scholarly journals Optimization of multi objective vehicle routing problem using a new hybrid algorithm based on particle swarm optimization and artificial bee colony algorithm considering Precedence constraints

2018 ◽  
Vol 57 (4) ◽  
pp. 2225-2239 ◽  
Author(s):  
Davoud Sedighizadeh ◽  
Houman Mazaheripour
Keyword(s):  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Algorithm ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Multi Objective ◽  
Bee Colony

scholarly journals An Enhanced ABC algorithm to Solve the Vehicle Routing Problem with Time Windows

2020 ◽  
Vol 14 (1) ◽  
pp. 46-52 ◽  
Author(s):  
Krittika Kantawong ◽  
Sakkayaphop Pravesjit
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Time Windows ◽  
Scatter Search ◽  
The Other ◽  
Routing Problem ◽  
Bee Colony ◽  
Problem Instances

This work proposes an enhanced artificial bee colony algorithm (ABC) to solve the vehicle routing problem with time windows (VRPTW). In this work, the fuzzy technique, scatter search method, and SD-based selection method are combined into the artificial bee colony algorithm. Instead of randomly producing the new solution, the scout randomly chooses the replacement solution from the abandoned solutions from the onlooker bee stage. Effective customer location networks are constructed in order to minimize the overall distance. The proposed algorithm is tested on the Solomon benchmark dataset where customers live in different geographical locations. The results from the proposed algorithm are shown in comparison with other algorithms in the literature. The findings from the computational results are very encouraging. Compared to other algorithms, the proposed algorithm produces the best result for all testing problem sets. More significantly, the proposed algorithm obtains better quality than the other algorithms for 39 of the 56 problem instances in terms of vehicle numbers. The proposed algorithm obtains a better number of vehicles and shorter distances than the other algorithm for 20 of the 39 problem instances.

scholarly journals Metaheuristic algorithm for solving the multi-objective vehicle routing problem with time window and drones

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142092003
Author(s):  
Yun-qi Han ◽  
Jun-qing Li ◽  
Zhengmin Liu ◽  
Chuang Liu ◽  
Jie Tian
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Time Window ◽  
Total Energy Consumption ◽  
Routing Problem ◽  
Bee Colony

In some special rescue scenarios, the needed goods should be transported by drones because of the landform. Therefore, in this study, we investigate a multi-objective vehicle routing problem with time window and drone transportation constraints. The vehicles are used to transport the goods and drones to customer locations, while the drones are used to transport goods vertically and timely to the customer. Three types of objectives are considered simultaneously, including minimization of the total energy consumption of the trucks, total energy consumption of the drones, and the total number of trucks. An improved artificial bee colony algorithm is designed to solve the problem. In the proposed algorithm, each solution is represented by a two-dimensional vector, and the initialization method based on the Push-Forward Insertion Heuristic is embedded. To enhance the exploitation abilities, an improved employed heuristic is developed to perform detailed local search. Meanwhile, a novel scout bee strategy is presented to improve the global search abilities of the proposed algorithm. Several instances extended from the Solomon instances are used to test the performance of the proposed improved artificial bee colony algorithm. Experimental comparisons with the other efficient algorithms in the literature verify the competitive performance of the proposed algorithm.

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