Group Genetic Algorithm for Heterogeneous Vehicle Routing

2014 ◽  
pp. 161-180
Author(s):  
Michael Mutingi
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Group Structure ◽  
Computation Time ◽  
Variable Cost ◽  
Benchmark Problems ◽  
Fixed Cost ◽  
Routing Problem ◽  
Heterogeneous Vehicle Routing Problem ◽  
Cost Efficient

Cost-efficient transportation is a central concern in the transportation and logistics industry. In particular, the Heterogeneous Vehicle Routing Problem (HVRP) has become a major optimization problem in supply chains involved with delivery (collection) of goods to (from) customers. In this problem, there are limited vehicles of different types with respect to capacity, fixed cost, and variable cost. The solution to this problem involves assigning customers to existing vehicles and, in relation to each vehicle, defining the order of visiting each customer for the delivery or collection of goods. Hence, the objective is to minimize the total costs, while satisfying customer requirements and visiting each customer exactly once. In this chapter, an enhanced Group Genetic Algorithm (GGA) based on the group structure of the problem is developed and tested on several benchmark problems. Computational results show that the proposed GGA algorithm is able to produce high quality solutions within a reasonable computation time.

scholarly journals A Modified Column Generation to Solve the Heterogeneous Fixed Fleet Open Vehicle Routing Problem

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Majid Yousefikhoshbakht ◽  
Azam Dolatnejad ◽  
Farzad Didehvar ◽  
Farhad Rahmati
Keyword(s):  
Vehicle Routing ◽  
Column Generation ◽  
Vehicle Routing Problem ◽  
Computation Time ◽  
Variable Cost ◽  
Fixed Cost ◽  
Ant System ◽  
Routing Problem ◽  
Open Vehicle Routing Problem ◽  
Open Vehicle Routing

In the heterogeneous fixed fleet open vehicle routing problem (HFFOVRP), several different types of vehicles can be used to service the customers. The types of vehicles are different in terms of capacity, fixed cost, and variable cost. In this problem, the vehicles are not required to return to the depot after completing a service and the number of vehicles of each type is fixed and limited. Since this problem belongs to NP-hard problems, in this paper a compound heuristic algorithm called SISEC which includes sweep algorithm, insert, swap, and 2-opt moves, modified elite ant system (EAS), and column generation (CG) is applied to solve the HFFOVRP. We report computational results on 22 problems and solve each problem by using our SISEC. The results which were compared to the results of exact algorithms and the classic CG confirm that the proposed algorithm produces high quality solutions within an acceptable computation time.

A hybrid genetic algorithm for multi-depot vehicle routing problem with considering time window repair and pick-up

2018 ◽  
Vol 13 (3) ◽  
pp. 698-717 ◽  
Author(s):  
Masoud Rabbani ◽  
Pooya Pourreza ◽  
Hamed Farrokhi-Asl ◽  
Narjes Nouri
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Heuristic Algorithms ◽  
Time Window ◽  
Hybrid Genetic Algorithm ◽  
Variable Cost ◽  
Routing Problem ◽  
Content Type ◽  
Simple Genetic Algorithm

Purpose This paper, considers the multi-depot vehicle routing problem with time window considering two repair and pickup vehicles (CMDVRPTW). Design/methodology/approach The objective of this problem is minimization of the total traveling cost and the time window violations. Two meta-heuristic algorithms, namely, simple genetic algorithm (GA) and hybrid genetic algorithm (HGA) are used to find the best solution for this problem. A comparison on the results of these two algorithms has been done and based on the outcome, it has been proved that HGA has better performance than GA. Findings A comparison on the results of these two algorithms has been done and based on the outcome, it has been proved that HGA has better performance than GA. Originality/value This paper, considers the multi-depot vehicle routing problem with time window considering two repair and pickup vehicles (CMDVRPTW). The defined problem is a practical problem in the supply management and logistic. The repair vehicle services the customers who have goods, while the pickup vehicle visits the customer with nonrepaired goods. All the vehicles belong to an internal fleet of a company and have different capacities and fixed/variable cost. Moreover, vehicles have different limitations in their time of traveling. The objective of this problem is minimization of the total traveling cost and the time window violations. Two meta-heuristic algorithms (simple genetic algorithm and hybrid one) are used to find the best solution for this problem.

scholarly journals A specialized genetic algorithm for the fuel consumption heterogeneous fleet vehicle routing problem with bidimensional packing constraints

