A generalized crossing local search method for solving vehicle routing problems

2007 ◽  
Vol 58 (4) ◽  
pp. 528-532 ◽  
Author(s):  
L Zeng ◽  
H L Ong ◽  
K M Ng
Keyword(s):  
Local Search ◽  
Vehicle Routing ◽  
Search Method ◽  
Vehicle Routing Problems ◽  
Local Search Method ◽  
Routing Problems

AN ASSIGNMENT-BASED LOCAL SEARCH METHOD FOR SOLVING VEHICLE ROUTING PROBLEMS

2005 ◽  
Vol 22 (01) ◽  
pp. 85-104 ◽  
Author(s):  
L. ZENG ◽  
H. L. ONG ◽  
K. M. NG
Keyword(s):  
Simulated Annealing ◽  
Local Search ◽  
Vehicle Routing ◽  
Search Method ◽  
Benchmark Problems ◽  
Heuristic Methods ◽  
Vehicle Routing Problems ◽  
Local Search Method ◽  
Routing Problems ◽  
Improvement Algorithm

In this paper, we propose an assignment-based local search method for solving vehicle routing problems. This method is a multi-route improvement algorithm that can operate on several routes at a time. To evaluate the performance of the proposed method, extensive computational experiments on the proposed method applied to a set of benchmark problems are carried out. The results show that the proposed method, when coupled with metaheuristics such as simulated annealing, is comparable with other efficient heuristic methods proposed in the literature.

scholarly journals Novel Encoding and Routing Balance Insertion Based Particle Swarm Optimization with Application to Optimal CVRP Depot Location Determination

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Ruey-Maw Chen ◽  
Yin-Mou Shen
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Vehicle Routing ◽  
Particle Swarm ◽  
Transportation Cost ◽  
Vehicle Routing Problems ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Routing Problems ◽  
Vehicle Routes

A depot location has a significant effect on the transportation cost in vehicle routing problems. This study proposes a hierarchical particle swarm optimization (PSO) including inner and outer layers to obtain the best location to establish a depot and the corresponding optimal vehicle routes using the determined depot location. The inner layer PSO is applied to obtain optimal vehicle routes while the outer layer PSO is to acquire the depot location. A novel particle encoding is suggested for the inner layer PSO, the novel PSO encoding facilitates solving the customer assignment and the visiting order determination simultaneously to greatly lower processing efforts and hence reduce the computation complexity. Meanwhile, a routing balance insertion (RBI) local search is designed to improve the solution quality. The RBI local search moves the nearest customer from the longest route to the shortest route to reduce the travel distance. Vehicle routing problems from an operation research library were tested and an average of 16% total routing distance improvement between having and not having planned the optimal depot locations is obtained. A real world case for finding the new plant location was also conducted and significantly reduced the cost by about 29%.

An Iterated Local Search Algorithm for Fuel Consumption Optimization of Vehicle Routing Problems

2011 ◽  
Vol 148-149 ◽  
pp. 1248-1251
Author(s):  
Xu Dong Wu
Keyword(s):  
Local Search ◽  
Vehicle Routing ◽  
Fuel Consumption ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Iterated Local Search ◽  
Vehicle Routing Problems ◽  
Local Search Algorithm ◽  
Routing Problems ◽  
Combinatorial Optimization Problems

The iterated local search algorithm has been widely used in combinatorial optimization problems. A new fuel consumption objective for the vehicle routing problems was presented in this paper. A fuel consumption modal of the vehicle load is introduced and an improved iterated local search algorithm is used for the problem. An initial solution is generated by the Solomon I1 algorithm, and then the iterated local search algorithm is proposed for the fuel consumption optimization.

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