Solving a Real World Routing Problem Using Multiple Evolutionary Agents

2002 ◽  
pp. 871-880 ◽  
Author(s):  
Neil Urquhart ◽  
Peter Ross ◽  
Ben Paechter ◽  
Ken Chisholm
Keyword(s):  
Real World ◽  
Routing Problem

scholarly journals New Instances for Maximum Weight Independent Set From a Vehicle Routing Application

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Yuanyuan Dong ◽  
Andrew V. Goldberg ◽  
Alexander Noe ◽  
Nikos Parotsidis ◽  
Mauricio G. C. Resende ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Real World ◽  
Vehicle Routing Problem ◽  
Independent Set ◽  
Maximum Weight ◽  
Routing Problem ◽  
Independent Set Problem ◽  
Maximum Weight Independent Set ◽  
Large Size

AbstractWe present a set of new instances of the maximum weight independent set problem. These instances are derived from a real-world vehicle routing problem and are challenging to solve in part because of their large size. We present instances with up to 881 thousand nodes and 383 million edges.

scholarly journals Solving Practical Vehicle Routing Problem with Time Windows Using Metaheuristic Algorithms

1970 ◽  
Vol 24 (4) ◽  
pp. 343-351 ◽  
Author(s):  
Filip Taner ◽  
Ante Galić ◽  
Tonči Carić
Keyword(s):  
Vehicle Routing ◽  
Real World ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Iterated Local Search ◽  
Metaheuristic Algorithms ◽  
Benchmark Problems ◽  
Routing Problem ◽  
Constructive Heuristics ◽  
Initial Feasible Solution

This paper addresses the Vehicle Routing Problem with Time Windows (VRPTW) and shows that implementing algorithms for solving various instances of VRPs can significantly reduce transportation costs that occur during the delivery process. Two metaheuristic algorithms were developed for solving VRPTW: Simulated Annealing and Iterated Local Search. Both algorithms generate initial feasible solution using constructive heuristics and use operators and various strategies for an iterative improvement. The algorithms were tested on Solomon’s benchmark problems and real world vehicle routing problems with time windows. In total, 44 real world problems were optimized in the case study using described algorithms. Obtained results showed that the same distribution task can be accomplished with savings up to 40% in the total travelled distance and that manually constructed routes are very ineffective.

scholarly journals POPMUSIC for a real-world large-scale vehicle routing problem with time windows

2009 ◽  
Vol 60 (7) ◽  
pp. 934-943 ◽  
Author(s):  
A Ostertag ◽  
K F Doerner ◽  
R F Hartl ◽  
E D Taillard ◽  
P Waelti
Keyword(s):  
Vehicle Routing ◽  
Real World ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Time Windows ◽  
Routing Problem

A multi-start algorithm for a balanced real-world Open Vehicle Routing Problem

2014 ◽  
Vol 238 (1) ◽  
pp. 104-113 ◽  
Author(s):  
A.D. López-Sánchez ◽  
A.G. Hernández-Díaz ◽  
D. Vigo ◽  
R. Caballero ◽  
J. Molina
Keyword(s):  
Vehicle Routing ◽  
Real World ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
Open Vehicle Routing Problem ◽  
Open Vehicle Routing

Use of GVRP as a Model of Two Specific Real World Problems and Its Bioinspired Solution

2016 ◽  
pp. 451-469
Author(s):  
Jorge Rodas ◽  
Daniel Azpeitia ◽  
Alberto Ochoa-Zezzatti ◽  
Raymundo Camarena ◽  
Tania Olivier
Keyword(s):  
Vehicle Routing ◽  
Real World ◽  
Vehicle Routing Problem ◽  
Combinatorial Optimization Problem ◽  
Exact Methods ◽  
Routing Problem ◽  
Computing Technique ◽  
Product Delivery ◽  
Soft Computing Technique ◽  
Generalized Vehicle Routing Problem

The aim of this chapter is about the inclusion of real world scenarios, viewed as a Generalized Vehicle Routing Problem (GVRP) model problem, and treated by bio inspired algorithms in order to find optimum routing of product delivery. GVRP is the generalization of the classical Vehicle Routing Problem (VRP) that is well known NP-hard as generalized combinatorial optimization problem with a number of real world applications and a variety of different versions. Due to its complexity, large instances of VRP are hard to solve using exact methods. Thus a solution by a soft computing technique is desired. From a methodological standpoint, the chapter includes four bio inspired algorithms, ant colony optimization and firefly. From an application standpoint, several factors of the generalized vehicle routing are considered from a real world scenario.

