Hybrid approach for the Multiple Vehicle Routing Problem with Deliveries and Selective Pickups

2012 ◽  
Author(s):  
Bruno Petrato Bruck ◽  
Andre Gustavo dos Santos
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Approach ◽  
Routing Problem

scholarly journals Hybrid Heuristic Algorithm for solving Capacitated Vehicle Routing problem

2014 ◽  
Vol 12 (9) ◽  
pp. 3844-3851 ◽  
Author(s):  
Moh. M. AbdElAziz ◽  
Haitham A. El-Ghareeb ◽  
M. S. M. Ksasy
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Nearest Neighbor ◽  
Hybrid Approach ◽  
Minimum Cost ◽  
Second Phase ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Vehicle Capacity ◽  
Capacitated Vehicle

The Capacitated Vehicle Routing Problem is the most common and basic variant of the vehicle routing problem, where it represents an important problem in the fields of transportation, distribution and logistics. It involves finding a set of optimal routes that achieve the minimum cost and serve scattered customer locations under several constraints such as the distance between customers’ locations, available vehicles, vehicle capacity and customer demands. The Cluster first – Route second is the proposed approach used to solve capacitated vehicle routing problem which applied in a real case study used in that research, it consists of two main phases. In the first phase, the objective is to group the closest geographical customer locations together into clusters based on their locations, vehicle capacity and demands by using Sweep algorithm. In the second phase, the objective is to generate the minimum cost route for each cluster by using the Nearest Neighbor algorithm. The hybrid approach is evaluated by Augerat’s Euclidean benchmark datasets.

A Hybrid of Sine Cosine and Particle Swarm Optimization (HSPS) for Solving Heterogeneous Fixed Fleet Vehicle Routing Problem

2021 ◽  
Vol 12 (1) ◽  
pp. 41-65
Author(s):  
Sandhya Bansal ◽  
Savita Wadhawan
Keyword(s):  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Approach ◽  
Real Life ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Heterogeneous Vehicle Routing Problem ◽  
Sine Cosine Algorithm

Heterogeneous fixed fleet vehicle routing problem is a real-life variant of classical VRP, which is a well-established NP-hard optimization problem. In this paper, a hybrid approach based on sine cosine algorithm and particle swarm optimization, namely HSPS, is proposed to solve heterogeneous vehicle routing problem. This hybridization incorporates the strength of both the algorithms for solving this variant. It works in two stages. In first stage, sine cosine algorithm is used to examine the unexplored solution space, and then in next stage, particle swarm optimization is used to exploit the search space. The proposed algorithm has been tested and compared with other algorithms on several benchmark instances. The numerical and statistical results demonstrate that the proposed hybrid is competitive with other existing hybrid algorithms in solving benchmarks with faster convergence rate.

Optimization of capacitated vehicle routing problem with alternative delivery, pick-up and time windows: A modified hybrid approach

2021 ◽  
Vol 423 ◽  
pp. 670-678 ◽  
Author(s):  
Paweł Sitek ◽  
Jarosław Wikarek ◽  
Katarzyna Rutczyńska-Wdowiak ◽  
Grzegorz Bocewicz ◽  
Zbigniew Banaszak
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Hybrid Approach ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Alternative Delivery ◽  
Capacitated Vehicle

A Hybrid Approach Based on LP Metric Method and Genetic Algorithm for the Vehicle-Routing Problem with Time Windows, Driver-Specific Times, and Vehicles-Specific Capacities

2018 ◽  
Vol 9 (4) ◽  
pp. 51-67 ◽  
Author(s):  
Ebrahim Asadi-Gangraj ◽  
Sina Nayeri
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Hybrid Approach ◽  
Mixed Integer ◽  
Computational Time ◽  
Routing Problem ◽  
Lp Metric

Due to increasing population, increasing number of vehicles as well as environmental pollution, planning vehicles efficiently one of important problems nowadays. This article proposes a Multi-Objective Mixed Integer Programming (MOMIP) model for the vehicle-routing problem with time windows, driver-specific times and vehicles-specific capacities (VRPTDV), a variant of the classical VRPT that uses driver-specific travel and service times and vehicles-specific capacity to model the familiarity of the different drivers with the customers to visit. The first objective function aims to minimize traveled distance and the second objective function minimizing working duration. Since the problem is NP-hard, optimal solution for the instances of realistic size cannot be obtained within a reasonable amount of computational time using exact solution approaches. Hence, the hybrid approach based on LP metric method and genetic algorithm is proposed to solve the given problem.

scholarly journals Solution of capacitated vehicle routing problem with invasive weed and hybrid algorithms

2021 ◽  
Vol 12 (4) ◽  
pp. 441-456 ◽  
Author(s):  
Ümit Yıldırım ◽  
Yusuf Kuvvetli
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Approach ◽  
Group Problem ◽  
Invasive Weed ◽  
Data Set ◽  
Routing Problem ◽  
Metaheuristic Methods ◽  
Hybrid Solutions ◽  
Problem Instances

The vehicle routing problem is widespread in terms of optimization, which is known as being NP-Hard. In this study, the vehicle routing problem with capacity constraints is solved using cost- and time-efficient metaheuristic methods: an invasive weed optimization algorithm, genetic algorithm, savings algorithm, and hybridized variants. These algorithms are tested using known problem sets in the literature. Twenty-four instances evaluate the performance of algorithms from P and five instances from the CMT data set group. The invasive weed algorithm and its hybrid variant with savings and genetic algorithms are used to determine the best methodology regarding time and cost values. The proposed hybrid approach has found optimal P group problem instances with a 2% difference from the best-known solution on average. Similarly, the CMT group problem is solved with about a 10% difference from the best-known solution on average. That the proposed hybrid solutions have a standard deviation of less than 2% on average from BKS indicates that these approaches are consistent.

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