Special Issue "Metaheuristics for Rich Vehicle Routing Problems"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (28 February 2018)

Special Issue Editors

Guest Editor
Prof. Dr. Christian Prins

ICD-LOSI (UMR CNRS 6281), Université de Technologie de Troyes (UTT), 12 rue Marie Curie, CS 42060, 10004 Troyes Cedex, France
Website | E-Mail
Interests: vehicle routing problems; supply chain modeling; metaheuristics
Guest Editor
Dr. Nacima Labadie

ICD-LOSI (UMR CNRS 6281), Université de Technologie de Troyes (UTT), 12 rue Marie Curie, CS 42060, 10004 Troyes Cedex, France
Website | E-Mail
Interests: transportation; vehicle routing problems; supply chain management; metaheuristic design

Special Issue Information

Dear Colleagues,

Vehicle routing problems (VRPs) represent a rapidly growing research domain in operations research and combinatorial optimization, aiming to develop mathematical models and efficient solution methods. As underlined by Laporte (2009), VRPs are nice laboratory-problems to test new ideas in algorithmics and optimization. Recent trends try to tackle rich VRPs, i.e., problems including many real-word constraints and requiring metaheuristics to solve the large instances met in practice.

An upcoming Special Issue of the journal Algorithms is dedicated to recent advances on metaheuristic design and implementation for rich and/or large scale vehicle routing problems. Interested authors are invited to submit original work to this Special Issue coordinated by Prof. Christian Prins and Dr. Nacima Labadie.

The topics include, but are not limited to:

  • - Trajectory-based, evolutionary algorithms and multi-agent metaheuristics for rich VRPS
  • -  Hybrid metaheuristics and innovative metaheuristic structures for rich VRPs
  • -  Genericity aspects in developing heuristic approaches for VRPs
  • - Algorithmic techniques to reduce running time and tackle very large VRP instances.
  • -  Case studies including the development of ad hoc algorithms for large instances.

Please submit your paper via the on-line submission system and feel free to contact us if you have any questions about this Special Issue.

Prof. Dr. Christian Prins
Dr. Nacima Labadie
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 850 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Vehicle Routing Problems (VRPs)
  • Metaheuristics
  • Hybrid metaheuristics

Published Papers (4 papers)