2021 ◽  
pp. 191-204 ◽  
Author(s):  
Luis Miguel Escobar-Falcón ◽  
David Álvarez-Martínez ◽  
John Wilmer-Escobar ◽  
Mauricio Granada-Echeverri
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Fuel Consumption ◽  
Vehicle Routing Problem ◽  
Computational Study ◽  
Real Life ◽  
Great Success ◽  
Routing Problem ◽  
Heterogeneous Vehicle Routing Problem ◽  
Benchmark Instances

The vehicle routing problem combined with loading of goods, considering the reduction of fuel consumption, aims at finding the set of routes that will serve the demands of the customers, arguing that the fuel consumption is directly related to the weight of the load in the paths that compose the routes. This study integrates the Fuel Consumption Heterogeneous Vehicle Routing Problem with Two-Dimensional Loading Constraints (2L-FHFVRP). To reduce fuel consumption taking the associated environmental impact into account is a classical VRP variant that has gained increasing attention in the last decade. The objective of this problem is to design the delivery routes to satisfy the customers’ demands with the lowest possible fuel consumption, which depends on the distances of the paths, the assigned vehicles, the loading/unloading pattern and the load weight. In the vehicle routing problem literature, the approximate algorithms have had great success, especially the evolutionary ones, which appear in previous works with quite a sophisticated structure, obtaining excellent results, but that are difficult to implement and adapt to other variants such as the one proposed here. In this study, we present a specialized genetic algorithm to solve the design of routes, keeping its main characteristic: the easy implementation. By contrast, the loading of goods restriction is validated by means of a GRASP algorithm, which has been widely employed for solving packing problems. With a view of confirming the performance of the proposed methodology, we provide a computational study that uses all the available benchmark instances, allowing to illustrate the savings achieved in fuel consumption. In addition, the methodology suggested can be adapted to the version of solely minimizing the total distance traveled for serving the customers (without the fuel consumption) and it is compared to the best works presented in the literature. The computational results show that the methodology manages to be adequately adapted to this version and it is capable of finding improved solutions for some benchmark instances. As for future work, we propose to adjust the methodology to consider the three-dimensional loading problem so that it adapts to more real-life conditions of the industry.

A Hybrid Genetic Algorithm-Simulated Annealing Approach for the Multi-Objective Vehicle Routing Problem with Time Windows

2011 ◽  
pp. 79-97
Author(s):  
Gülfem Tuzkaya ◽  
Bahadir Gülsün ◽  
Ender Bildik ◽  
E. Gözde Çaglar
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Hybrid Genetic Algorithm ◽  
Benchmark Problems ◽  
Routing Problem ◽  
Solution Approach ◽  
Multi Objective

In this study, the vehicle routing problem with time windows (VRPTW) is investigated and formulated as a multi-objective model. As a solution approach, a hybrid meta-heuristic algorithm is proposed. Proposed algorithm consists of two meta-heuristics: Genetic Algorithm (GA) and Simulated Annealing (SA). In this algorithm, SA is used as an improvement operator in GA. Besides, a hypothetical application is presented to foster the better understanding of the proposed model and algorithm. The validity of the algorithm is tested via some well-known benchmark problems from the literature.

An Improved Genetic Algorithm for Multi-Depot Heterogeneous Vehicle Routing Problem with Simultaneous Pickup and Delivery Time Windows

2015 ◽  
Vol 738-739 ◽  
pp. 361-365 ◽  
Author(s):  
Yan Guang Cai ◽  
Ya Lian Tang ◽  
Qi Jiang Yang
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Pickup And Delivery ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Heterogeneous Vehicle Routing Problem ◽  
Simultaneous Pickup And Delivery ◽  
Elite Selection

Multi-depot heterogeneous vehicle routing problem with simultaneous pickup and delivery and time windows (MDHVRPSPDTW) is an extension of vehicle routing problem (VRP), MDHVRPSPDTW mathematical model was established. The improved genetic algorithm (IGA) is proposed for solving the model. Firstly, MDHVRPSPDTW is transferred into different groups by the seed customer selecting method and scanning algorithm (SA).Secondly, IGA based on elite selection and inversion operator is used to solve the model, and then cutting merge strategy based on greedy thought and three kinds of neighborhood search methods is applied to optimize the feasible solutions further. Finally, 3-opt local search is applied to adjust the solution. The proposed IGA has been test on a random new numerical example.The computational results show that IGA is superior to branch and bound algorithm (BBD) by Lingo 9.0 in terms of optimum speed and solution quality, and the model and the proposed approach is effective and feasible.

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