Selecting meta-heuristics for solving vehicle routing problems with time windows via meta-learning

Gutierrez-Rodríguez, Andres E.; Conant-Pablos, Santiago Enrique; Ortíz-Bayliss, José Carlos; Terashima-Marín, Hugo

doi:10.1016/j.eswa.2018.10.036

Cited by 45 publications

(32 citation statements)

References 36 publications

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“…For the innovation of the modeling process, a matching network based on memory and attention is proposed, which makes it possible to learn quickly. For the innovation of the training process, this work is based on a principle of traditional ML, that is training and testing are to be carried out under the same conditions [35]- [36]. It is proposed that the network should constantly look at the insufficient samples of each type during training, which will be consistent with the testing process.…”

Section: Matching Networkmentioning

confidence: 99%

Learning Models for Semantic Classification of Insufficient Plantar Pressure Images

Wu¹,

Wu²,

Dey³

et al. 2020

IJIMAI

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Establishing a reliable and stable model to predict a target by using insufficient labeled samples is feasible and effective, particularly, for a sensor-generated data-set. This paper has been inspired with insufficient data-set learning algorithms, such as metric-based, prototype networks and meta-learning, and therefore we propose an insufficient data-set transfer model learning method. Firstly, two basic models for transfer learning are introduced. A classification system and calculation criteria are then subsequently introduced. Secondly, a dataset of plantar pressure for comfort shoe design is acquired and preprocessed through foot scan system; and by using a pre-trained convolution neural network employing AlexNet and convolution neural network (CNN)based transfer modeling, the classification accuracy of the plantar pressure images is over 93.5%. Finally, the proposed method has been compared to the current classifiers VGG, ResNet, AlexNet and pre-trained CNN. Also, our work is compared with known-scaling and shifting (SS) and unknown-plain slot (PS) partition methods on the public test databases: SUN, CUB, AWA1, AWA2, and aPY with indices of precision (tr, ts, H) and time (training and evaluation). The proposed method for the plantar pressure classification task shows high performance in most indices when comparing with other methods. The transfer learning-based method can be applied to other insufficient data-sets of sensor imaging fields.

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Section: Matching Networkmentioning

confidence: 99%

Learning Models for Semantic Classification of Insufficient Plantar Pressure Images

Wu¹,

Wu²,

Dey³

et al. 2020

IJIMAI

View full text Add to dashboard Cite

show abstract

“…Nevertheless, the greedy heuristic may yield locally optimal solutions that approximate a globally optimal solution in a reasonable amount of time. This algorithm has been applied to VRPs in various domains, such as cumulative capacitated VRPs, 39 robust stochastic vehicle routing and scheduling for bushfire emergency evacuation, 40 selecting metaheuristics for solving VRPs with time windows via meta-learning, 41 rebalancing bike-sharing systems, 42 and other similar domains. [27][28][29][43][44][45][46] However, it has not been utilized for UAV routing in real-estate sector, which is targeted in the current study.…”

Section: Uav Routing Problems and Key Algorithmsmentioning

confidence: 99%

Advertising through UAVs: Optimized path system for delivering smart real‐estate advertisement materials

Ullah

Al‐Turjman²,

Qayyum

et al. 2021

Int J Intell Syst

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Real-estate advertisements through electronic and print media are bringing considerable fortune to the global real-estate sector. However, innovative advertisement methods must be adopted if real estate aims to transform into smart real estate. The current study, which is based on a systematic literature review of 58 articles published in the last decade, identifies key media for real-estate advertisements as print media (e.g., magazines, brochures, newspapers, and digests), electronic media (e.g., websites, social media, and other digital tools), and mixed methods (e.g., billboards, signs and banners, and personalized messaging). This study takes the case of Kingsford suburb in the eastern Sydney area, and investigates the performance of the Australian real-estate industry in general and lists the key dynamics of properties in Kingsford and its prominent real-estate agencies. An unmanned aerial vehicle (UAV)-based smart real-estate advertisement material delivery system is proposed to deliver advertisement materials and gifts to the potential customers of these agencies. The system paths are optimized through four Java-run algorithms: greedy, interroute, intraroute, and Tabu. Results based on six cases, three each for rent and sales with varying numbers of customers and UAVs and an 8-h operating time, indicate that the Tabu algorithm provides the best-optimized paths in all cases, followed by the interroute, intraroute, and greedy algorithms. However, the inter-and intraroute algorithms show superior performance in terms of computation speed. The proposed framework is a practical approach in disrupting the real-estate advertising sector, thereby helping this sector transform into a smart real estate consistent with industry 4.0 goals.

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“…e metalearning field is also known as "learning to learn" [22] and it brings systems that can help by searching patterns across different tasks to control the process of exploiting cumulative expertise. e metalearning concept has been present in the field of heuristics and metaheuristics for TSP [23], the quadratic assignment problem, and hyperheuristics.…”

Section: Metalearningmentioning

confidence: 99%

“…On the other hand, Gutierrez-Rodríguez et al [23] used VRP with time windows and proposed a methodology based on metalearning to select the best metaheuristic for each instance. Besides, their proposal shared and exploited an offline scheme for the instant solutions of academies and industry.…”

Section: Metalearningmentioning

confidence: 99%

A Methodology to Determine the Subset of Heuristics for Hyperheuristics through Metalearning for Solving Graph Coloring and Capacitated Vehicle Routing Problems

et al. 2021

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In this work, we focus on the problem of selecting low-level heuristics in a hyperheuristic approach with offline learning, for the solution of instances of different problem domains. The objective is to improve the performance of the offline hyperheuristic approach, identifying equivalence classes in a set of instances of different problems and selecting the best performing heuristics in each of them. A methodology is proposed as the first step of a set of instances of all problems, and the generic characteristics of each instance and the performance of the heuristics in each one of them are considered to define the vectors of characteristics and make a grouping of classes. Metalearning with statistical tests is used to select the heuristics for each class. Finally, we used the Naive Bayes to test the set instances with k-fold cross-validation, and we compared all results statistically with the best-known values. In this research, the methodology was tested by applying it to the problems of capacitated vehicle routing (CVRP) and graph coloring (GCP). The experimental results show that the proposed methodology can improve the performance of the offline hyperheuristic approach, correctly identifying the classes of instances and applying the appropriate heuristics in each case. This is based on the statistical comparison of the results obtained with those of the state of the art of each instance.

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Selecting meta-heuristics for solving vehicle routing problems with time windows via meta-learning

Cited by 45 publications

References 36 publications

Learning Models for Semantic Classification of Insufficient Plantar Pressure Images

Learning Models for Semantic Classification of Insufficient Plantar Pressure Images

Advertising through UAVs: Optimized path system for delivering smart real‐estate advertisement materials

A Methodology to Determine the Subset of Heuristics for Hyperheuristics through Metalearning for Solving Graph Coloring and Capacitated Vehicle Routing Problems

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