An integrated optimization approach for e-order fulfillment using self-owned and crowdsourced delivery

• We study the e-order fulfillment problem with crowdsourced delivery personnel. • We propose a heuristic that combines ALNS with VNS. • Experiments validate our algorithm’ effectiveness in boosting e-order fulfillment. • Sensitivity analysis examines crowdsourced delivery’s impact on e-order fulfillment. In this paper, we investigate the e-order fulfillment problem with crowdsourced delivery personnel (EOFP-CDP), which aims to enhance order fulfillment performance by integrating crowdsourced delivery with self-owned logistics. We begin by presenting a mixed-integer programming formulation that enables the determination of optimal solutions for small-scale instances, capturing the complexities of the problem. We then propose a heuristic algorithm that combines adaptive large neighborhood search with variable neighborhood search. Through extensive experiments, we demonstrate the effectiveness of this algorithm in improving e-order fulfillment performance. We also conduct a comprehensive sensitivity analysis to examine the impact of crowdsourced delivery on e-order fulfillment. Specifically, we explore the role of key cost factors–such as travel, loading, and time costs–associated with crowdsourced delivery personnel and their influence on fulfillment outcomes. Finally, a case study based on JD.com validates the practical advantages of the consolidation-delivery mode within e-tailer fulfillment systems.