A multiple ant colony system with random variable neighbourhood descent for the vehicle routing problem with time windows

Júnior, Orivalde S. Silva; Leal, José Eugênio

doi:10.1504/ijlsm.2021.117687

Cited by 4 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…12/17 (4) Although the time window deviation of perturbation management is slightly higher than that of global rescheduling, the time window deviation of global management negatively affects the number of additional vehicles; therefore, overall, the perturbation management approach is more optimal than global management.…”

Section: Empirical Conclusionmentioning

confidence: 99%

“…Belmecheri et al 3 improved the particle swarm optimization algorithm for solving the VRPTW for mixed round-trip customers. To solve the VRPTW, Júnior and Leal 4 proposed a hybrid heuristic algorithm using a multiple ant colony system; thus, they reduced the number of vehicles and the total distance traveled, while incorporating a stochastic variable neighborhood-descent algorithm for local search to obtain delivery paths with more optimal results. Bouchra et al 5 combined GA and variable neighborhood search to form a hybrid optimization method for solving a vehicle path problem with a soft time window and utilized GA to obtain an initial solution, which is obtained using a GA; thus, the study obtains the initial solution and optimizes the initial solution using two different neighborhood structures.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analyzing Vehicle Path Optimization using an Improved Genetic Algorithm in the Presence of Stochastic perturbation Matter

shengdong,

Boyu,

Jijian

et al. 2024

Preprint

View full text Add to dashboard Cite

By analyzing the influence of stochastic perturbation matters on vehicle path optimization, a perturbation scheduling model for logistics and distribution with a carbon tax mechanism is established under the premise of time window variation and load capacity constraints. Herein, we propose an enhanced Genetic Algorithm(GA) based on a Gaussian matrix mutation(GMM) operator, which maintains the diversity of the population while speeding up the algorithm’s convergence. The model builds a Gaussian probability matrix using the site positional order distribution characteristics implied in the original site data information, and applies the Gaussian probability matrix to individual gene mutations using a roulette-wheel-selection method; thus, the study guarantees the genetic diversity of the population while guiding it to evolve in the high-fitness direction. Finally, an experimental simulation is performed using data obtained from a commercial supermarket, thereby verifying the effectiveness of the proposed algorithm and comparing it with other algorithms. The results reveal that compared with the classical GA, the average convergence speed of the improved GA can be increased by 50%~60% and the consumed algorithm time can be reduced by 48% while maintaining the difference in solution accuracy within 1%.

show abstract

Section: Empirical Conclusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analyzing Vehicle Path Optimization using an Improved Genetic Algorithm in the Presence of Stochastic perturbation Matter

shengdong,

Boyu,

Jijian

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…This ability dealt with the nonlinear global optimization problem and was initially developed for finding the shortest path of the travelling salesman problem [22]. Other combinatorial optimization problems that have been tackled using ACS, including vehicle routing [23], job scheduling [24], network communication [19], [25] and image processing [26]. However, the ACS's exploring mechanism is ineffective.…”

Section: Introductionmentioning

confidence: 99%

Feature selection for sky image classification based on self adaptive ant colony system algorithm

Petwan,

Ku-Mahamud

2023

IJECE

View full text Add to dashboard Cite

Statistical-based feature extraction has been typically used to purpose obtaining the important features from the sky image for cloud classification. These features come up with many kinds of noise, redundant and irrelevant features which can influence the classification accuracy and be time consuming. Thus, this paper proposed a new feature selection algorithm to distinguish significant features from the extracted features using an ant colony system (ACS). The informative features are extracted from the sky images using a Gaussian smoothness standard deviation, and then represented in a directed graph. In feature selection phase, the self-adaptive ACS (SAACS) algorithm has been improved by enhancing the exploration mechanism to select only the significant features. Support vector machine, kernel support vector machine, multilayer perceptron, random forest, k-nearest neighbor, and decision tree were used to evaluate the algorithms. Four datasets are used to test the proposed model: Kiel, Singapore whole-sky imaging categories, MGC Diagnostics Corporation, and greatest common divisor. The SAACS algorithm is compared with six bio-inspired benchmark feature selection algorithms. The SAACS algorithm achieved classification accuracy of 95.64% that is superior to all the benchmark feature selection algorithms. Additionally, the Friedman test and Mann-Whitney <em>U</em> test are employed to statistically evaluate the efficiency of the proposed algorithms.

show abstract

Implementation of GVRP in Reducing Environmental Impacts and GHG Emission

Prakash

Pushkar

2021

2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA)

View full text Add to dashboard Cite

A multiple ant colony system with random variable neighbourhood descent for the vehicle routing problem with time windows

Cited by 4 publications

References 0 publications

Analyzing Vehicle Path Optimization using an Improved Genetic Algorithm in the Presence of Stochastic perturbation Matter

Analyzing Vehicle Path Optimization using an Improved Genetic Algorithm in the Presence of Stochastic perturbation Matter

Feature selection for sky image classification based on self adaptive ant colony system algorithm

Implementation of GVRP in Reducing Environmental Impacts and GHG Emission

Contact Info

Product

Resources

About