Dynamic Order Fulfillment in Last-Mile Drone Delivery under Demand Uncertainty

TL;DR

This paper presents a novel stochastic optimization approach for dynamic last-mile drone delivery, improving decision-making under demand uncertainty with a fast heuristic that significantly reduces computation time.

Contribution

It introduces an accelerated L-shaped algorithm for real-time drone routing under demand uncertainty, combining heuristics and strategic cut addition for efficiency.

Findings

Achieves 20-fold reduction in runtime compared to exact methods.

Maintains less than 1% optimality gap in solutions.

Potential cost savings up to 20% when considering demand uncertainty.

Abstract

Drones have attracted growing interest in last-mile delivery due to their potential to significantly reduce costs and enhance operational flexibility, particularly in areas of sparse and uncertain demand where traditional truck delivery proves inefficient. This paper addresses the dynamic order fulfillment problem faced by a retailer operating a fleet of drones to service delivery requests that arrive stochastically. These delivery requests may vary in package profiles, delivery locations, and urgency. We adopt a rolling-horizon framework for order fulfillment and devise a two-stage stochastic program aimed at strategically managing existing orders while considering incoming requests that are subject to various uncertainties. A significant challenge in deploying the envisioned two-stage model lies in its incorporation of vehicle routing constraints, on which exact or brute-force methods are computationally inefficient and unsuitable for real-time operational decisions. To address this, we propose an accelerated L-shaped algorithm that (i) reduces the branching tree size, (ii) replaces exact second-stage solutions with heuristic estimates, and (iii) adapts an alternating strategy for adding optimality cuts. The proposed heuristic demonstrates remarkable performance superiority over the exact method, achieving a 20-fold reduction in average runtime while maintaining an average optimality gap of less than 1\%. We apply the algorithm to a wide range of instances to evaluate the benefits of postponing orders for batch service using the stochastic model. Our results show potential long-term cost savings of up to 20\% when demand uncertainty is explicitly considered in order fulfillment decisions. Meanwhile, the derived savings tend to diminish as the uncertainty increases in order arrivals.