scholarly journals Machine Learning-Based Parameter Tuned Genetic Algorithm for Energy Minimizing Vehicle Routing Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
P. L. N. U. Cooray ◽  
Thashika D. Rupasinghe
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Business Practices ◽  
Routing Problem ◽  
Solution Approach ◽  
Sustainable Logistics

During the last decade, tremendous focus has been given to sustainable logistics practices to overcome environmental concerns of business practices. Since transportation is a prominent area of logistics, a new area of literature known as Green Transportation and Green Vehicle Routing has emerged. Vehicle Routing Problem (VRP) has been a very active area of the literature with contribution from many researchers over the last three decades. With the computational constraints of solving VRP which is NP-hard, metaheuristics have been applied successfully to solve VRPs in the recent past. This is a threefold study. First, it critically reviews the current literature on EMVRP and the use of metaheuristics as a solution approach. Second, the study implements a genetic algorithm (GA) to solve the EMVRP formulation using the benchmark instances listed on the repository of CVRPLib. Finally, the GA developed in Phase 2 was enhanced through machine learning techniques to tune its parameters. The study reveals that, by identifying the underlying characteristics of data, a particular GA can be tuned significantly to outperform any generic GA with competitive computational times. The scrutiny identifies several knowledge gaps where new methodologies can be developed to solve the EMVRPs and develops propositions for future research.

scholarly journals Algoritma Genetika untuk Pemecahan Masalah Rute Kendaraan dengan Ukuran dan Campuran Armada, Trip Majemuk, Pengiriman Terbagi, Produk Majemuk, dan Kendaraan dengan Kompartemen Majemuk

2018 ◽  
Vol 19 (2) ◽  
pp. 115
Author(s):  
Suprayogi Suprayogi ◽  
Daniel Bunga Paillin
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
Fleet Size ◽  
Split Delivery ◽  
Solution Approach ◽  
Fleet Size And Mix

This paper discusses a variant of the basic vehicle routing problem (VRP) by including the following characteristics: fleet size and mix, multiple trips, split delivery, and multiple compartments. One of real cases of this problem is related to determining tanker’s routes in delivering fuel products from a supply port to a number of destination ports. Each tanker has several compartments where each compartment is dedicated to a certain fuel product. In this paper, a solution approach based on genetic algorithm (GA) is proposed and tested using nine hypothetical instances. Experiment results show that the proposed GA gives consistent results measured by coefficient variations

scholarly journals Tabu Search for the Vehicle Routing Problem with Multiple Trips, Time Windows, and Simultaneous Delivery-Pickup

2018 ◽  
Vol 19 (2) ◽  
pp. 75
Author(s):  
Suprayogi Suprayogi ◽  
Yusuf Priyandari
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Local Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Objective Function Value ◽  
Time Windows ◽  
Routing Problem ◽  
Solution Approach ◽  
Simultaneous Delivery

This paper discusses a vehicle routing problem with multiple trips, time windows, and simultaneous delivery-pickup (VRPMTTWSDP). This problem is a variant of the basic vehicle routing problem (VRP) including the following characteristics: multiple trips, time windows, and simultaneous delivery-pickup.  In this paper, a solution approach based on tabu search (TS) is proposed. In the proposed TS, the sequential insertion (SI) algorithm is used to construct an initial solution. A neighbor structure is generated by applying an operator order consisting of eleven operators of relocation, exchange, and crossover operators. A tabu solution code (TSC) method is applied as a tabu restriction mechanism. Computational experiments are carried out to examine the performance of the proposed TS using hypothetical instances. The performance of the proposed TS is compared to the local search (LS) and the genetic algorithm (GA). The comparison shows that the proposed TS is better in terms of the objective function value.

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.

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.

scholarly journals EODVGA: An Enhanced ODV Based Genetic Algorithm for Multi-Depot Vehicle Routing Problem

2019 ◽  
Vol 6 (21) ◽  
pp. 159099
Author(s):  
Prabu U ◽  
Ravisasthiri P ◽  
Sriram R ◽  
Malarvizhi N ◽  
Amudhavel J
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem
Sign in / Sign up

Export Citation Format

Share Document