Treffer: Online crowdsourced delivery optimization problem for takeaway orders with balanced rider resources and uncertain travel time.

The takeaway food is mostly ordered online, and delivered. Some riders may receive orders beyond their capacity, causing resource imbalance. Additionally, uncertain travel time makes it difficult for riders to complete deliveries effectively. Thus, an integer programming is formulated for the online crowdsourced delivery problem with balanced rider resources and uncertain travel time (OCD-BRUT) to optimize rider delivery routes. An improved genetic algorithm (IGA) with order sequence optimization operator is developed. Numerical experiments on both simulated and real-world datasets demonstrate that the OCD-BRUT effectively balances rider resources, especially in medium and large instances. For small to medium instances, the average gap between the IGA and the optimal baseline is −2.58%, while the average gap reaches −7.48% in large-scale instances, indicating IGA's efficiency in handling numerous orders in rush hours. Besides, a sensitivity analysis of several key parameters is also performed to derive managerial insights. [ABSTRACT FROM AUTHOR]