Solving Capacitated Vehicle Routing Problem with route optimization using Swarm Intelligence

Akhand, M. A. H.; Peya, Zahrul Jannat; Sultana, Tanzima; Al-Mahmud,

doi:10.1109/eict.2015.7391932

Cited by 12 publications

(9 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The first is to use greedy-random initialization to obtain an initial travel solution that is closer to the optimal solution. After the migration and mutation of the reasoning algorithm, the 2-opt algorithm is used to improve the circuit path [35], so as to obtain the best solution. Please refer to the flowchart of the improved method in Figure 5.…”

Section: G2bbo Solving Travel Salesman Problemmentioning

confidence: 99%

A Biogeography-Based Optimization with a Greedy Randomized Adaptive Search Procedure and the 2-Opt Algorithm for the Traveling Salesman Problem

et al. 2023

View full text Add to dashboard Cite

We develop a novel method to improve biogeography-based optimization (BBO) for solving the traveling salesman problem (TSP). The improved method is comprised of a greedy randomized adaptive search procedure, the 2-opt algorithm, and G2BBO. The G2BBO formulation is derived and the process flowchart is shown in this article. For solving TSP, G2BBO effectively avoids the local minimum problem and accelerates convergence by optimizing the initial values. To demonstrate, we adopt three public datasets (eil51, eil76, and kroa100) from TSPLIB and compare them with various well-known algorithms. The results of G2BBO as well as the other algorithms perform close enough to the optimal solutions in eil51 and eil76 where simple TSP coordinates are considered. In the case of kroa100, with more complicated coordinates, G2BBO shows greater performance over other methods.

show abstract

Section: G2bbo Solving Travel Salesman Problemmentioning

confidence: 99%

A Biogeography-Based Optimization with a Greedy Randomized Adaptive Search Procedure and the 2-Opt Algorithm for the Traveling Salesman Problem

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Permasalahan CVRP telah banyak diselesaikan dengan menggunakan berbagai macam algoritma, baik algoritma heuristic maupun algoritma metaheuristic. Beberapa penelitian yang terkait dengan CVRP dan algoritma yang digunakan antara lain algoritma genetika dan algoritma ant colony yang dibahas oleh (Mazidi et al, 2016), algoritma Variant Sweep and Swarm yang dibahas oleh (Akhand et al, 2017), algoritma Genetic and Guided Local Search yang dibahas oleh (Rahmani Hosseinabadi et al, 2019), algoritma Adaptive Sweep Clustering yang dibahas oleh (Peya et al, 2018), algoritma Artificial Bee Colony yang dibahas oleh (Brajevic, 2011).…”

Section: Pendahuluanunclassified

Algoritma Gravitional Emulation Local Search Pada CVRP Dan Implementasinya

Azis

Wahyuningsih²,

Satyananda

2022

JKMA

View full text Add to dashboard Cite

Permasalahan optimalisasi distribusi dapat dipecahkan dengan menggunakan algoritma pada varian Vehicle Routing Problem (VRP). Salah satu varian dari VRP adalah Capacitated Vehicle Routing Problem (CVRP) yaitu dengan tambahan kendala kapasitas kendaraan yang identik. Algoritma Gravitational Emulation Local Search (GELS) dapat digunakan untuk menentukan solusi CVRP. Algorima GELS merupakan gabungan dari algoritma genetika dan local search (best improvement local search). Pada artikel ini dibahas langkah algoritma dan diimplementasikan ke dalam computer menggunakan aplikasi Borland Delphi 7. Input program berupa ukuran populasi, probabilitas crossover, probabilitas mutasi, maksimum iterasi, kapasitas kendaraan, banyaknya titik, dan permintaan setiap customer. Output berupa hasil rute dengan jarak yang ditempuh serta divisualisasi rutenya dengan gambar graph. Diberikan contoh penyelesaian permasalahan dengan contoh 7 titik terdiri dari satu depot dan enam customer. Hasil tampilan program berupa matrik bobot titik, permintaan, dan hasil berupa rute optimal. Aplikasi program GELS pada CVRP secara praktis dapat digunakan untuk penyelesaian optimasi distribusi.

show abstract

“…Where: N is the collection of service points and distribution centers; i and j respectively represent the number of the demand point; K is the total number of vehicles; c ij is the transportation cost of vehicles from i to j; d i is the demand of customer i; Q is the maximum carrying capacity of the vehicle; x ijk is when vehicle k is assigned to run from customer i to customer j, take 1; otherwise, take 0. Equation (1) represents the objective function is the lowest total distribution cost; Equation (2) indicates that each demand point can only be served by one vehicle once; equation (3) represents the capacity constraint of the vehicle; equation (4) indicates that the vehicle must start from the distribution center; equation (5) indicates that vehicle k must leave after the service of demand point j is completed; equation (6) indicates that each vehicle must return to the distribution center; equation (7) indicates that the constraint variable is 0-1 variable.…”

Section: Modelingmentioning

confidence: 99%

“…Li Shuanglin et al [5] established a logistics distribution model of multi-objective location multimodal transportation, and used multi-objective genetic algorithm to solve the problem, so that the total time of emergency resource distribution is the shortest, and the resource demand satisfaction rate of disaster area is the highest. Stanley et al [6] studied the formation of clusters from different starting angles In the Sweep variant, and used velocity tentative particle swarms to optimize the routh, which can finally solve the CVRP problem better.…”

Section: Introductionmentioning

confidence: 99%

Emergency Resource Scheduling During COVID-19

Ling-fang

Xiang

2021

E3S Web Conf.

View full text Add to dashboard Cite

Due to the suddenness and uncertainty of the COVID-19, sufficient emergency resources should be allocated to the appropriate emergency space within a limited time. In order to ensure the smooth implementation of emergency resource scheduling, emergency resource must be timely and effectively supplied. Therefore, it is of great significance to study how to reasonably distribute emergency resource and how to optimize the vehicle routing problem to improve the efficiency of emergency. Based on the research of capacitated vehicle routing problem (CVRP) solved by traditional genetic algorithm, this paper proposes simulated annealing-genetic algorithm, which has been verified to effectively solve the capacitated vehicle routing problem.

show abstract

Solving Capacitated Vehicle Routing Problem with route optimization using Swarm Intelligence

Cited by 12 publications

References 4 publications

A Biogeography-Based Optimization with a Greedy Randomized Adaptive Search Procedure and the 2-Opt Algorithm for the Traveling Salesman Problem

A Biogeography-Based Optimization with a Greedy Randomized Adaptive Search Procedure and the 2-Opt Algorithm for the Traveling Salesman Problem

Algoritma Gravitional Emulation Local Search Pada CVRP Dan Implementasinya

Emergency Resource Scheduling During COVID-19

Contact Info

Product

Resources

About