Models and Solutions for Truck and Trailer Routing Problems

2013 ◽  
Vol 4 (2) ◽  
pp. 31-43 ◽  
Author(s):  
Isis Torres Pérez ◽  
José Luis Verdegay ◽  
Carlos Cruz Corona ◽  
Alejandro Rosete Suárez
Keyword(s):  
Combinatorial Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem ◽  
Practical Relevance ◽  
Routing Problem ◽  
Considerable Difficulty ◽  
Geographically Dispersed ◽  
Optimal Set

This paper is a survey about of the Truck and Trailer Routing Problem. The Truck and Trailer Routing Problem is an extension of the well-known Vehicle Routing Problem. Defined recently, this problem consists in designing the optimal set of routes for fleet of vehicles (trucks and trailers) in order to serve a given set of geographically dispersed customers. Since TTRP itself is a very difficult combinatorial optimization problem are usually tackled by metaheuristics. The interest in Truck and Trailer Routing Problem is motivated by its practical relevance as well as by its considerable difficulty. The goal of this paper is to show a study on the TTRP and the metaheuristics used for to solve it.

scholarly journals On the Selective Vehicle Routing Problem

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 771 ◽  
Author(s):  
Cosmin Sabo ◽  
Petrică C. Pop ◽  
Andrei Horvat-Marc
Keyword(s):  
Mathematical Model ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Mixed Integer ◽  
The Novel ◽  
Routing Problem ◽  
Proposed Model ◽  
Benchmark Instances ◽  
Number Of Customers ◽  
Optimal Set

The Generalized Vehicle Routing Problem (GVRP) is an extension of the classical Vehicle Routing Problem (VRP), in which we are looking for an optimal set of delivery or collection routes from a given depot to a number of customers divided into predefined, mutually exclusive, and exhaustive clusters, visiting exactly one customer from each cluster and fulfilling the capacity restrictions. This paper deals with a more generic version of the GVRP, introduced recently and called Selective Vehicle Routing Problem (SVRP). This problem generalizes the GVRP in the sense that the customers are divided into clusters, but they may belong to one or more clusters. The aim of this work is to describe a novel mixed integer programming based mathematical model of the SVRP. To validate the consistency of the novel mathematical model, a comparison between the proposed model and the existing models from literature is performed, on the existing benchmark instances for SVRP and on a set of additional benchmark instances used in the case of GVRP and adapted for SVRP. The proposed model showed better results against the existing models.

The Traveling Salesman Problem, the Vehicle Routing Problem, and Their Impact on Combinatorial Optimization

2010 ◽  
Vol 1 (2) ◽  
pp. 82-92 ◽  
Author(s):  
Gilbert Laporte
Keyword(s):  
Combinatorial Optimization ◽  
Vehicle Routing ◽  
Traveling Salesman Problem ◽  
Vehicle Routing Problem ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Traveling Salesman ◽  
Routing Problem ◽  
Combinatorial Optimization Problems ◽  
The Traveling Salesman Problem

The Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP) are two of the most popular problems in the field of combinatorial optimization. Due to the study of these two problems, there has been a significant growth in families of exact and heuristic algorithms being used today. The purpose of this paper is to show how their study has fostered developments of the most popular algorithms now applied to the solution of combinatorial optimization problems. These include exact algorithms, classical heuristics and metaheuristics.

scholarly journals Focusing on the Golden Ball Metaheuristic: An Extended Study on a Wider Set of Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
E. Osaba ◽  
F. Diaz ◽  
R. Carballedo ◽  
E. Onieva ◽  
A. Perallos
Keyword(s):  
Genetic Algorithms ◽  
Combinatorial Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Problems ◽  
Traveling Salesman ◽  
Routing Problem ◽  
Combinatorial Optimization Problems ◽  
Golden Ball ◽  
The One

Nowadays, the development of new metaheuristics for solving optimization problems is a topic of interest in the scientific community. In the literature, a large number of techniques of this kind can be found. Anyway, there are many recently proposed techniques, such as the artificial bee colony and imperialist competitive algorithm. This paper is focused on one recently published technique, the one called Golden Ball (GB). The GB is a multiple-population metaheuristic based on soccer concepts. Although it was designed to solve combinatorial optimization problems, until now, it has only been tested with two simple routing problems: the traveling salesman problem and the capacitated vehicle routing problem. In this paper, the GB is applied to four different combinatorial optimization problems. Two of them are routing problems, which are more complex than the previously used ones: the asymmetric traveling salesman problem and the vehicle routing problem with backhauls. Additionally, one constraint satisfaction problem (the n-queen problem) and one combinatorial design problem (the one-dimensional bin packing problem) have also been used. The outcomes obtained by GB are compared with the ones got by two different genetic algorithms and two distributed genetic algorithms. Additionally, two statistical tests are conducted to compare these results.

scholarly journals Solving Rich Vehicle Routing Problem Using Three Steps Heuristic

