Parallel adaptive large neighborhood search based on spark to solve VRPTW

Abstract

Aiming at the multi-objective vehicle path planning problem with time windows (VRPTW), a Spark-based parallel Adaptive Large Neighborhood Search algorithm (Spark-ALNS) is proposed to solve it. The main design of the 4-point strategy: (1) Design a new simulated annealing algorithm cooling strategy to achieve a better jump out of the local optimal solution. (2) Adopt CW initialization to accelerate the convergence speed. (3) Use three destruction operators and three repair operators to implement local path optimization. (4) A new parallel strategy is proposed to improve the algorithm’s accuracy and reduce the running time. To illustrate the algorithm’s effectiveness, the arithmetic example in Solomon is used as an example. The experimental results show that the proposed Spark-ALNS can find better solutions, get the known optimal solutions for 41 out of 56 instances, and find new optimal solutions for 31 algorithms, which outperforms other evolutionary algorithms. The runtime is 3–5 times better than other parallel algorithms and is able to solve VRPTW effectively.