Heuristic and exact algorithms for the multi-pile vehicle routing problem

OR Spectrum ◽  
2009 ◽  
Vol 33 (4) ◽  
pp. 931-959 ◽  
Author(s):  
Fabien Tricoire ◽  
Karl F. Doerner ◽  
Richard F. Hartl ◽  
Manuel Iori
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Exact Algorithms ◽  
Routing Problem

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

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

Heuristic and exact algorithms for a min–max selective vehicle routing problem

2011 ◽  
Vol 38 (7) ◽  
pp. 1054-1065 ◽  
Author(s):  
Cristiano Arbex Valle ◽  
Leonardo Conegundes Martinez ◽  
Alexandre Salles da Cunha ◽  
Geraldo R. Mateus
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Exact Algorithms ◽  
Routing Problem

Two exact algorithms for the vehicle routing problem on trees

1999 ◽  
Vol 46 (1) ◽  
pp. 75-89 ◽  
Author(s):  
Pontien Mbaraga ◽  
Andr� Langevin ◽  
Gilbert Laporte
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Exact Algorithms ◽  
Routing Problem

Learning-Based Branch-and-Price Algorithms for the Vehicle Routing Problem with Time Windows and Two-Dimensional Loading Constraints

2021 ◽  
Author(s):  
Xiangyi Zhang ◽  
Lu Chen ◽  
Michel Gendreau ◽  
André Langevin
Keyword(s):  
Vehicle Routing ◽  
Operations Research ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Learning Model ◽  
Exact Algorithms ◽  
Two Dimensional ◽  
Branch And Price ◽  
Routing Problem ◽  
Loading Constraints

A capacitated vehicle routing problem with two-dimensional loading constraints is addressed. Associated with each customer are a set of rectangular items, the total weight of the items, and a time window. Designing exact algorithms for the problem is very challenging because the problem is a combination of two NP-hard problems. An exact branch-and-price algorithm and an approximate counterpart are proposed to solve the problem. We introduce an exact dominance rule and an approximate dominance rule. To cope with the difficulty brought by the loading constraints, a new column generation mechanism boosted by a supervised learning model is proposed. Extensive experiments demonstrate the superiority of integrating the learning model in terms of CPU time and calls of the feasibility checker. Moreover, the branch-and-price algorithms are able to significantly improve the solutions of the existing instances from literature and solve instances with up to 50 customers and 103 items. Summary of Contribution: We wish to submit an original research article entitled “Learning-based branch-and-price algorithms for a vehicle routing problem with time windows and two-dimensional loading constraints” for consideration by IJOC. We confirm that this work is original and has not been published elsewhere, nor is it currently under for publication elsewhere. In this paper, we report a study in which we develop two branch-and-price algorithms with a machine learning model injected to solve a vehicle routing problem integrated the two-dimensional packing. Due to the complexity brought by the integration, studies on exact algorithms in this field are very limited. Our study is important to the field, because we develop an effective method to significantly mitigate computational burden brought by the packing problem so that exactness turns to be achievable within reasonable time budget. The approach can be generalized to the three-dimensional case by simply replacing the packing algorithm. It can also be adapted for other VRPs when high-dimensional loading constraints are concerned. Broadly speaking, the study is a typical example of adopting supervised learning to achieve acceleration for operations research algorithms, which expands the envelop of computing and operations research. Hence, we believe this manuscript is appropriate for publication by IJOC.

scholarly journals Erratum—Exact Algorithms for the Clustered Vehicle Routing Problem

2016 ◽  
Vol 64 (2) ◽  
pp. 456-457 ◽  
Author(s):  
Maaike Hoogeboom ◽  
Maria Battarra ◽  
Güneş Erdoǧan ◽  
Daniele Vigo
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Exact Algorithms ◽  
Routing Problem
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