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Research

Open AccessArticle Using Metaheuristics on the Multi-Depot Vehicle Routing Problem with Modified Optimization Criterion
Algorithms 2018, 11(5), 74; https://doi.org/10.3390/a11050074
Received: 8 March 2018 / Revised: 16 May 2018 / Accepted: 17 May 2018 / Published: 18 May 2018
Cited by 1 | PDF Full-text (2663 KB) | HTML Full-text | XML Full-text
Abstract
This article deals with the modified Multi-Depot Vehicle Routing Problem (MDVRP). The modification consists of altering the optimization criterion. The optimization criterion of the standard MDVRP is to minimize the total sum of routes of all vehicles, whereas the criterion of modified MDVRP
[...] Read more.
This article deals with the modified Multi-Depot Vehicle Routing Problem (MDVRP). The modification consists of altering the optimization criterion. The optimization criterion of the standard MDVRP is to minimize the total sum of routes of all vehicles, whereas the criterion of modified MDVRP (M-MDVRP) is to minimize the longest route of all vehicles, i.e., the time to conduct the routing operation is as short as possible. For this problem, a metaheuristic algorithm—based on the Ant Colony Optimization (ACO) theory and developed by the author for solving the classic MDVRP instances—has been modified and adapted for M-MDVRP. In this article, an additional deterministic optimization process which further enhances the original ACO algorithm has been proposed. For evaluation of results, Cordeau’s benchmark instances are used. Full article
(This article belongs to the Special Issue Metaheuristics for Rich Vehicle Routing Problems)
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Open AccessArticle A Multi-Stage Algorithm for a Capacitated Vehicle Routing Problem with Time Constraints
Algorithms 2018, 11(5), 69; https://doi.org/10.3390/a11050069
Received: 28 February 2018 / Revised: 16 April 2018 / Accepted: 16 April 2018 / Published: 10 May 2018
PDF Full-text (1556 KB) | HTML Full-text | XML Full-text
Abstract
The Vehicle Routing Problem (VRP) is one of the most optimized tasks studied and it is implemented in a huge variety of industrial applications. The objective is to design a set of minimum cost paths for each vehicle in order to serve a
[...] Read more.
The Vehicle Routing Problem (VRP) is one of the most optimized tasks studied and it is implemented in a huge variety of industrial applications. The objective is to design a set of minimum cost paths for each vehicle in order to serve a given set of customers. Our attention is focused on a variant of VRP, the capacitated vehicle routing problem when applied to natural gas distribution networks. Managing natural gas distribution networks includes facing a variety of decisions ranging from human resources and material resources to facilities, infrastructures, and carriers. Despite the numerous papers available on vehicle routing problem, there are only a few that study and analyze the problems occurring in capillary distribution operations such as those found in a metropolitan area. Therefore, this work introduces a new algorithm based on the Saving Algorithm heuristic approach which aims to solve a Capacitated Vehicle Routing Problem with time and distance constraints. This joint algorithm minimizes the transportation costs and maximizes the workload according to customer demand within the constraints of a time window. Results from a real case study in a natural gas distribution network demonstrates the effectiveness of the approach. Full article
(This article belongs to the Special Issue Metaheuristics for Rich Vehicle Routing Problems)
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Open AccessArticle Safe Path Planning of Mobile Robot Based on Improved A* Algorithm in Complex Terrains
Algorithms 2018, 11(4), 44; https://doi.org/10.3390/a11040044
Received: 24 January 2018 / Revised: 21 March 2018 / Accepted: 5 April 2018 / Published: 9 April 2018
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Abstract
The A* algorithm has been widely investigated and applied in path planning problems, but it does not fully consider the safety and smoothness of the path. Therefore, an improved A* algorithm is presented in this paper. Firstly, a new environment modeling method is
[...] Read more.
The A* algorithm has been widely investigated and applied in path planning problems, but it does not fully consider the safety and smoothness of the path. Therefore, an improved A* algorithm is presented in this paper. Firstly, a new environment modeling method is proposed in which the evaluation function of A* algorithm is improved by taking the safety cost into account. This results in a safer path which can stay farther away from obstacles. Then a new path smoothing method is proposed, which introduces a path evaluation mechanism into the smoothing process. This method is then applied to smoothing the path without safety reduction. Secondly, with respect to path planning problems in complex terrains, a complex terrain environment model is established in which the distance and safety cost of the evaluation function of the A* algorithm are converted into time cost. This results in a unification of units as well as a clarity in their physical meanings. The simulation results show that the improved A* algorithm can greatly improve the safety and smoothness of the planned path and the movement time of the robot in complex terrain is greatly reduced. Full article
(This article belongs to the Special Issue Metaheuristics for Rich Vehicle Routing Problems)
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Open AccessArticle Bilayer Local Search Enhanced Particle Swarm Optimization for the Capacitated Vehicle Routing Problem
Algorithms 2018, 11(3), 31; https://doi.org/10.3390/a11030031
Received: 29 January 2018 / Revised: 7 March 2018 / Accepted: 13 March 2018 / Published: 15 March 2018
Cited by 1 | PDF Full-text (2453 KB) | HTML Full-text | XML Full-text
Abstract
The classical capacitated vehicle routing problem (CVRP) is a very popular combinatorial optimization problem in the field of logistics and supply chain management. Although CVRP has drawn interests of many researchers, no standard way has been established yet to obtain best known solutions
[...] Read more.
The classical capacitated vehicle routing problem (CVRP) is a very popular combinatorial optimization problem in the field of logistics and supply chain management. Although CVRP has drawn interests of many researchers, no standard way has been established yet to obtain best known solutions for all the different problem sets. We propose an efficient algorithm Bilayer Local Search-based Particle Swarm Optimization (BLS-PSO) along with a novel decoding method to solve CVRP. Decoding method is important to relate the encoded particle position to a feasible CVRP solution. In bilayer local search, one layer of local search is for the whole population in any iteration whereas another one is applied only on the pool of the best particles generated in different generations. Such searching strategies help the BLS-PSO to perform better than the existing proposals by obtaining best known solutions for most of the existing benchmark problems within very reasonable computational time. Computational results also show that the performance achieved by the proposed algorithm outperforms other PSO-based approaches. Full article
(This article belongs to the Special Issue Metaheuristics for Rich Vehicle Routing Problems)
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