2019 ◽  
Vol 1 (1) ◽  
pp. 1-19
Author(s):  
Editor
Keyword(s):  
Vehicle Routing ◽  
Public Transportation ◽  
Vehicle Routing Problem ◽  
Optimization Problem ◽  
Maximum Load ◽  
Load Factor ◽  
Routing Problem ◽  
The Government ◽  
Hard And Soft Constraints

Vehicle Routing Problem (VRP) relates to the problem of providing optimum service with a fleet of vehicles to customers. It is a combinatorial optimization problem. The objective is usually to maximize the profit of the operation. However, for public transportation owned and operated by the government, accessibility takes priority over profitability. Accessibility usually reduces profit, while increasing profit tends to reduce accessibility. In this research, we look at how accessibility can be increased without penalizing the profitability. This requires the determination of routes with minimum fuel consumption, the maximum number of ports of call and maximum load factor satisfying a number of pre-determined constraints: hard and soft constraints. To solve this problem, we propose a heuristic algorithm. The results from this experiment show that the algorithm proposed has better performance compared to the partitioning set.

scholarly journals Simultaneous Multi-Start Simulated Annealing for Capacitated Vehicle Routing Problem

2020 ◽  
Vol 8 ◽  
Author(s):  
Arman Davtyan ◽  
Suren Khachatryan
Keyword(s):  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Superior Performance ◽  
Metaheuristic Algorithm ◽  
Optimal Solutions ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Capacitated Vehicle

A new metaheuristic algorithm is proposed for Capacitated Vehicle Routing Problem. CVRP is one of the fundamental problems in combinatorial optimization that deals with transport route minimization. The algorithm combines Simulated Annealing, multi-start and simultaneous computing techniques. A series of computational tests are conducted on several CVRP benchmarks and near-optimal solutions are obtained. The results indicate superior performance compared with Simulated Annealing

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.

Intervention Planning in ITIL Context

2021 ◽  
Vol 12 (4) ◽  
pp. 85-109
Author(s):  
Mounir Ketata ◽  
Zied Loukil ◽  
Faiez Gargouri
Keyword(s):  
Combinatorial Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Service Level ◽  
Incident Management ◽  
Planning Problem ◽  
Service Level Agreements ◽  
Routing Problem ◽  
Priority Order ◽  
Intervention Planning

In incident management and especially in after-sales services, customer interventions must be planned according to a priority order set by service level agreements as well as the availability of both technicians and clients. Despite the availability of incident management software solutions, intervention planning is still performed manually in most solutions because numerous constraints must be considered such as the synchronization of technician skills and customer requests, their availability, and the customer priorities. The intervention planning problem is considered as a difficult combinatorial optimization issue. Various approaches have been proposed in the literature including the transformation of this problem into a vehicle routing problem (VRP) or into a CSP in the context of ITIL framework. Yet, the resolution of this problem with a classical CSP solver is time consuming and must be optimized by proposing filtering rules or specific heuristics. This paper proposes the improved CSP and COP models for intervention planning problem with implementing filtering rules and techniques.

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.

A Methodology and Modelling Technique for Taxi Path Optimization

2013 ◽  
Vol 711 ◽  
pp. 816-821
Author(s):  
Zhi Ping Hou ◽  
Feng Jin ◽  
Qin Jian Yuan ◽  
Yong Yi Li
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Route Optimization ◽  
Routing Problem ◽  
Control Center ◽  
Material Resources ◽  
New Type

Vehicle Routing Problem (VRP) is a typical combinatorial optimization problem. A new type of bionic algorithm-ant colony algorithm is very appropriate to solve Vehicle Routing Problem because of its positive feedback, robustness, parallel computing and collaboration features. In view of the taxi route optimization problem, this article raised the issue of the control of the taxi, by using the Geographic Information System (GIS), through the establishment of the SMS platform and reasonable taxi dispatch control center, combining ant colony algorithm to find the most nearest no-load taxi from the passenger, and giving the no-load taxi the best path to the passenger. Finally this paper use Ant Colony laboratory to give the simulation. By using this way of control, taxis can avoid the no-load problem effectively, so that the human and material resources can also achieve savings.

An Effective Tabu Search for Vehicle Routing Problem with Pickup and Delivery Service

2011 ◽  
Vol 181-182 ◽  
pp. 760-764
Author(s):  
Yun Yao Li ◽  
Chang Shi Liu
Keyword(s):  
Tabu Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Computing Time ◽  
Bench Mark ◽  
Pickup And Delivery ◽  
Data Sets ◽  
Routing Problem ◽  
Optimal Set ◽  
Reasonable Computing

The vehicle routing problem with delivery and pick-up service was considered in this paper. A tabu search was proposed to determine the optimal set of routes to totally satisfy both the delivery and pick-up demand. Performances are compared with other heuristics appeared in the literature recently by the bench-mark data sets. The computational results show that the proposed approaches produce high quality results within a reasonable computing time.

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