Heuristic approach for optimising reliable supply chain network using drones in last-mile delivery under uncertainty

ABSTRACT

In addressing the challenges of last-mile logistics, the reliability of the supply chain network becomes paramount. These challenges are intensified due to drone performance limitations and various uncertainties in supply chain operations. While recent literature recognises the potential of drones for last-mile delivery, it falls short in effectively considering these uncertainties in drone-enabled supply chain models. Our research addresses this gap with two major contributions: first, a novel stochastic mixed-integer programming model is developed to construct a feasible delivery network, including warehouses and recharging stations, enhancing both coverage and reliability. Second, a modification in the genetic algorithm by considering each scenario independently improves computational efficiency, outperforming commercial software by an average of 40% and up to 55%. Empirical findings reveal that strategic investments in system hardening can yield substantial improvements in reliability. Despite the absence of real-world stochastic parameters as a limitation, this research pioneers the design of reliable networks under uncertainties and extends drone coverage through strategic charging stations. This work sets a significant milestone for future optimization in drone logistics, offering practical implications for supply chain managers considering drone adoption.

KEYWORDS:

• Supply chain network design
• unmanned aerial vehicle
• drone delivery
• stochastic programming
• reliability

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.