scholarly journals Optimal Solution of Vehicle Routing Problems with Fractional Objective Function

2020 ◽  
Author(s):  
Roberto Baldacci ◽  
Andrew Lim ◽  
Emiliano Traversi ◽  
Roberto Wolfler Calvo
Keyword(s):  
Objective Function ◽  
Vehicle Routing ◽  
Optimal Solution ◽  
Vehicle Routing Problems ◽  
Routing Problems

scholarly journals Bee-Inspired Algorithms Applied to Vehicle Routing Problems: A Survey and a Proposal

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
Thiago A. S. Masutti ◽  
Leandro N. de Castro
Keyword(s):  
Vehicle Routing ◽  
Collective Intelligence ◽  
Travelling Salesman Problem ◽  
Optimal Solution ◽  
Vehicle Routing Problems ◽  
Open Problems ◽  
Chip Design ◽  
Routing Problems ◽  
New Approach ◽  
Np Hard Problem

Vehicle routing problems constitute a class of combinatorial optimization tasks that search for optimal routes (e.g., minimal cost routes) for one or more vehicles to attend a set of nodes (e.g., cities or customers). Finding the optimal solution to vehicle routing tasks is an NP-hard problem, meaning that the size of problems that can be solved by exhaustive search is limited. From a practical perspective, this class of problems has a wide and important set of applications, from the distribution of goods to the integrated chip design. Rooted on the use of collective intelligence, swarm-inspired algorithms, more specifically bee-inspired approaches, have been used with good performance to solve such problems. In this context, the present paper provides a broad review on the use of bee-inspired methods for solving vehicle routing problems, introduces a new approach to solve one of the main tasks in this area (the travelling salesman problem), and describes open problems in the field.

Application Research of Improved Simulated Annealing Algorithm in Vehicle Routing Problems

2011 ◽  
Vol 58-60 ◽  
pp. 1031-1036
Author(s):  
Dong Mei Yan ◽  
Cheng Hua Lu
Keyword(s):  
Simulated Annealing ◽  
Vehicle Routing ◽  
Simulated Annealing Algorithm ◽  
Memory Function ◽  
Optimal Solution ◽  
Vehicle Routing Problems ◽  
Application Research ◽  
Routing Problems ◽  
Annealing Algorithm ◽  
Optimization Efficiency

This paper analyzed the principle and insufficient of traditional simulated annealing algorithm, and on the basis of the traditional simulated annealing algorithm, this paper used improved simulated annealing algorithm to solve vehicle routing problems. The new algorithm increases memory function, and keeps the current best state to avoid losing current optimal solution while reducing the computation times and accelerating the algorithm speed. The experimental results show that, the algorithm can significantly improve the optimization efficiency, and has faster convergence speed than traditional simulated annealing algorithm.

scholarly journals Overview of fuzzy vehicle routing problems

2020 ◽  
Vol 20 (3) ◽  
pp. 325-331
Author(s):  
Yu. O. Chernyshev ◽  
V. N. Kubil ◽  
A. V. Trebukhin
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Fuzzy Variable ◽  
Optimal Solution ◽  
Fuzzy Programming ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Routing Problem ◽  
Fuzzy Variables ◽  
Fundamental Properties

Introduction. Various algorithms for solving fuzzy vehicle routing problems are considered. The work objective was to study modern methods for the optimal solution to fuzzy, random and rough vehicle routing problems. Materials and Methods. The paper reviews fuzzy vehicle routing problems, existing methods and approaches to their solution. The most effective features of some approaches to solving fuzzy vehicle routing problems considering their specificity, are highlighted. Results. The Fuzzy Vehicle Routing Problem (FVRP) occurs whenever the routing data is vague, unclear, or ambiguous. In many cases, these fuzzy elements can better reflect reality. However, it is very difficult to use Vehicle Routing Problem (VRP) solving algorithms to solve FVRP since several fundamental properties of deterministic problems are no longer fulfilled in FVRP. Therefore, it is required to introduce new models and algorithms of fuzzy programming to solve such problems. Thus, the use of methods of the theory of fuzzy sets will provide successful simulation of the problems containing elements of uncertainty and subjectivity. Discussion and conclusions. As a result of reviewing various methods and approaches to solving vehicle routing problems, it is concluded that the development and study of new solutions come into sharp focus of researchers nowadays, but the degree of elaboration of various options varies. Methods for the optimal solution of FVRP are limited, in general, to some single fuzzy variable. There is a very limited number of papers that consider a large number of fuzzy variables.

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