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.

scholarly journals Development Of A New Genetic Algorithm For Solving Capacitated Vehicle Routing Problem In Short Time

2019 ◽  
Vol 8 (11) ◽  
pp. 903-907
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Benchmark Instances ◽  
Full Capacity ◽  
Short Time ◽  
Number Of Customers ◽  
Capacitated Vehicle

In this paper a new genetic algorithm is developed for solving capacitated vehicle routing problem (CVRP) in situations where demand is unknown till the beginning of the trip. In these situations it is not possible normal metaheuristics due to time constraints. The new method proposed uses a new genetic algorithm based on modified sweep algorithm that produces a solution with the least number of vehicles, in a relatively short amount of time. The objective of having least number of vehicles is achieved by loading the vehicles nearly to their full capacity, by skipping some of the customers. The reduction in processing time is achieved by restricting the number of chromosomes to just one. This method is tested on 3 sets of standard benchmark instances (A, M, and G) found in the literature. The results are compared with the results from normal metaheuristic method which produces reasonably accurate results. The results indicate that whenever the number of customers and number of vehicles are large the new genetic algorithm provides a much better solution in terms of the CPU time without much increase in total distance traveled. If time permits the output from this method can be further improved by using normal established metaheuristics to get better solution

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.

A Memetic Algorithm for the Multi-Depot Vehicle Routing Problem with Limited Stocks

2015 ◽  
pp. 411-445 ◽  
Author(s):  
Shi Li ◽  
Yahong Zheng
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Memetic Algorithm ◽  
Central Place ◽  
Combinatorial Problems ◽  
Logistics Management ◽  
Routing Problem ◽  
Benchmark Instances

The Vehicle Routing Problem (VRP) is one of important combinatorial problems, which holds a central place in logistics management. One of the most widely studied problems in the VRP family is the Multi-Depot Vehicle Routing Problem (MDVRP), where more than one depot is considered. In this chapter, the authors focus on a new extension of the MDVRP in which goods loaded by the vehicle are restricted due to limited stocks available at warehouses. More specifically, this extension consists in determining a least cost routing plan that can satisfy all the customs demands by delivering available stocks. Indeed, this problem is often encountered when goods are shortage in some warehouses. To deal with the problem efficiently, a memetic algorithm is proposed in this chapter. The authors study this approach on a set of modified benchmark instances and compare its performance to a pure genetic algorithm.

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.

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.

scholarly journals A two-level based genetic algorithm for solving the soft-clustered vehicle routing problem

2021 ◽  
Vol 38 (1) ◽  
pp. 117-128
Author(s):  
OVIDIU COSMA ◽  
◽  
PETRICĂ C. POP ◽  
CORINA POP SITAR ◽  
◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Problem ◽  
A Priori ◽  
Minimum Cost ◽  
Routing Problem ◽  
Solution Approach ◽  
Benchmark Instances ◽  
Generalized Vehicle Routing Problem

The soft-clustered vehicle routing problem (Soft-CluVRP) is a relaxation of the clustered vehicle routing problem (CluVRP), which in turn is a variant of the generalized vehicle routing problem (GVRP). The aim of the Soft-CluVRP is to look for a minimum cost group of routes starting and ending at a given depot to a set of customers partitioned into a priori defined, mutually exclusive and exhaustive clusters, satisfying the capacity constraints of the vehicles and with the supplementary property that all the customers from the same cluster have to be supplied by the same vehicle. The considered optimization problem is NP-hard, that is why we proposed a two-level based genetic algorithm in order to solve it. The computational results reported on a set of existing benchmark instances from the literature, prove that our novel solution approach provides high-quality solutions within acceptable running times.

Sign in / Sign up

Export Citation Format

Share Document