A Two-Phase Genetic and Set Partitioning Approach for the Vehicle Routing Problem with Time Windows

2005 ◽  
Author(s):  
G.B. Alvarenga ◽  
G.R. Mateus
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Set Partitioning ◽  
Two Phase ◽  
Routing Problem

scholarly journals The Robust Vehicle Routing Problem with Time Windows: Compact Formulation and Branch-Price-and-Cut Method

2019 ◽  
Vol 53 (4) ◽  
pp. 1043-1066 ◽  
Author(s):  
Pedro Munari ◽  
Alfredo Moreno ◽  
Jonathan De La Vega ◽  
Douglas Alem ◽  
Jacek Gondzio ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Real Life ◽  
Set Partitioning ◽  
Branch And Cut ◽  
Small Scale ◽  
Routing Problem ◽  
Compact Formulation ◽  
Cut Method

We address the robust vehicle routing problem with time windows (RVRPTW) under customer demand and travel time uncertainties. As presented thus far in the literature, robust counterparts of standard formulations have challenged general-purpose optimization solvers and specialized branch-and-cut methods. Hence, optimal solutions have been reported for small-scale instances only. Additionally, although the most successful methods for solving many variants of vehicle routing problems are based on the column generation technique, the RVRPTW has never been addressed by this type of method. In this paper, we introduce a novel robust counterpart model based on the well-known budgeted uncertainty set, which has advantageous features in comparison with other formulations and presents better overall performance when solved by commercial solvers. This model results from incorporating dynamic programming recursive equations into a standard deterministic formulation and does not require the classical dualization scheme typically used in robust optimization. In addition, we propose a branch-price-and-cut method based on a set partitioning formulation of the problem, which relies on a robust resource-constrained elementary shortest path problem to generate routes that are robust regarding both vehicle capacity and customer time windows. Computational experiments using Solomon’s instances show that the proposed approach is effective and able to obtain robust solutions within a reasonable running time. The results of an extensive Monte Carlo simulation indicate the relevance of obtaining robust routes for a more reliable decision-making process in real-life settings.

scholarly journals Feasible Initial Population with Genetic Diversity for a Population-Based Algorithm Applied to the Vehicle Routing Problem with Time Windows

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Marco Antonio Cruz-Chávez ◽  
Alina Martínez-Oropeza
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Clustering Algorithm ◽  
Time Windows ◽  
Population Based ◽  
Stochastic Algorithm ◽  
Two Phase ◽  
Distance Method ◽  
Routing Problem ◽  
Population Based Algorithm

A stochastic algorithm for obtaining feasible initial populations to the Vehicle Routing Problem with Time Windows is presented. The theoretical formulation for the Vehicle Routing Problem with Time Windows is explained. The proposed method is primarily divided into a clustering algorithm and a two-phase algorithm. The first step is the application of a modifiedk-means clustering algorithm which is proposed in this paper. The two-phase algorithm evaluates a partial solution to transform it into a feasible individual. The two-phase algorithm consists of a hybridization of four kinds of insertions which interact randomly to obtain feasible individuals. It has been proven that different kinds of insertions impact the diversity among individuals in initial populations, which is crucial for population-based algorithm behavior. A modification to the Hamming distance method is applied to the populations generated for the Vehicle Routing Problem with Time Windows to evaluate their diversity. Experimental tests were performed based on the Solomon benchmarking. Experimental results show that the proposed method facilitates generation of highly diverse populations, which vary according to the type and distribution of the instances.

scholarly journals A Two-Phase Distributed Ruin-and-Recreate Genetic Algorithm for Solving the Vehicle Routing Problem With Time Windows

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 169851-169871
Author(s):  
Thau-Soon Khoo ◽  
Babrdel Bonab Mohammad ◽  
Voon-Hee Wong ◽  
Yong-Haur Tay ◽  
Madhavan Nair
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Two Phase ◽  
Routing Problem

scholarly journals A hybrid ant colony algorithm based on multiple strategies for the vehicle routing problem with time windows

2021 ◽  
Author(s):  
Hongguang Wu ◽  
Yuelin Gao ◽  
Wanting Wang ◽  
Ziyu Zhang
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony Algorithm ◽  
Time Window ◽  
Time Windows ◽  
Ant Colony ◽  
Routing Problem ◽  
Multiple Strategies ◽  
Hard Time ◽  
Mutation Operation

AbstractIn this paper, we propose a vehicle routing problem with time windows (TWVRP). In this problem, we consider a hard time constraint that the fleet can only serve customers within a specific time window. To solve this problem, a hybrid ant colony (HACO) algorithm is proposed based on ant colony algorithm and mutation operation. The HACO algorithm proposed has three innovations: the first is to update pheromones with a new method; the second is the introduction of adaptive parameters; and the third is to add the mutation operation. A famous Solomon instance is used to evaluate the performance of the proposed algorithm. Experimental results show that HACO algorithm is effective against solving the problem of vehicle routing with time windows. Besides, the proposed algorithm also has practical implications for vehicle routing problem and the results show that it is applicable and effective in practical problems.

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