An improved artificial bee colony algorithm for vehicle routing problem with time windows: A real case in Dalian

Yu, Shaoqiang; Tai, Cuicui; Liu, Yanan; Gao, Linjie

doi:10.1177/1687814016665298

Cited by 8 publications

(6 citation statements)

References 35 publications

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“…The customer transferred from one route to another with the lowest delivery cost after using tabu search 10,11 . According to the attraction of each path, Yu et al 12 created the collective behavior of ants in ant colony‐based DVRP and determined the shortest path 13 . To prevent premature convergence, an improved evolutionary algorithm using a brand‐new crossover operator was suggested 14 .…”

Section: Introductionmentioning

confidence: 99%

Modeling a novel heuristic model for handling vehicle routing using multiobjective optimization

Praveen¹,

Periasamy

Gurusamy³

et al. 2023

Concurrency and Computation

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SummaryElectric vehicles (EVs) are drastically growing, and their emergence is more popular. Moreover, with the constraint driving range, the batteries of these vehicles are rechargeable throughout the routes in various situations. Here, a new problem is formulated with the inclusion of both battery swapping and partial recharging decisions. The energy level and the routing directions are more important, and the investigators intend to handle these issues effectually. However, the major constraint is routing and time window. Here, a heuristic model (LTS‐RSS) is designed to find the best matching solution. A novel Random Sub‐Space (RSS) and local Tabu search (LTS) are modeled to handle these issues. The probabilistic model of RSS is anticipated by integrating the consequences of time windows and distances. The experimentation is done with an online database and used for performance validation. The outcomes show that the newly modeled (LTS‐RSS) approach enhances the significance of the model. The outcomes of all the instances with diverse strategies enhance the model's robustness and stability for resolving these issues. The empirical analysis is done with MATLAB 2020b simulator, and metrics like optimization of routing solution, the best vehicle, best distance, and numbers of vehicles are evaluated, and the outcomes are compared with various other approaches.

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Section: Introductionmentioning

confidence: 99%

Modeling a novel heuristic model for handling vehicle routing using multiobjective optimization

Praveen¹,

Periasamy

Gurusamy³

et al. 2023

Concurrency and Computation

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show abstract

“…15,31,32 The artificial bee colony (ABC) algorithm was firstly proposed by Karaboga. 33,34 Since then, the ABC algorithm has been applied in many types of optimization problem, such as the graph selection problem, 35 the flexible job shop scheduling problem, 36,37 the path planning problem, 38 the distributed flow shop scheduling problem, 39,40 the block flowshop, 41 the prefabricated optimization problem, 42 the VRPTW problem, 43 the fuzzy scheduling problem, 44 and the task scheduling problem. 45 The ABC algorithm has been shown as a competitive optimization method compared with other efficient optimization methods, such as the Jaya algorithm, 46 the invasive weed optimization algorithm, 47 the differential evolution algorithm, 48 the harmony search algorithm, 49 the shuffled frog-leaping algorithm, 50 the bioinspired metaheuristic algorithm, 51 the krill herd algorithm, 52 the TS algorithm, 53 the particle swarm algorithm, 54 and other multi-objective optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic algorithm for solving the multi-objective vehicle routing problem with time window and drones

Han

Liu

et al. 2020

International Journal of Advanced Robotic Systems

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In some special rescue scenarios, the needed goods should be transported by drones because of the landform. Therefore, in this study, we investigate a multi-objective vehicle routing problem with time window and drone transportation constraints. The vehicles are used to transport the goods and drones to customer locations, while the drones are used to transport goods vertically and timely to the customer. Three types of objectives are considered simultaneously, including minimization of the total energy consumption of the trucks, total energy consumption of the drones, and the total number of trucks. An improved artificial bee colony algorithm is designed to solve the problem. In the proposed algorithm, each solution is represented by a two-dimensional vector, and the initialization method based on the Push-Forward Insertion Heuristic is embedded. To enhance the exploitation abilities, an improved employed heuristic is developed to perform detailed local search. Meanwhile, a novel scout bee strategy is presented to improve the global search abilities of the proposed algorithm. Several instances extended from the Solomon instances are used to test the performance of the proposed improved artificial bee colony algorithm. Experimental comparisons with the other efficient algorithms in the literature verify the competitive performance of the proposed algorithm.

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“…Note that the identification of fuzzy measure aiming to obtain more accurate solutions is a difficult point. 34 In this work, artificial bee colony (ABC) algorithm 35,36 is first introduced to identify the l-fuzzy measure and achieve superior results. Fuzzy integral mainly includes Sugeno fuzzy integral, Choquet fuzzy integral, and Zhenyuan fuzzy integral.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of equipment maintenance quality: A hybrid multi-criteria decision-making approach

Zhang

Shan

2019

Advances in Mechanical Engineering

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Maintenance plays a crucial role in the entire life cycle of equipment. With the acceleration of industrialization, the evaluation of equipment maintenance quality has undoubtedly become more challenging due to the complex mechanical structure, various maintenance modes, and so on. In order to make decisions scientifically, a hybrid multi-criteria decision-making approach integrating triangle fuzzy number, l-fuzzy measure, TOPSIS, and Choquet fuzzy integral is proposed in this article. First, the interaction among criteria can be handled reasonably by fuzzy integral based on l-fuzzymeasure. Second, fuzzy numbers which are given by experts are applied to deal with fuzzy linguistic value. In addition, artificial bee colony algorithm is first introduced to identify l-fuzzy-measure. The comparison results of three optimization algorithms which include artificial bee colony algorithm, genetic algorithm, and particle swarm optimization prove artificial bee colony algorithm is more effective than genetic algorithm and particle swarm optimization. A case study which contains six maintenance alternatives is practiced to prove the effectiveness of the proposed hybrid multi-criteria decision-making approach. Finally, the comparison is made between the proposed method and two classical multi-criteria decision-making approaches which refer to TOPSIS and gray correlation, and the results demonstrate the proposed method is suitable to solve maintenance quality evaluation problem.

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An improved artificial bee colony algorithm for vehicle routing problem with time windows: A real case in Dalian

Cited by 8 publications

References 35 publications

Modeling a novel heuristic model for handling vehicle routing using multiobjective optimization

Modeling a novel heuristic model for handling vehicle routing using multiobjective optimization

Metaheuristic algorithm for solving the multi-objective vehicle routing problem with time window and drones

Evaluation of equipment maintenance quality: A hybrid multi-criteria decision-making approach

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