scholarly journals Hyperparameter search in periodic vehicle routing problem

2019 ◽  
Vol 259 ◽  
pp. 01003 ◽  
Author(s):  
Ekaterina Grakova ◽  
Martin Golasowski ◽  
Roberto Montemanni ◽  
Kateřina Slaninová ◽  
Jan Martinovič ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Real World ◽  
Vehicle Routing Problem ◽  
Computational Method ◽  
Distributed Environment ◽  
Routing Problem ◽  
Real World Applications ◽  
Distribution Process ◽  
Periodic Vehicle Routing Problem ◽  
Periodic Vehicle Routing

The large number of real-world applications have shown that the use of computational method for distribution process planning produces substantial savings. Many of these applications lead to problem generally known as Vehicle Routing Problem. The real-world applications are highly computationally demanding for larger instances. This article aims to show the possibilities and benefits of using hyperparameter search for solving the Periodic Vehicle Routing Problem for exhausted oil collection by execution on the supercomputing infrastructure using HyperLoom platform. HyperLoom is an open source platform for defining and executing scientific pipelines in a distributed environment. This experiment was run on the supercomputer Salomon operated by IT4Innovations.

A novel hybrid genetic algorithm for the multidepot periodic vehicle routing problem

2014 ◽  
Vol 29 (1) ◽  
pp. 45-54 ◽  
Author(s):  
Mohammad Mirabi
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Real World ◽  
Vehicle Routing Problem ◽  
Hybrid Genetic Algorithm ◽  
Neighborhood Search ◽  
Routing Problem ◽  
Wide Range ◽  
Periodic Vehicle Routing Problem ◽  
Periodic Vehicle Routing

AbstractA genetic algorithm is a metaheuristic proposed to derive approximate solutions for computationally hard problems. In the literature, several successful applications have been reported for graph-based optimization problems, such as scheduling problems. This paper provides one definition of periodic vehicle routing problem for single and multidepots conforming to a wide range of real-world problems and also develops a novel hybrid genetic algorithm to solve it. The proposed hybrid genetic algorithm applies a modified approach to generate a population of initial chromosomes and also uses an improved heuristic called the iterated swap procedure to improve the initial solutions. Moreover, during the implementation a hybrid algorithm, cyclic transfers, an effective class of neighborhood search is applied. The author uses three genetic operators to produce good new offspring. The objective function consists of two terms: total traveled distance at each depot and total waiting time of all customers to take service. Distances are assumed Euclidean or straight line. These conditions are exactly consistent with the real-world situations and have received little attention in the literature. Finally, the experimental results have revealed that the proposed hybrid method can be competitive with the best existing methods as asynchronous parallel heuristic and variable neighborhood search in terms of solution quality to solve the vehicle routing problem.

scholarly journals A Cluster-First Route-Second Heuristic Approach to Solve The Multi-Trip Periodic Vehicle Routing Problem

2019 ◽  
Vol 20 (2) ◽  
pp. 68
Author(s):  
Annisa Kesy Garside ◽  
Nabila Rohmatul Laili
Keyword(s):  
Integer Programming ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Phase 1 ◽  
Working Hours ◽  
Real Problem ◽  
Phase 2 ◽  
Routing Problem ◽  
Periodic Vehicle Routing Problem ◽  
Periodic Vehicle Routing

This paper discusses periodic vehicle routing problems that allow vehicles to travel on multiple trips in a single day. It is known as the Multi-Trip Periodic Vehicles (MTPVRP) Problem Route. Cluster-first route-second (CFRS) heuristics to solve MTPVRP was proposed in this study. In phase 1, customers were divided into clusters using the formulation of integer programming. Phase 2 determined the route of the cluster and verifies that the total journey time to visit the trips does not exceed the working hours of the vehicle. The implementation of the heuristic CFRS to solve the real problem faced by the LPG distributor shows that the procedure could provide a better routing solution.

scholarly journals Branch-and-Price-and-Cut for the Periodic Vehicle Routing Problem with Flexible Schedule Structures

2019 ◽  
Vol 53 (3) ◽  
pp. 850-866 ◽  
Author(s):  
Ann-Kathrin Rothenbächer
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Branch And Price ◽  
Constraint Aggregation ◽  
Routing Problem ◽  
Shortest Path Problems ◽  
Acceleration Techniques ◽  
Periodic Vehicle Routing Problem ◽  
The Impact ◽  
Periodic Vehicle Routing

This paper addresses the periodic vehicle routing problem with time windows (PVRPTW). Therein, customers require one or several visits during a planning horizon of several periods. The possible visiting patterns (schedules) per customer are limited. In the classical PVRPTW, it is common to assume that each customer requires a specific visit frequency and offers all corresponding schedules with regular intervals between the visits. In this paper, we permit all kinds of schedule structures and the choice of the service frequency. We present an exact branch-and-price-and-cut algorithm for the classical PVRPTW and its variant with flexible schedules. The pricing problems are elementary shortest-path problems with resource constraints. They can be based on one of two new types of networks and solved with a labeling algorithm, which uses several known acceleration techniques, such as the [Formula: see text]-path relaxation and dynamic halfway points within bidirectional labeling. For instances in which schedule sets fulfill a certain symmetry condition, we present specialized improvements of the algorithm, such as constraint aggregation and symmetry breaking. Computational tests on benchmark instances for the PVRPTW show the effectiveness of our algorithm. Furthermore, we analyze the impact of different schedule structures on run times and objective function values. The online appendix is available at https://doi.org/10.1287/trsc.2018.0855 .

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