An Improved Genetic Algorithm for Vehicle Routing Problem with Time Window Requirements

This paper introduces a method for optimizing vehicle delivery routes using an enhanced genetic algorithm to improve delivery efficiency and meet time window constraints in vehicle routing problems. Delivery problems often face challenges such as variations in delivery times due to real-world disturbances and the adverse effects of early or late deliveries. To effectively mitigate these challenges, we propose a tolerance mechanism to aid the genetic algorithm in dynamically selecting routes in a timely manner, thereby enhancing its exploration capability in complex delivery environments. Through extensive experimentation across different scenarios, the algorithm performance is comprehensively evaluated and analyzed. Experimental results demonstrate that the improved genetic algorithm exhibits superior path optimization capabilities under time window constraints, thereby significantly enhancing delivery efficiency.