A Soft Computing-Based Idea Applied to the Truck and Trailer Routing Problem

2014 ◽  
pp. 245-259
Author(s):  
Isis Torres Pérez ◽  
Alejandro Rosete Suárez ◽  
Carlos Cruz-Corona ◽  
José L. Verdegay
Keyword(s):  
Combinatorial Optimization ◽  
Vehicle Routing ◽  
Soft Computing ◽  
Real World ◽  
Combinatorial Optimization Problem ◽  
Decision Makers ◽  
Linguistic Information ◽  
Routing Problem ◽  
Problem Model ◽  
Subjective Knowledge

Techniques based on Soft Computing are useful to model and solve real-world problems where decision makers use subjective knowledge or linguistic information when making decisions, measuring parameters, objectives, and constraints, and even when modeling the problem. In many problems in transport and logistics, it is necessary to take into account that the available knowledge about some data and parameters of the problem model is imprecise or uncertain. Truck and Trailer Routing Problem, TTRP, is one of most recent and interesting problems in transport routing planning. TTRP is a combinatorial optimization problem, and it is computationally more difficult to solve than the known Vehicle Routing Problem, VRP. Most of models used in the literature assume that the data available is accurate; but this consideration does not correspond with reality. For this reason, it is appropriate to focus research toward defining TTRP models for incorporating the uncertainty present in their data. The aims of the present chapter are: a) to provide a study on the Truck and Trailer Routing Problem that serves as help to researchers interested on this topic and b) to present an approach using techniques of Soft Computing to solve this problem.

scholarly journals Using Congestion Zones for Solving the Time Dependent Vehicle Routing Problem

2020 ◽  
Vol 32 (1) ◽  
pp. 25-38 ◽  
Author(s):  
Tonči Carić ◽  
Juraj Fosin
Keyword(s):  
Vehicle Routing ◽  
Real World ◽  
Vehicle Routing Problem ◽  
Time Delays ◽  
Historical Data ◽  
Time Dependent ◽  
Routing Problem ◽  
Real World Problem ◽  
Independent Case ◽  
Real World Problems

This paper provides a framework for solving the Time Dependent Vehicle Routing Problem (TDVRP) by using historical data. The data are used to predict travel times during certain times of the day and derive zones of congestion that can be used by optimization algorithms. A combination of well-known algorithms was adapted to the time dependent setting and used to solve the real-world problems. The adapted algorithm outperforms the best-known results for TDVRP benchmarks. The proposed framework was applied to a real-world problem and results show a reduction in time delays in serving customers compared to the time independent case.

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 Multi-Robot Routing Problem with Min–Max Objective

Robotics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 122
Author(s):  
Jennifer David ◽  
Thorsteinn Rögnvaldsson
Keyword(s):  
Simulated Annealing ◽  
Comparative Study ◽  
Bipartite Graph ◽  
Real World ◽  
Heuristic Method ◽  
Problem Formulation ◽  
Permutation Matrix ◽  
Routing Problem ◽  
Research Communities ◽  
Multi Robot

In this paper, we study the “Multi-Robot Routing problem” with min–max objective (MRR-MM) in detail. It involves the assignment of sequentially ordered tasks to robots such that the maximum cost of the slowest robot is minimized. The problem description, the different types of formulations, and the methods used across various research communities are discussed in this paper. We propose a new problem formulation by treating this problem as a bipartite graph with a permutation matrix to solve it. A comparative study is done between three methods: Stochastic simulated annealing, deterministic mean-field annealing, and a heuristic-based graph search method. Each method is investigated in detail with several data sets (simulation and real-world), and the results are analysed and compared with respect to scalability, computational complexity, optimality, and its application to real-world scenarios. The paper shows that the heuristic method produces results very quickly with good scalability. However, the solution quality is sub-optimal. On the other hand, when optimal or near-optimal results are required with considerable computational resources, the simulated annealing method proves to be more efficient. However, the results show that the optimal choice of algorithm depends on the dataset size and the available computational budget. The contribution of the paper is three-fold: We study the MRR-MM problem in detail across various research communities. This study also shows the lack of inter-research terminology that has led to different names for the same problem. Secondly, formulating the task allocation problem as a permutation matrix formulation (bipartite graph) has opened up new approaches to solve this problem. Thirdly, we applied our problem formulation to three different methods and conducted a detailed comparative study using real-world and simulation data.

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