Hybrid Firefly Model in Routing Heterogeneous Fleet of Vehicles in Logistics Distribution

Simić, Dragan; Kovačević, Ilija; Svirčević, Vasa; Simić, Svetlana

doi:10.1093/jigpal/jzv011

Cited by 23 publications

(9 citation statements)

References 11 publications

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“…VRPTW is developed from CVRP, therefore, they adopted the real-coded schema of CVRP as well as the real-coded schema for PSO algorithm in VRP introduced by Wu et al (2007). Simic et al, (2015) applied a hybrid firefly algorithm for solving the HFFVRP. Comparing the experimental results, applied in realistic data of a company, with the company's empirical results the algorithm outperformed them.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Despite of the fact that firefly algorithm was originally proposed for optimization problems with continuous variables, many attempts have been made in order to implement it in practical problems which are discrete in nature with very promising results. These include job scheduling problems (Sayadi et al, 2010), knapsack problems (Baykasoglu and Ozsoydan, 2014) and vehicle routing problems (Pan et al, 2013;Simic et al, 2015;Wang et al, 2015;Osaba et al, 2016;Saraei and Ali Ghaheri, 2017).…”

Section: Introductionmentioning

confidence: 99%

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A Firefly Algorithm for the Heterogeneous Fixed Fleet VRP

Matthopoulos

Sofianopoulou

2019

IJISE

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Vehicle routing is a key success factor in logistics problems. A variation of Vehicle Routing problem (VRP), the heterogeneous fixed fleet VRP in which the vehicles available for distribution activities are characterized by different capacities and costs, is tackled. A hybrid firefly algorithm for optimizing the routing of heterogeneous fixed fleet of vehicles in logistics distribution systems is presented. The principles and key steps of the proposed firefly algorithm are introduced in detail. Experimental results from solving the heterogeneous fixed fleet vehicle routing problem when tested on benchmark datasets are demonstrated. Moreover, the algorithm is compared with other algorithms solving similar problems in order to prove the effectiveness of the proposed hybrid firefly algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Firefly Algorithm for the Heterogeneous Fixed Fleet VRP

Matthopoulos

Sofianopoulou

2019

IJISE

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“…Constraints (12) and (13) guarantee that any vehicles (i.e., truck and helicopter) after serving any nodes must come back to the start point and the route is closed. Constraint (14) ensures that each vehicle (i.e., helicopter or truck) only serves one node (a point with healthy leading road) and consequently, Constraint (15) identi es that each vehicle (helicopter) serves only one unhealthy node (a ected point with damaged leading road). Constraints (16) and (17) are the capacity limitations of trucks and helicopters.…”

Section: Notations and Sets Vmentioning

confidence: 99%

“…More lighting results in more objective function and leads to a better solution [58]. In fact, the objective function of this algorithm is tness function of the genetic algorithm [14]. Figure 5 represents a re y and the pseudo-code of re y algorithm that shows an initialization, a moving factor, and an objective function [60].…”

Section: Mainframe Of Re Y Algorithmmentioning

confidence: 99%

“…They suggested two well-known methods of NSGA-II and MOSA for solving their model. Simic et al [14] presented a routing model with heterogeneous eet of vehicles in logistics distribution. In fact, this study presented the hybrid of genetic and re y algorithms, in which the genetic algorithm was used in the rst step of the optimization process and capacity zones de nition, and the re y algorithm was used in the second step in this model.…”

Section: Introduction 1motivationmentioning

confidence: 99%

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A cluster-based emergency vehicle routing problem in disaster with reliability

et al. 2017

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Abstract. In the event of natural disasters, relief distribution is the most challenging problem in emergency transportation. What is important in response to disaster is victims' relief in disaster areas with the quick distribution of vital commodity. In this regard, damage to infrastructure (e.g., roads) can make trouble in designing a distribution network. Therefore, this paper considers a problem using a three-stage approach. In the rst stage, pre-processing of model inputs is done through an Arti cial Neural Fuzzy Inference System (ANFIS) followed by investigating the safest route for each cluster using decision-making techniques and graph theory. In the second stage, a heterogeneous multi-depot multimode vehicle routing problem is formulated for minimizing the transportation time and maximizing the reliability. Finally, since the routing problem is NP-hard, 2 multi-objective meta-heuristic algorithms, namely, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Fire y Algorithm (MOFA), are proposed to obtain the optimal solution and their performances are compared through a set of randomly generated test problems. The results show that for this routing problem, the MOFF gives better solutions than NSGA-II does, and it performs well in terms of accuracy and solution time.

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