to solve large-scale instances of GVRP. The core of ALNS algorithm is destroy operators and repair operators. In the destroy operators, a new removal heuristic applying to the characteristics of GVRP is proposed. The heuristic can quickly remove customers who bring a large amount of carbon emissions with pertinence, and these customers may be arranged more properly in future repair operators. In the repair operators, a fast insertion method is developed. In the fast insertion method, the feasibility of a new route is judged by checking the constraints of partial customers after the inserted customer, instead of checking the constraints of all customers. Thus, the computational time of the ALNS algorithm is greatly saved. Computational experiments were performed on Solomon benchmark with 100 customers and Homberger benchmark instances with up to 1000 customers. Given the same computational time, the proposed ALNS improves the average accuracy by 8.49% compared with the classic ALNS. In the optimal situation, the improvement can achieve 33.61%.

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