Bees Algorithm for Vehicle Routing Problems with Time Windows

Alzaqebah, Malek; Jawarneh, Sana; Sarim, Hafiz Mohd; Abdullah, Salwani

doi:10.18178/ijmlc.2018.8.3.693

Cited by 30 publications

(26 citation statements)

References 25 publications

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“…The performance of the proposed algorithm was evaluated in comparison with the best-known results of other algorithms which were taken from [18]. Table 2 illustrates the computation results from the proposed algorithm compared to the other five published algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…The vehicle capacity and the position of customers are shown in columns 2 and 3. Columns 4 to 9 present the best mean of each problem set from the proposed algorithm and the algorithms proposed by [18], [27], [28], [9], and [29] respectively. When compared with other five algorithms, it shows that the proposed algorithm produces the best results for all problem instances.…”

Section: Resultsmentioning

confidence: 99%

“…Many heuristics, metaheuristics, swarm intelligence, and other algorithms have been widely published to solve the VRPTW in the past few decades. The Bee algorithm (BA) and modified artificial bee colony were published to solve the VRPTW, for example, in the publications of [17][18][19]. The artificial bee colony (ABC) algorithm is a class of natureinspired algorithm which was proposed by [20].…”

Section: Literature Reviewmentioning

confidence: 99%

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An Enhanced ABC algorithm to Solve the Vehicle Routing Problem with Time Windows

Kantawong

Pravesjit

2020

ECTI-CIT

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This work proposes an enhanced artificial bee colony algorithm (ABC) to solve the vehicle routing problem with time windows (VRPTW). In this work, the fuzzy technique, scatter search method, and SD-based selection method are combined into the artificial bee colony algorithm. Instead of randomly producing the new solution, the scout randomly chooses the replacement solution from the abandoned solutions from the onlooker bee stage. Effective customer location networks are constructed in order to minimize the overall distance. The proposed algorithm is tested on the Solomon benchmark dataset where customers live in different geographical locations. The results from the proposed algorithm are shown in comparison with other algorithms in the literature. The findings from the computational results are very encouraging. Compared to other algorithms, the proposed algorithm produces the best result for all testing problem sets. More significantly, the proposed algorithm obtains better quality than the other algorithms for 39 of the 56 problem instances in terms of vehicle numbers. The proposed algorithm obtains a better number of vehicles and shorter distances than the other algorithm for 20 of the 39 problem instances.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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An Enhanced ABC algorithm to Solve the Vehicle Routing Problem with Time Windows

Kantawong

Pravesjit

2020

ECTI-CIT

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“…The Bees Algorithm has successful applications against other combinatorial problems. Some of the highlights from the few applications; single machine scheduling problem [16], combinational circuit design [17], PCB assembly optimisation [18], vehicle routing problem [19], examination timetabling problems [20], a multi-objective supply chain optimisation [21,22], disassembly sequence planning of human-robot collaboration in remanufacturing [15,23]. In this study, the Bees Algorithm was designed within the scope of car disassembly process planning.…”

Section: Introductionmentioning

confidence: 99%

Using Online Bees Algorithm for Real-time Permutation Flow Shop Problem in Car Disassembly Line

Şahin¹,

Wang²

2021

Preprint

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The automobile disassembly line is a typical permutation flow shop problem (PFSP). In the automobile disassembly line, the goal of the research is to minimize the completion time of scheduling with optimizing the disassembly order of vehicles. However, disassembly time was uncertain at the initial stage as the consuming time of cars’ specific sequence only could be roughly estimated, which make the PFSP in automobile disassembly line unlike the traditional ones. In this study, the real-time PFSP problem in automobile disassembly line was defined and well solved with the proposed online Bees Algorithm (O-BA). The algorithm has been prepared in Matlab/Simulink to work in real-time. Time consumed by each component of each vehicle was roughly estimated based on engineer’s experience. First optimisation was carried out to decide the disassembly order of vehicles to be disassembled. When each component was disassembled, the real consuming time value was updated with the detecting system which was realized in Simulink. Then the O-BA was activated and created the new solution for the disassembly order of vehicles which were still not entering the disassembly line. The proposed O-BA algorithm has an online structure and conducted the optimisation with distinguishing whether the vehicles to be disassembled were entering the disassembly line. The result shows the O-BA with the detecting system was succeeded in realizing real-time PFSP in the disassembly line. Moreover, the method proposed in this study was suitable for solving the same kind of real-time PFSP in the assembly line or disassembly line.

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“…Swarm-based algorithms are naturally inspired algorithms that mimic the group's animal behavior in searching for food, for example, The Grey Wolf Optimizer proposed by [13], Particle Swarm Optimization algorithm [14], Moth-Flame Optimization [15] and the Artificial Bee Colony algorithm [16]. Swarm-based algorithms have been widely used to solve optimization problems and showed to be superior in many fields of research [17][18][19][20].…”

mentioning

confidence: 99%

Neighborhood search methods with Moth Optimization algorithm as a wrapper method for feature selection problems

Alzaqebah¹,

Alrefai²,

Ahmed³

et al. 2020

IJECE

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Feature selection methods are used to select a subset of features from data, therefore only the useful information can be mined from the samples to get better accuracy and improves the computational efficiency of the learning model. Moth-flam Optimization (MFO) algorithm is a population-based approach, that simulates the behavior of real moth in nature, one drawback of the MFO algorithm is that the solutions move toward the best solution, and it easily can be stuck in local optima as we investigated in this paper, therefore, we proposed a MFO Algorithm combined with a neighborhood search method for feature selection problems, in order to avoid the MFO algorithm getting trapped in a local optima, and helps in avoiding the premature convergence, the neighborhood search method is applied after a predefined number of unimproved iterations (the number of tries fail to improve the current solution). As a result, the proposed algorithm shows good performance when compared with the original MFO algorithm and with state-of-the-art approaches.

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Bees Algorithm for Vehicle Routing Problems with Time Windows

Cited by 30 publications

References 25 publications

An Enhanced ABC algorithm to Solve the Vehicle Routing Problem with Time Windows

An Enhanced ABC algorithm to Solve the Vehicle Routing Problem with Time Windows

Using Online Bees Algorithm for Real-time Permutation Flow Shop Problem in Car Disassembly Line

Neighborhood search methods with Moth Optimization algorithm as a wrapper method for feature selection problems

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