Multilevel Variable Neighborhood Search for Periodic Routing Problems

2010 ◽  
pp. 226-238 ◽  
Author(s):  
Sandro Pirkwieser ◽  
Günther R. Raidl
Keyword(s):  
Variable Neighborhood Search ◽  
Neighborhood Search ◽  
Routing Problems ◽  
Periodic Routing

scholarly journals A variable neighborhood search heuristic for periodic routing problems

2009 ◽  
Vol 195 (3) ◽  
pp. 791-802 ◽  
Author(s):  
Vera C. Hemmelmayr ◽  
Karl F. Doerner ◽  
Richard F. Hartl
Keyword(s):  
Variable Neighborhood Search ◽  
Neighborhood Search ◽  
Routing Problems ◽  
Search Heuristic ◽  
Periodic Routing

A biased-randomized variable neighborhood search for sustainable multi-depot vehicle routing problems

2018 ◽  
Vol 26 (3) ◽  
pp. 401-422 ◽  
Author(s):  
Lorena Reyes-Rubiano ◽  
Laura Calvet ◽  
Angel A. Juan ◽  
Javier Faulin ◽  
Lluc Bové
Keyword(s):  
Vehicle Routing ◽  
Variable Neighborhood Search ◽  
Neighborhood Search ◽  
Vehicle Routing Problems ◽  
Routing Problems

Application of variable neighborhood search for solving large-scale many to many hub location routing problems

2019 ◽  
Vol 16 (5) ◽  
pp. 683-697
Author(s):  
Mehdi Abbasi ◽  
Nahid Mokhtari ◽  
Hamid Shahvar ◽  
Amin Mahmoudi
Keyword(s):  
Large Scale ◽  
Variable Neighborhood Search ◽  
Benders Decomposition ◽  
Neighborhood Search ◽  
Np Hard ◽  
Computational Results ◽  
Hub Location ◽  
Routing Problems ◽  
Content Type ◽  
Location Routing

Purpose The purpose of this paper is to solve large-scale many-to-many hub location-routing problem (MMHLRP) using variable neighborhood search (VNS). The MMHLRP is a combination of a single allocation hub location and traveling salesman problems that are known as one of the new fields in routing problems. MMHLRP is considered NP-hard since the two sub-problems are NP-hard. To date, only the Benders decomposition (BD) algorithm and the variable neighborhood particle swarm optimization (VNPSO) algorithm have been applied to solve the MMHLRP model with ten nodes and more (up to 300 nodes), respectively. In this research, the VNS method is suggested to solve large-scale MMHLRP (up to 1,000 nodes). Design/methodology/approach Generated MMHLRP sample tests in the previous work were considered and were added to them. In total, 35 sample tests of MMHLRP models between 10 and 1,000 nodes were applied. Three methods (BD, VNPSO and VNS algorithms) were run by a computer to solve the generated sample tests of MMHLRP. The maximum available time for solving the sample tests was 6 h. Accuracy (value of objective function solution) and speed (CPU time consumption) were considered as two major criteria for comparing the mentioned methods. Findings Based on the results, the VNS algorithm was more efficient than VNPSO for solving the MMHLRP sample tests with 10–440 nodes. It had many similarities with the exact BD algorithm with ten nodes. In large-scale MMHLRP (sample tests with more than 440 nodes (up to 1,000 nodes)), the previously suggested methods were disabled to solve the problem and the VNS was the only method for solving samples after 6 h. Originality/value The computational results indicated that the VNS algorithm has a notable efficiency in comparison to the rival algorithm (VNPSO) in order to solve large-scale MMHLRP. According to the computational results, in the situation that the problems were solved for 6 h using both VNS and VNPSO, VNS solved the problems with more accuracy and speed. Additionally, VNS can only solve large-scale MMHLRPs with more than 440 nodes (up to 1,000 nodes) during 6 h.

A variable neighborhood search heuristic algorithm for production routing problems

2018 ◽  
Vol 66 ◽  
pp. 311-318 ◽  
Author(s):  
Yuzhuo Qiu ◽  
Liang Wang ◽  
Xiaoling Xu ◽  
Xuanjing Fang ◽  
Panos M. Pardalos
Keyword(s):  
Heuristic Algorithm ◽  
Variable Neighborhood Search ◽  
Neighborhood Search ◽  
Routing Problems ◽  
Search Heuristic ◽  
Production Routing
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