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

Abstract

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.

Application of Genetic Algorithms to Solve MTSP Problems with Priority (Case Study at the Jakarta Street Lighting Service)

“…The Capacitated Vehicle Routing Problem (CVRP) is also a development model of the basic VRP, which this model is created to solve problems in determining the optimal route in the presence of vehicle capacity limitations [9]. The VRP model development with delivery priority has been developed by [10] based on importance, [11] based on preference, [12] based on limited resources, [13] based on Emergency Response Plans, and [14] based on stochastic demand. This study will discuss the determination of routes in the service sector in an urban environment inspired by the real problems faced by the Street Lighting Section in the City of Jakarta, which is responsible for the maintenance of street lighting in the City of Jakarta.…”

Quantization of Weights of Neural Networks with Negligible Decreasing of Prediction Accuracy

“…In addition, application of NN in image classification has been investigated in [27], where the ship classification problem is considered. The paper [29] applies NN to create the controller within automatic control system, while paper [31] considers a pointer NN for purpose of vehicle routing. In [30], the use of NN has been done in the context of solving four-class motor imagery classification problem.…”