Optimization of instant order delivery routes under non-collaborative distribution strategies

This study addresses the challenge of optimizing delivery routes for instant orders in non-collaborative distribution strategies, a critical issue in modern logistics. With the surge in online shopping, improving delivery efficiency and reducing delays have become essential objectives. To tackle this, the study proposes an advanced algorithmic solution based on the Non-dominated Sorting Ant Colony Optimization (NSACO) algorithm. The methodology leverages real-time trajectory data from delivery personnel and key performance metrics, namely the average courier travel distance and the average total delay time. By adjusting five key parameters—iteration count (T), ant colony size (R), heuristic coefficient ( α ) , pheromone coefficient ( β ) , and pheromone evaporation rate ( ρ ) —the study aims to enhance delivery efficiency. Extensive experiments using real-world data show that the proposed algorithm significantly reduces the average courier travel distance and the average total delay time. The optimized parameter set (T=40, R=80, ( α ) =0.14, ( β ) =0.12, ( ρ ) =0.6) results in a 22.78% reduction in the per-order average delivery distance (a derived indicator for interpretability), from 2.098 km to 1.62 km, and reduces the late-delivery rate from 11.28% to 0%. Meanwhile, the optimized solutions achieve zero average total delay time in this case. These findings highlight the effectiveness of the NSACO algorithm in optimizing delivery routes for instant orders, offering logistics companies a practical solution to improve operational efficiency. • We analyze the courier fulfillment process under centrally optimized non-collaborative delivery and propose an order batching strategy for instant orders based on order creation time and expected delivery time, enabling unified planning within each batch. • We formulate a multi-objective combinatorial optimization model for instant order delivery under non-collaborative execution, with objectives of minimizing the average courier travel distance and the average total delay time, subject to process-consistent constraints. • We propose a Non-dominated Sorting Ant Colony Optimization (NSACO) algorithm with a non-dominated-sorting-based pheromone update mechanism to efficiently solve the above multi-objective dynamic routing-and-assignment problem in a batch-based rolling manner.