Parallel Adaptive Large Neighborhood Search based on Spark to solve VRPTW 

Abstract

The Adaptive Large Neighborhood Search is an effective method for solving unconstrained optimization problems, but there are drawbacks such as poor accuracy, easy falling into local optimum and slow convergence when solving VRPTW. In order to improve the above problems, this paper improves the cooling function, uses CW initialization to improve the quality of the solution, and adopts three destruction operators and three repair operators, on the basis of which a parallel strategy is proposed to improve the accuracy of the algorithm and reduce the running time. The Solomon dataset is selected for simulation experiments to test both solution quality and running time, and comparison experiments are conducted with other parallel algorithms. The simulation results show that the algorithm can effectively solve VRPTW with a greater improvement in the solving accuracy and a 3–5 times improvement in the solving speed compared with other parallel algorithms.