An optimization algorithm for solving the rich vehicle routing problem based on Variable Neighborhood Search and Tabu Search metaheuristics

Sicilia, Juan Antonio; Quemada, Carlos; Royo, Beatriz; Escuín, David

doi:10.1016/j.cam.2015.03.050

Cited by 62 publications

(21 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…J.A. Sicilia et al [39] present an optimization algorithm that consists of metaheuristic processes to solve the problem of the capillary distribution of goods in major urban areas taking into consideration: time windows, capacity constraints, compatibility between orders and vehicles, etc. In [40], A. Fraile et al propose a decision model that allows, through a Geographic Information System to determine in an urban setting, the possible optimal locations of various facilities that would make up a new use for the transport infrastructure or logistic sector.…”

Section: Editorial / Journal Of Computational and Applied Mathematics (mentioning

confidence: 99%

Mathematical modeling and computational methods

Torregrosa

Jódar

López

et al. 2016

Journal of Computational and Applied Mathematics

View full text Add to dashboard Cite

Section: Editorial / Journal Of Computational and Applied Mathematics (mentioning

confidence: 99%

Mathematical modeling and computational methods

Torregrosa

Jódar

López

et al. 2016

Journal of Computational and Applied Mathematics

View full text Add to dashboard Cite

“…The metaheuristic based on time constraints has proved to be an efficient decision support tool for assignment problem solving. For example, in [42] the authors presented an optimization algorithm that solved a Rich Vehicle Routing Problem (RVRP) and arose from a research project carried out for an important Spanish distribution company.…”

Section: Related Workmentioning

confidence: 99%

A Temporal Distributed Group Decision Support System Based on Multi-Criteria Analysis

Khiat¹,

Djamila²

2019

IJIMAI

View full text Add to dashboard Cite

T HE field of decision support has been the subject of research conducted by several scientists of various fields. Therefore, it has constituted an attractive pole for different studies and applications. Among these applications, we find several cases which are related to problems with spatial reference: urban and regional planning, transport, management of water resources, environmental management, evaluation of the territory and location of industrial activities, etc. The problems which are related to the area of spatial decision support focus on the selection of geographical sites [14] [4] [37]. This latter is based on the choice between several sets of physical criteria [12] [13].Moreover, decision support systems aim to help decision makers in their tasks by providing them with all the relevant elements for decision-making and spatial planning. In fact, territorial decision support involves several conflicting criteria, whose importance are not the same. The territorial decision support involves several decisionmakers and institutions, which generally have divergent preferences and objectives where various points of view must be taken into account for a final decision [23]. The decision appears as a compromise between several interests and divergent points of view that imply the use of a negotiation strategy between the various involved actors who must take decisions as quickly as possible by taking into account a functioning constrained time. Consequently, this permits to obtain an answer to an interrogation before a given deadline in order to allow decision makers to act as quickly and appropriately as possible.Territory planning (TP) is based on a prospective and strategic vision that takes into account the potential, physical, social, economic, and environmental constraints of the concerned territory [14]. This type of problem involves several decision makers (persons and institutions) with different interests who have generally divergent preferences and objectives and whose different points of view must be taken into account for public decision.The realization of spatial localization in TP relies on mathematical methods and computer tools such as geographical information systems (GIS). The application of GIS is oriented to several areas: urban development [39], environmental management [10], the territory evaluation [12], industrial diagnosis [41] etc.More particularly, spatial decision support systems are interesting, especially in the development of a model of group decision support which is dedicated to the problems of space localization in TP: the problem which entails the search for a surface on a geographical map satisfying a set of criteria and finding a compromise between several interests that appeal to the expertise of several people, particularly those which are concerned by the decision.However, the group of decision-makers will be modeled by a set of entities called computer agents. These agents represent each decisionmaker in a multi-agent system (MAS) [11] [27].The problem addressed in th...

show abstract

“…The proposed integrated hybrid genetic search metaheuristic relies on problem-independent integrated local search, genetic operators, and advanced variety of management methods. Sicilia et al [41] presented a novel optimization algorithm that consists of metaheuristic processes to solve the problem of the capillary distribution of goods in major urban areas while taking into account the features encountered in real life: time windows, capacity constraints, maximum number of orders per vehicle, compatibility between orders and vehicles, orders depending on the pickup and delivery, and not returning to the depot. Vehicle routing problem with attributes, such as multiple depots, time windows, deliveries to plants, and heterogeneous eets of vehicles, was considered by Dayarian et al [42].…”

Section: Literature Of Multi-attribute Vehicle Routing Problemmentioning

confidence: 99%

The combination of TOPSIS method and Dijkstra’s algorithm in multi-attribute routing

Roghanian

Kebria

2017

Scientia Iranica

View full text Add to dashboard Cite

Abstract. This paper introduces a new method called multi-attribute Dijkstra that is an extension of Dijkstra used to determine the shortest path between two points of a graph while arcs between the points, in addition to the distance, have other attributes such as time (distance), cost, emissions, risk, etc. Technique for order preferences by similarity to the ideal (TOPSIS) method is used for ranking and selecting the routes which is a method for solving Multi-Attribute Decision-Making problems (MADM). In this regard, we try to choose appropriate weights for the attributes to consider the right decision for creating a balance between the e ective elements in route selection. In this paper, the algorithm of Dijkstra and TOPSIS will be reviewed, and the proposed method obtained by the combination of these two will also be described. Finally, three examples with di erent conditions are presented to represent the performance of the model. Then, these examples are compared with single-attribute Dijkstra to realize the e ectiveness of the proposed method. Obviously, in solving large-scale examples, the approach is based on coding in appropriate software.

show abstract

An optimization algorithm for solving the rich vehicle routing problem based on Variable Neighborhood Search and Tabu Search metaheuristics

Cited by 62 publications

References 22 publications

Mathematical modeling and computational methods

Mathematical modeling and computational methods

A Temporal Distributed Group Decision Support System Based on Multi-Criteria Analysis

The combination of TOPSIS method and Dijkstra’s algorithm in multi-attribute routing

Contact Info

Product

Resources

About