Machine Learning-Based Parameter Tuned Genetic Algorithm for Energy Minimizing Vehicle Routing Problem

Cooray, P. L. N. U.; Rupasinghe, Thashika

doi:10.1155/2017/3019523

Cited by 32 publications

(12 citation statements)

References 8 publications

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“…In addition to mathematical programming methods, machine learning methods have been increasingly used to solve VRPs; for example, Cooray et al [6] enhanced the genetic algorithm with machine learning technology and decreased the calculation time to below that of the general genetic algorithm. Moreover, machine learning can not only be combined with heuristic algorithms but can also be used to construct neural network models to solve VRPs.…”

Section: Related Workmentioning

confidence: 99%

A Pointer Neural Network for the Vehicle Routing Problem with Task Priority and Limited Resources

Sheng

Wei

2020

ITC

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The vehicle routing problem with task priority and limited resources (VRPTPLR) is a generalized version of the vehicle routing problem (VRP) with multiple task priorities and insufficient vehicle capacities. The objective of this problem is to maximize the total benefits. Compared to the traditional mathematical analysis methods, the pointer neural network proposed in this paper continuously learns the mapping relationship between input nodes and output decision schemes based on the actual distribution conditions. In addition, a global attention mechanism is adopted in the neural network to improve the convergence rate and results. To verify the effectiveness of the method, we model the VRPTPLR and compare the results with those of a genetic algorithm. The parameter sensitivity of each algorithm is assessed using different datasets. Then, comparison experiments with the two algorithms employing optimal parameter configurations are performed for the validation sets, which are generated at different instance scales. It is found that the solution time of the pointer neural network is much shorter than that of the genetic algorithm and that the proposed method provides better solutions for large-scale instances.

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Section: Related Workmentioning

confidence: 99%

A Pointer Neural Network for the Vehicle Routing Problem with Task Priority and Limited Resources

Sheng

Wei

2020

ITC

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“…The authors in the articles [3,12,13] used heuristics or metaheuristics algorith ms for the PVRP. The Clark-Wright algorith m is one of the classic heuristics algorith ms that is used to solve the VRP and is based on the notion of savings.…”

Section: Setting the Configuration Parameters Of Pvrp Algorithmmentioning

confidence: 99%

“…The maximu m performance of optimizat ion algorith ms may depend on the setting of tens to hundreds of parameters. There are a lot of methods to solve the setting of the configuration parameters of the VRP algorith m. Authors in [12] used heuristic search techniques for the setting of the configuration parameters for the metaheuristics algorith ms. Another group of parameters tuning of optimizat ion algorith ms is focused on statistical modelling approaches [13]. Authors in [12] proposed machine learning approaches.…”

Section: Setting the Configuration Parameters Of Pvrp Algorithmmentioning

confidence: 99%

Setting the Configuration Parameters of the Algorithm for the Periodic Vehicle Routing Problem by HPC Power

et al. 2019

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The quality of an opt imal solut ion of the Vehicle Rout ing Problem is st rongly depended on the sett ing of the configurat ion parameters of the algorithm. The paper is focused on the int roduct ion of hyperparameter search for solving the Vehicle Rout ing Problem using a HyperLoom plat form for defining and execut ing scient ific pipelines in a dist ributed environment . To give a concrete example, we focused on Periodic Vehicle Rout ing Problem for the waste collect ion. HyperLoom plat form was used to define and execute the hyperparameters sweep pipeline. The heurist ic algorithm was tested on a real benchmark of the waste collect ion in Ostrava, Czech Republic. The aim of our ca se was to effect ively combine the minimizat ion of the total t ravelled distance and the opt imizat ion of the fairness of the routes in terms of the standard deviat ion of a tour length. The waste collect ion problem was very extensive and computat ionally demanding, so it was necessary to use high performance comput ing architecture for test ing a large number of different sett ings of configurat ion parameters. The experiments were run on the supercomputer Salomon operated by IT4Innovations Nat ional Supercomput ing Center in the Czech Republic.

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“…Many researchers are currently focusing on the development of new modeling techniques that make use of Machine Learning algorithms [8][9][10], and, among these, we can identify research directions that fall into three main categories: the first category consists of approaches that use FE method combined with Machine Learning. Stoffel et al [11] take into account the strain-rate and high dynamic deformation in nonlinear structural deformations and propose an intelligent finite element, where an Artificial Neural Network (ANN) is used instead of viscoelastic constitutive equations.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Spring-Mass-Damper Modeling and Parameter Estimation of Train Frontal Crash Using CLGAN Model

Dong

Zhao

Yang

et al. 2020

Shock and Vibration

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Due to the complexity of a train crash, it is a challenging process to describe and estimate mathematically. Although different mathematical models have been developed, it is still difficult to balance the complexity of models and the accuracy of estimation. This paper proposes a nonlinear spring-mass-damper model of train frontal crash, which achieves high accuracy and maintains low complexity. The Convolutional Long-short-term-memory Generation Adversarial Network (CLGAN) model is applied to study the nonlinear parameters dynamic variation of the key components of a rail vehicle (e.g., the head car, anticlimbing energy absorber, and the coupler buffer devices). Firstly, the nonlinear lumped model of train frontal crash is built, and then the physical parameters are deduced in twenty different cases using D’Alembert’s principle. Secondly, the input/output relationship of the CLGAN model is determined, where the inputs are the nonlinear physical parameters in twenty initial conditions, and the output is the nonlinear relationship between the train crash nonlinear parameters under other initial cases. Finally, the train crash dynamic characteristics are accurately estimated during the train crash processes through the training of the CLGAN model, and then the crash processes under different given conditions can be described effectively. The estimation results exhibit good agreement with finite element (FE) simulations and experimental results. Furthermore, the CLGAN model shows great potential in nonlinear estimation, and CLGAN can better describe the variation of nonlinear spring damping compared with the traditional model. The nonlinear spring-mass-damper modeling is involved in improving the speed and accuracy of the train crash estimation, as well as being able to offer guidance for structure optimization in the early design stage.

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Machine Learning-Based Parameter Tuned Genetic Algorithm for Energy Minimizing Vehicle Routing Problem

Cited by 32 publications

References 8 publications

A Pointer Neural Network for the Vehicle Routing Problem with Task Priority and Limited Resources

A Pointer Neural Network for the Vehicle Routing Problem with Task Priority and Limited Resources

Setting the Configuration Parameters of the Algorithm for the Periodic Vehicle Routing Problem by HPC Power

Nonlinear Spring-Mass-Damper Modeling and Parameter Estimation of Train Frontal Crash Using CLGAN Model

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