scholarly journals A Bee Evolutionary Algorithm for Multiobjective Vehicle Routing Problem with Simultaneous Pickup and Delivery

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Guiliang Gong ◽  
Qianwang Deng ◽  
Xuran Gong ◽  
Like Zhang ◽  
Haibin Wang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Vehicle Routing ◽  
Fuel Consumption ◽  
Vehicle Routing Problem ◽  
Two Stage ◽  
Nsga Ii ◽  
Routing Problem ◽  
Optimization Mechanism ◽  
Nondominated Sorting

A new closed-loop supply chain logistics network of vehicle routing problem with simultaneous pickups and deliveries (VRPSPD) dominated by remanufacturer is constructed, in which the customers are originally divided into three types: distributors, recyclers, and suppliers. Furthermore, the fuel consumption is originally added to the optimization objectives of the proposed VRPSPD. In addition, a bee evolutionary algorithm guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) with a two-stage optimization mechanism is originally designed to solve the proposed VRPSPD model with three optimization objectives: minimum fuel consumption, minimum waiting time, and the shortest delivery distance. The proposed BEG-NSGA-II could conquer the disadvantages of traditional nondominated sorting genetic algorithm II (NSGA-II) and algorithms with a two-stage optimization mechanism. Finally, the validity and feasibility of the proposed model and algorithm are verified by simulating an engineering machinery remanufacturing company’s reverse logistics and another three test examples.

scholarly journals A complexity task of optimization in logistic distribution: A new approach to the green multi-objective vehicle routing problem

2022 ◽  
Vol 38 (1) ◽  
Author(s):  
Ferreira J. ◽  
Steiner M.
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Logistic Distribution ◽  
Nsga Ii ◽  
Routing Problem ◽  
Multi Objective ◽  
The Difference ◽  
Stage 1

Logistic distribution involves many costs for organizations. Therefore, opportunities for optimization in this respect are always welcome. The purpose of this work is to present a methodology to provide a solution to a complexity task of optimization in Multi-objective Optimization for Green Vehicle Routing Problem (MOOGVRP). The methodology, illustrated using a case study (employee transport problem) and instances from the literature, was divided into three stages: Stage 1, “data treatment”, where the asymmetry of the routes to be formed and other particular features were addressed; Stage 2, “metaheuristic approaches” (hybrid or non-hybrid), used comparatively, more specifically: NSGA-II (Non-dominated Sorting Genetic Algorithm II), MOPSO (Multi-Objective Particle Swarm Optimization), which were compared with the new approaches proposed by the authors, CWNSGA-II (Clarke and Wright’s Savings with the Non-dominated Sorting Genetic Algorithm II) and CWTSNSGA-II (Clarke and Wright’s Savings, Tabu Search and Non-dominated Sorting Genetic Algorithm II); and, finally, Stage 3, “analysis of the results”, with a comparison of the algorithms. Using the same parameters as the current solution, an optimization of 5.2% was achieved for Objective Function 1 (OF{\displaystyle _{1}}; minimization of CO{\displaystyle _{2}} emissions) and 11.4% with regard to Objective Function 2 (OF{\displaystyle _{2}}; minimization of the difference in demand), with the proposed CWNSGA-II algorithm showing superiority over the others for the approached problem. Furthermore, a complementary scenario was tested, meeting the constraints required by the company concerning time limitation. For the instances from the literature, the CWNSGA-II and CWTSNSGA-II algorithms achieved superior results.

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.

Study on a Multi-Depot and Heterogeneous-Vehicle Open Vehicle Routing Problem to Reduce Fuel Consumption

2013 ◽  
Vol 336-338 ◽  
pp. 2567-2571
Author(s):  
Li Hua Zhang ◽  
Ming Yang Wang
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Fuel Consumption ◽  
Vehicle Routing Problem ◽  
Initial Population ◽  
Hard Problem ◽  
Routing Problem ◽  
Open Vehicle Routing Problem ◽  
Start Up ◽  
Open Vehicle Routing

An open vehicle routing problem is studied. In this problem, multi-depot, heterogeneous-vehicle, fuel consumption and start-up costs of vehicles are considered, thus a genetic algorithm is given to solve this hard problem. In order to improve the performance of the genetic algorithm, a heuristic algorithm is provided to produce the initial population and participate in crossover. An example is given to illustrate the genetic algorithm.

An evolutionary algorithm for the vehicle routing problem with route balancing

2009 ◽  
Vol 195 (3) ◽  
pp. 761-769 ◽  
Author(s):  
Nicolas Jozefowiez ◽  
Frédéric Semet ◽  
El-Ghazali Talbi
Keyword(s):  
Evolutionary Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem
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