An improved hybrid firefly algorithm for capacitated vehicle routing problem

Altabeeb, Asma M.; Mohsen, Abdulqader M.; Ghallab, Abdullatif

doi:10.1016/j.asoc.2019.105728

Cited by 106 publications

(50 citation statements)

References 18 publications

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“…The vehicle routing problem and its variants are generally classified as NP-hard problems, based on their combinatorial characteristics. Hence, the majority of studies apply heuristic optimization algorithms [39]- [42], metaheuristic optimization algorithms [43]- [44], and hybrid optimization algorithms [45]- [49] to solve the problem. The results from the numerical experiments, conducted in the reviewed studies, suggest that metaheuristic optimization algorithms can obtain near-optimal solutions for large-scale problem instances when compared with the results from exact optimization algorithms.…”

Section: B the Vehicle Routing Problemmentioning

confidence: 99%

An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

Pasha

Dulebenets

Kavoosi³

et al. 2020

IEEE Access

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The "factory-in-a-box" concept involves assembling production modules (i.e., factories) in containers and transporting the containers to different customer locations. Such a concept could be highly effective during emergencies, when there is an urgent demand for products (e.g., the COVID-19 pandemic). The "factory-in-a-box" planning problem can be divided into two sub-problems. The first sub-problem deals with the assignment of raw materials to suppliers, sub-assembly decomposition, assignment of subassembly modules to manufacturers, and assignment of tasks to manufacturers. The second sub-problem focuses on the transport of sub-assembly modules between suppliers and manufacturers by assigning vehicles to locations, deciding the order of visits for suppliers, manufacturers, and customers, and selecting the appropriate routes within the transportation network. This study addresses the second sub-problem, which resembles the vehicle routing problem, by developing an optimization model and solution algorithms in order to optimize the "factory-in-a-box" supply chain. A mixed-integer linear programming model, which aims to minimize the total cost of the "factory-in-a-box" supply chain, is presented in this study. CPLEX is used to solve the model to the global optimality, while four metaheuristic algorithms, including the Evolutionary Algorithm, Variable Neighborhood Search, Tabu Search, and Simulated Annealing, are employed to solve the model for large-scale problem instances. A set of numerical experiments, conducted for a case study of "factory-in-a-box", demonstrate that the Evolutionary Algorithm outperforms the other metaheuristic algorithms developed for the model. Some managerial insights are outlined in the numerical experiments as well. INDEX TERMS Factory-in-a-box, metaheuristics, supply chains, urgent demand, vehicle routing problem.

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Section: B the Vehicle Routing Problemmentioning

confidence: 99%

An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

Pasha

Dulebenets

Kavoosi³

et al. 2020

IEEE Access

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“…Algoritma genetika yang memiliki kelemahan pada total waktu perjalanan dan total jarak perjalanan yang lebih jauh dari algoritma tabu search [7]. Hasil dari algoritma genetika dalam menyelesaikan permasalahan Capacited Vehicle Routing Problem tidak lebih unggul daripada algoritma firefly yang memiliki keunggulan dalam menghasilkan solusi terbaik dengan tingkat konvergensi lebih cepat dan akurasi komputasi yang lebih tinggi daripada algoritma genetika [8]. Karena kekurangan pada algoritma tersebut, sehingga mengusulkan algoritma optimasi lain, yaitu algoritm Firefly dan Tabu Search.…”

Section: Pendahuluanunclassified

Determination of Optimal Public Transportation Routes Using Firefly and Tabu Search Algorithms

Mansur

Widowati

Imrona

2020

RESTI

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Traffic congestion problems generally caused by the increasing use of private vehicles and public transportations. In order to overcome the situation, the optimization of public transportation’s route is required particularly the urban transportation. In this research, the performance analysis of Firefly and Tabu Search algorithm is conducted to optimize eleven public transportation’s routes in Bandung. This optimization aims to increase the dispersion of public transportation’s route by expanding the scope of route that are crossed by public transportation so that it can reach the entire Bandung city and increase the driver’s income by providing the passengers easier access to public transportations in order to get to their destinations. The optimal route is represented by the route with most roads and highest number of incomes. In this research, the comparison results between the reference route and the public transportation’s optimized route increasing the dispersion of public transportation’s route to 60,58% and increasing the driver’s income to 20,03%.

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“…The meta-heuristics that have been applied in the UFT context are ant colony optimization [30], artificial bee colony [34], biogeography-based algorithms [23], the firefly algorithm [19], evolutionary algorithms [15], [17], [18], [24], [25], [31], [21], taboo search [33], [38] and simulated annealing [29], [40], among others. From the articles that address multi-objective versions of VRP with more than 20 nodes (clients), in more than 80% of them use, as said, meta-heuristics, and among them, in particular Evolutionary Multi-Objective Algorithms (MOEAs) [36].…”

Section: Literature Reviewmentioning

confidence: 99%

A Decision Support Tool for Urban Freight Transport Planning Based on a Multi-Objective Evolutionary Algorithm

et al. 2019

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We present an optimization procedure based on a hybrid version of an evolutionary multiobjective decision-making algorithm for its application in urban freight transportation planning problems. This tool is intended to solve the planning problems of a merchandise distribution firm that dispatches small volume fractional loads of fresh foods on daily schedules. The firm owns a network of distribution centers supplying a large number of small businesses in Buenos Aires and its surroundings. The recombination operator of the evolutionary algorithm used here has been designed specifically for this problem. It is intended to embody a strategy that takes into account constraints like temporary closeness, closeness time window and connectivity in order to improve its performance in the clustering phase. The representation allows incorporating specific information about the actual instances of the problem and uses adaptive control of the parameters in the calibration stage. The performance of the proposed optimizer was tested against the results obtained by two evolutionary algorithms, NSGA II and SPEA 2, widely used in similar problems. We use hypervolume as a measure of convergence and dispersion of Pareto fronts. The statistical analysis of the results obtained with the three algorithms uses the Wilcoxon rank sum test, which yields evidence that our procedure provides good results.

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An improved hybrid firefly algorithm for capacitated vehicle routing problem

Cited by 106 publications

References 18 publications

An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

Determination of Optimal Public Transportation Routes Using Firefly and Tabu Search Algorithms

A Decision Support Tool for Urban Freight Transport Planning Based on a Multi-Objective Evolutionary Algorithm

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