Exact Algorithms for the Chance-Constrained Vehicle Routing Problem

2016 ◽  
pp. 89-101 ◽  
Author(s):  
Thai Dinh ◽  
Ricardo Fukasawa ◽  
James Luedtke
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Exact Algorithms ◽  
Routing Problem ◽  
Constrained Vehicle Routing

Two exact algorithms for the distance-constrained vehicle routing problem

Networks ◽  
1984 ◽  
Vol 14 (1) ◽  
pp. 161-172 ◽  
Author(s):  
Gilbert Laporte ◽  
Martin Desrochers ◽  
Yves Nobert
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Exact Algorithms ◽  
Routing Problem ◽  
Constrained Vehicle Routing

Exact algorithms for the chance-constrained vehicle routing problem

2017 ◽  
Vol 172 (1-2) ◽  
pp. 105-138 ◽  
Author(s):  
Thai Dinh ◽  
Ricardo Fukasawa ◽  
James Luedtke
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Exact Algorithms ◽  
Routing Problem ◽  
Constrained Vehicle Routing

scholarly journals A hybrid metaheuristic for solving asymmetric distance-constrained vehicle routing problem

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ha-Bang Ban ◽  
Phuong Khanh Nguyen
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Natural Extension ◽  
Solution Space ◽  
Neighborhood Search ◽  
Np Hard ◽  
Routing Problem ◽  
Hybrid Metaheuristic ◽  
Asymmetric Distance ◽  
Constrained Vehicle Routing

AbstractThe Asymmetric Distance-Constrained Vehicle Routing Problem (ADVRP) is NP-hard as it is a natural extension of the NP-hard Vehicle Routing Problem. In ADVRP problem, each customer is visited exactly once by a vehicle; every tour starts and ends at a depot; and the traveled distance by each vehicle is not allowed to exceed a predetermined limit. We propose a hybrid metaheuristic algorithm combining the Randomized Variable Neighborhood Search (RVNS) and the Tabu Search (TS) to solve the problem. The combination of multiple neighborhoods and tabu mechanism is used for their capacity to escape local optima while exploring the solution space. Furthermore, the intensification and diversification phases are also included to deliver optimized and diversified solutions. Extensive numerical experiments and comparisons with all the state-of-the-art algorithms show that the proposed method is highly competitive in terms of solution quality and computation time, providing new best solutions for a number of instances.

Dynamic Vehicle Routing Solution in the Framework of Nature-Inspired Algorithms

2016 ◽  
pp. 36-50
Author(s):  
Omprakash Kaiwartya ◽  
Pawan Kumar Tiwari ◽  
Sushil Kumar ◽  
Mukesh Prasad
Keyword(s):  
Customer Satisfaction ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Exact Algorithms ◽  
Real Field ◽  
Data Sets ◽  
Dynamic Vehicle Routing ◽  
Routing Problem ◽  
Field Environment ◽  
Nature Inspired Algorithms

Vehicle Routing Problem (VRP), a well-known combinatorial optimization problem had been presented by Dantzing and Hamser in 1959. The problem has taken its inspiration from the transport field. In real field environment, a lot of variants of the problem exist that actually belongs to the class of NP-hard problem. Dynamic Vehicle routing problem (DVRP) is one of the variant of VRP that varies with respect to time. In DVRP, new customer orders appear over time and new route must be reconfigured at any instantaneous time. Although, some exact algorithms such as dynamic programming methods, branch and bound etc. can be applied to find the optimal route of a smaller size VRP. But, These Algorithms fail to give the solution of existed model of VRP in real field environment under given real time constraints. Courier services, dial a ride services and express mail delivery etc. are the few examples of real field environment problems that can be formulated in the form of DVRP. In this chapter, A novel variants of DVRP named as DVRP with geographic ranking (DVRP-GR) has been proposed. In DVRP-GR, geographical ranking, customer ranking, service time, expected reachability time, customer satisfaction level have been optimized. A solution of DVRP-GR using seed based particle swarm optimization (S-DVRS-PSO) has been also proposed. The simulations have been performed using customized simulator developed in C++ environment. The data sets used in the simulations are OMK-01, OMK-02 and OMK-03 generated in real vehicular environment. The solution of the proposed algorithm has been compared with the randomized solution technique. Analysis of the simulation results confirms the effectiveness of the proposed solution in terms of various parameters considered viz. number of vehicles, expected reachability time, profit and customer satisfaction.

Exact Algorithms for the Vehicle Routing Problem with Time Windows and Combinatorial Auction

2019 ◽  
Vol 53 (2) ◽  
pp. 427-441 ◽  
Author(s):  
Zhenzhen Zhang ◽  
Zhixing Luo ◽  
Hu Qin ◽  
Andrew Lim
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Exact Algorithms ◽  
Combinatorial Auction ◽  
Routing Problem
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