A Modified Artificial Bee Colony Algorithm for Vehicle Routing Problems with Time Windows

Shi, Yufeng; Meng, Fanwei; Shen, Guo-Jiang

doi:10.3923/itj.2012.1490.1495

Cited by 12 publications

(6 citation statements)

References 27 publications

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“…The enhanced ABC and hybrid ABC algorithm with other heuristic and metaheuristics e.g. BCiA by [15], ABC-T by [16] and Tabu-ABC by [17] are adopted to solve the problem efficiently. However, the exploration of the algorithm works well but the exploitation is poor.…”

Section: Introductionmentioning

confidence: 99%

The New Methodology for Vehicular Network With Fuzzy Time Windows

Kantawong

Pravesjit

Chaisricharoen

2019

MJCS

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This work proposes the new methodology for the vehicular network with fuzzy time windows. The Fuzzy technique is applied to produce an initial population and then the evolutionary algorithm is employed to improve the solution. In this work, the inter-route crossover, intra-route mutation, elitism strategy, and onlooker bee probability selection method were enhanced in the original processes of the evolutionary algorithm. The proposed algorithm is tested on 56 datasets of Solomon. The results from the proposed algorithm are shown in comparison with other algorithms in the literature. The findings from the computational results are very inspiring, it shows that the algorithm is very competitive. Comparing with the algorithm in the literature, the proposed algorithm obtains the best solution in terms of the coefficient of variation values for almost 40 instances from the 56 problem instances. In addition, the information regarding the p-value was resolved by the Wilcoxon signed-rank test for the considered testing instances that display statistically superior performance at the 95% significance level (Î± = 0.05) on comparing algorithms.

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Section: Introductionmentioning

confidence: 99%

The New Methodology for Vehicular Network With Fuzzy Time Windows

Kantawong

Pravesjit

Chaisricharoen

2019

MJCS

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“…The objective of the VRPTW is to find a minimum cost routes for serving all the customers during their given intervals. [4][5][6][7][8] VRPTW is also proved to be NP-hard problem. Heuristic algorithms are the first choice to solve this combinational problem.…”

Section: Introductionmentioning

confidence: 99%

“…Heuristic algorithms are the first choice to solve this combinational problem. [7][8][9][10][11][12][13][14][15][16] Chiang and Russell 17 proposed a simulated annealing metaheuristic for solving the VRPTW. Tan et al 18 presented a hybrid genetic algorithms (GAs) to solve the VRPs with time window constraints.…”

Section: Introductionmentioning

confidence: 99%

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

Tai

Liu

et al. 2016

Advances in Mechanical Engineering

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This study has been motivated from a real western-style food delivery problem in Dalian city, China, which can be described as a vehicle routing problem with time windows. An integer linear model for the problem is developed, and an improved artificial bee colony algorithm, which possesses a new strategy called an adaptive strategy, a crossover operation, and a mutation operation, is proposed to solve the problem. Then, the effectiveness of the proposed improved artificial bee colony is first validated by some benchmark instances. Furthermore, results obtained on a real-case instance for western-style food delivery problem in Dalian city are also discussed. In this case, the results indicate that the improved artificial bee colony algorithm is a feasible method to solve the real vehicle routing problem with time windows such as western-style food delivery.

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“…Shi et al [53] proposed the ABC-T algorithm for solving the VRPTW. ABC-T is based on the standard ABC but uses a tournament selection mechanism to select food sources, improving the global search capability of the algorithm.…”

Section: Artificial Bee Colonymentioning

confidence: 99%

Bee‐Inspired Algorithms Applied to Vehicle Routing Problems: A Survey and a Proposal

Masutti

Castro

2017

Mathematical Problems in Engineering

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Vehicle routing problems constitute a class of combinatorial optimization tasks that search for optimal routes (e.g., minimal cost routes) for one or more vehicles to attend a set of nodes (e.g., cities or customers). Finding the optimal solution to vehicle routing tasks is an NP-hard problem, meaning that the size of problems that can be solved by exhaustive search is limited. From a practical perspective, this class of problems has a wide and important set of applications, from the distribution of goods to the integrated chip design. Rooted on the use of collective intelligence, swarm-inspired algorithms, more specifically bee-inspired approaches, have been used with good performance to solve such problems. In this context, the present paper provides a broad review on the use of bee-inspired methods for solving vehicle routing problems, introduces a new approach to solve one of the main tasks in this area (the travelling salesman problem), and describes open problems in the field.

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A Modified Artificial Bee Colony Algorithm for Vehicle Routing Problems with Time Windows

Cited by 12 publications

References 27 publications

The New Methodology for Vehicular Network With Fuzzy Time Windows

The New Methodology for Vehicular Network With Fuzzy Time Windows

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

Bee‐Inspired Algorithms Applied to Vehicle Routing Problems: A Survey and a Proposal

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